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Sample records for charaterizing biochemical protein-protein

  1. Biochemical and Physiological Characterization: Development & Apply Optical Methods for Charaterizing Biochemical Protein-Protein Interactions in MR-1

    Energy Technology Data Exchange (ETDEWEB)

    Weiss, Shimon

    2006-08-30

    The objectives of this report are to: Develop novel site-specific protein labeling chemistries for assaying protein-protein interactions in MR-1; and development of a novel optical acquisition and data analysis method for characterizing protein-protein interactions in MR-1 model systems. Our work on analyzing protein-protein interactions in MR-1 is divided in four areas: (1) expression and labeling of MR-1 proteins; (2) general scheme for site-specific fluorescent labeling of expressed proteins; (3) methodology development for monitoring protein-protein interactions; and (4) study of protein-protein interactions in MR-1. In this final report, we give an account for our advances in all areas.

  2. Protein-protein interactions

    DEFF Research Database (Denmark)

    Byron, Olwyn; Vestergaard, Bente

    2015-01-01

    Responsive formation of protein:protein interaction (PPI) upon diverse stimuli is a fundament of cellular function. As a consequence, PPIs are complex, adaptive entities, and exist in structurally heterogeneous interplays defined by the energetic states of the free and complexed protomers....... The biophysical and structural investigations of PPIs consequently demand hybrid approaches, implementing orthogonal methods and strategies for global data analysis. Currently, impressive developments in hardware and software within several methodologies define a new era for the biostructural community. Data can...

  3. Synthesis and Charaterization of Silica-Based Aldehyde Chitosan Hybrid Material for Biodiesel Purification.

    Science.gov (United States)

    da Silva, Sandra Rodrigues; de Albuquerque, Nilson J A; de Almeida, Rusiene M; de Abreu, Fabiane C

    2017-09-25

    This study concerns the development and charaterization of Silica-based aldehyde Chitosan hybrid material as an adsorbent for biodiesel purification. This biocomposite was prepared by sol-gel route and oxidation with periodate, and then characterized. FTIR experiments showed that the hybrid formed presents absorption bands similar to those of Chitosan-Silica, with the exception of the vibrations at 1480 cm(-1) and 1570 cm(-1) attributed to the symmetrical angular deformation in the N-H plane, and possess large N₂ Brunauer-Emmett-Teller (BET) surface areas. Thermogravimetric analysis (TG) and scanning electron microscopy (SEM) was also carried out. Adsorption studies of bioadsorbents involving the analysis of free glycerol, soap, acidity, diglycerides, triglycerides, and fluorescence spectroscopy showed that silica-based aldehyde chitosan has a good affinity for glycerol and a good purification process.

  4. Synthesis and Charaterization of Silica-Based Aldehyde Chitosan Hybrid Material for Biodiesel Purification

    Directory of Open Access Journals (Sweden)

    Sandra Rodrigues da Silva

    2017-09-01

    Full Text Available This study concerns the development and charaterization of Silica-based aldehyde Chitosan hybrid material as an adsorbent for biodiesel purification. This biocomposite was prepared by sol-gel route and oxidation with periodate, and then characterized. FTIR experiments showed that the hybrid formed presents absorption bands similar to those of Chitosan-Silica, with the exception of the vibrations at 1480 cm−1 and 1570 cm−1 attributed to the symmetrical angular deformation in the N-H plane, and possess large N2 Brunauer–Emmett–Teller (BET surface areas. Thermogravimetric analysis (TG and scanning electron microscopy (SEM was also carried out. Adsorption studies of bioadsorbents involving the analysis of free glycerol, soap, acidity, diglycerides, triglycerides, and fluorescence spectroscopy showed that silica-based aldehyde chitosan has a good affinity for glycerol and a good purification process.

  5. Protein-Protein Interaction Databases

    DEFF Research Database (Denmark)

    Szklarczyk, Damian; Jensen, Lars Juhl

    2015-01-01

    of research are explored. Here we present an overview of the most widely used protein-protein interaction databases and the methods they employ to gather, combine, and predict interactions. We also point out the trade-off between comprehensiveness and accuracy and the main pitfall scientists have to be aware......Years of meticulous curation of scientific literature and increasingly reliable computational predictions have resulted in creation of vast databases of protein interaction data. Over the years, these repositories have become a basic framework in which experiments are analyzed and new directions...

  6. Scaffolds for blocking protein-protein interactions.

    Science.gov (United States)

    Hershberger, Stefan J; Lee, Song-Gil; Chmielewski, Jean

    2007-01-01

    Due to the pivotal roles that protein-protein interactions play in a plethora of biological processes, the design of therapeutic agents targeting these interactions has become an attractive and important area of research. The development of such agents is faced with a variety of challenges. Nevertheless, considerable progress has been made in the design of proteomimetics capable of disrupting protein-protein interactions. Those inhibitors based on molecular scaffold designs hold considerable interest because of the ease of variation in regard to their displayed functionality. In particular, protein surface mimetics, alpha-helical mimetics, beta-sheet/beta-strand mimetics, as well as beta-turn mimetics have successfully modulated protein-protein interactions involved in such diseases as cancer and HIV. In this review, current progress in the development of molecular scaffolds designed for the disruption of protein-protein interactions will be discussed with an emphasis on those active against biological targets.

  7. Direct protein-protein conjugation by genetically introducing bioorthogonal functional groups into proteins.

    Science.gov (United States)

    Kim, Sanggil; Ko, Wooseok; Sung, Bong Hyun; Kim, Sun Chang; Lee, Hyun Soo

    2016-11-15

    Proteins often function as complex structures in conjunction with other proteins. Because these complex structures are essential for sophisticated functions, developing protein-protein conjugates has gained research interest. In this study, site-specific protein-protein conjugation was performed by genetically incorporating an azide-containing amino acid into one protein and a bicyclononyne (BCN)-containing amino acid into the other. Three to four sites in each of the proteins were tested for conjugation efficiency, and three combinations showed excellent conjugation efficiency. The genetic incorporation of unnatural amino acids (UAAs) is technically simple and produces the mutant protein in high yield. In addition, the conjugation reaction can be conducted by simple mixing, and does not require additional reagents or linker molecules. Therefore, this method may prove very useful for generating protein-protein conjugates and protein complexes of biochemical significance. Copyright © 2016. Published by Elsevier Ltd.

  8. Structural analysis of protein-protein interactions in type I polyketide synthases.

    Science.gov (United States)

    Xu, Wei; Qiao, Kangjian; Tang, Yi

    2013-01-01

    Polyketide synthases (PKSs) are responsible for synthesizing a myriad of natural products with agricultural, medicinal relevance. The PKSs consist of multiple functional domains of which each can catalyze a specified chemical reaction leading to the synthesis of polyketides. Biochemical studies showed that protein-substrate and protein-protein interactions play crucial roles in these complex regio-/stereo-selective biochemical processes. Recent developments on X-ray crystallography and protein NMR techniques have allowed us to understand the biosynthetic mechanism of these enzymes from their structures. These structural studies have facilitated the elucidation of the sequence-function relationship of PKSs and will ultimately contribute to the prediction of product structure. This review will focus on the current knowledge of type I PKS structures and the protein-protein interactions in this system.

  9. Protein-protein interactions as drug targets.

    Science.gov (United States)

    Skwarczynska, Malgorzata; Ottmann, Christian

    2015-01-01

    Modulation of protein-protein interactions (PPIs) is becoming increasingly important in drug discovery and chemical biology. While a few years ago this 'target class' was deemed to be largely undruggable an impressing number of publications and success stories now show that targeting PPIs with small, drug-like molecules indeed is a feasible approach. Here, we summarize the current state of small-molecule inhibition and stabilization of PPIs and review the active molecules from a structural and medicinal chemistry angle, especially focusing on the key examples of iNOS, LFA-1 and 14-3-3.

  10. Conductometric monitoring of protein-protein interactions.

    Science.gov (United States)

    Spera, Rosanna; Festa, Fernanda; Bragazzi, Nicola L; Pechkova, Eugenia; LaBaer, Joshua; Nicolini, Claudio

    2013-12-06

    Conductometric monitoring of protein-protein and protein-sterol interactions is here proved feasible by coupling quartz crystal microbalance with dissipation monitoring (QCM_D) to nucleic acid programmable protein arrays (NAPPA). The conductance curves measured in NAPPA microarrays printed on quartz surface allowed the identification of binding events between the immobilized proteins and the query. NAPPA allows the immobilization on the quartz surface of a wide range of proteins and can be easily adapted to generate innumerous types of biosensors. Indeed multiple proteins on the same quartz crystal have been tested and envisaged proving the possibility of analyzing the same array for several distinct interactions. Two examples of NAPPA-based conductometer applications with clinical relevance are presented herein, the interaction between the transcription factors Jun and ATF2 and the interaction between Cytochrome P540scc and cholesterol.

  11. New Compound Classes: Protein-Protein Interactions.

    Science.gov (United States)

    Ottmann, C

    2016-01-01

    "Protein-protein interactions (PPIs) are one of the most promising new targets in drug discovery. With estimates between 300,000 and 650,000 in human physiology, targeted modulation of PPIs would tremendously extend the "druggable" genome. In fact, in every disease a wealth of potentially addressable PPIs can be found making pharmacological intervention based on PPI modulators in principle a generally applicable technology. An impressing number of success stories in small-molecule PPI inhibition and natural-product PPI stabilization increasingly encourage academia and industry to invest in PPI modulation. In this chapter examples of both inhibition as well as stabilization of PPIs are reviewed including some of the technologies which has been used for their identification."

  12. Detecting mutually exclusive interactions in protein-protein interaction maps.

    Directory of Open Access Journals (Sweden)

    Carmen Sánchez Claros

    Full Text Available Comprehensive protein interaction maps can complement genetic and biochemical experiments and allow the formulation of new hypotheses to be tested in the system of interest. The computational analysis of the maps may help to focus on interesting cases and thereby to appropriately prioritize the validation experiments. We show here that, by automatically comparing and analyzing structurally similar regions of proteins of known structure interacting with a common partner, it is possible to identify mutually exclusive interactions present in the maps with a sensitivity of 70% and a specificity higher than 85% and that, in about three fourth of the correctly identified complexes, we also correctly recognize at least one residue (five on average belonging to the interaction interface. Given the present and continuously increasing number of proteins of known structure, the requirement of the knowledge of the structure of the interacting proteins does not substantially impact on the coverage of our strategy that can be estimated to be around 25%. We also introduce here the Estrella server that embodies this strategy, is designed for users interested in validating specific hypotheses about the functional role of a protein-protein interaction and it also allows access to pre-computed data for seven organisms.

  13. Detecting mutually exclusive interactions in protein-protein interaction maps.

    KAUST Repository

    Sánchez Claros, Carmen

    2012-06-08

    Comprehensive protein interaction maps can complement genetic and biochemical experiments and allow the formulation of new hypotheses to be tested in the system of interest. The computational analysis of the maps may help to focus on interesting cases and thereby to appropriately prioritize the validation experiments. We show here that, by automatically comparing and analyzing structurally similar regions of proteins of known structure interacting with a common partner, it is possible to identify mutually exclusive interactions present in the maps with a sensitivity of 70% and a specificity higher than 85% and that, in about three fourth of the correctly identified complexes, we also correctly recognize at least one residue (five on average) belonging to the interaction interface. Given the present and continuously increasing number of proteins of known structure, the requirement of the knowledge of the structure of the interacting proteins does not substantially impact on the coverage of our strategy that can be estimated to be around 25%. We also introduce here the Estrella server that embodies this strategy, is designed for users interested in validating specific hypotheses about the functional role of a protein-protein interaction and it also allows access to pre-computed data for seven organisms.

  14. Anchored design of protein-protein interfaces.

    Directory of Open Access Journals (Sweden)

    Steven M Lewis

    Full Text Available BACKGROUND: Few existing protein-protein interface design methods allow for extensive backbone rearrangements during the design process. There is also a dichotomy between redesign methods, which take advantage of the native interface, and de novo methods, which produce novel binders. METHODOLOGY: Here, we propose a new method for designing novel protein reagents that combines advantages of redesign and de novo methods and allows for extensive backbone motion. This method requires a bound structure of a target and one of its natural binding partners. A key interaction in this interface, the anchor, is computationally grafted out of the partner and into a surface loop on the design scaffold. The design scaffold's surface is then redesigned with backbone flexibility to create a new binding partner for the target. Careful choice of a scaffold will bring experimentally desirable characteristics into the new complex. The use of an anchor both expedites the design process and ensures that binding proceeds against a known location on the target. The use of surface loops on the scaffold allows for flexible-backbone redesign to properly search conformational space. CONCLUSIONS AND SIGNIFICANCE: This protocol was implemented within the Rosetta3 software suite. To demonstrate and evaluate this protocol, we have developed a benchmarking set of structures from the PDB with loop-mediated interfaces. This protocol can recover the correct loop-mediated interface in 15 out of 16 tested structures, using only a single residue as an anchor.

  15. Protopia: a protein-protein interaction tool

    Science.gov (United States)

    Real-Chicharro, Alejandro; Ruiz-Mostazo, Iván; Navas-Delgado, Ismael; Kerzazi, Amine; Chniber, Othmane; Sánchez-Jiménez, Francisca; Medina, Miguel Ángel; Aldana-Montes, José F

    2009-01-01

    Background Protein-protein interactions can be considered the basic skeleton for living organism self-organization and homeostasis. Impressive quantities of experimental data are being obtained and computational tools are essential to integrate and to organize this information. This paper presents Protopia, a biological tool that offers a way of searching for proteins and their interactions in different Protein Interaction Web Databases, as a part of a multidisciplinary initiative of our institution for the integration of biological data . Results The tool accesses the different Databases (at present, the free version of Transfac, DIP, Hprd, Int-Act and iHop), and results are expressed with biological protein names or databases codes and can be depicted as a vector or a matrix. They can be represented and handled interactively as an organic graph. Comparison among databases is carried out using the Uniprot codes annotated for each protein. Conclusion The tool locates and integrates the current information stored in the aforementioned databases, and redundancies among them are detected. Results are compatible with the most important network analysers, so that they can be compared and analysed by other world-wide known tools and platforms. The visualization possibilities help to attain this goal and they are especially interesting for handling multiple-step or complex networks. PMID:19828077

  16. Grafting of protein-protein binding sites

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A strategy for grafting protein-protein binding sites is described. Firstly, key interaction residues at the interface of ligand protein to be grafted are identified and suitable positions in scaffold protein for grafting these key residues are sought. Secondly, the scaffold proteins are superposed onto the ligand protein based on the corresponding Ca and Cb atoms. The complementarity between the scaffold protein and the receptor protein is evaluated and only matches with high score are accepted. The relative position between scaffold and receptor proteins is adjusted so that the interface has a reasonable packing density. Then the scaffold protein is mutated to corresponding residues in ligand protein at each candidate position. And the residues having bad steric contacts with the receptor proteins, or buried charged residues not involved in the formation of any salt bridge are mutated. Finally, the mutated scaffold protein in complex with receptor protein is co-minimized by Charmm. In addition, we deduce a scoring function to evaluate the affinity between mutated scaffold protein and receptor protein by statistical analysis of rigid binding data sets.

  17. Interface-resolved network of protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Margaret E Johnson

    Full Text Available We define an interface-interaction network (IIN to capture the specificity and competition between protein-protein interactions (PPI. This new type of network represents interactions between individual interfaces used in functional protein binding and thereby contains the detail necessary to describe the competition and cooperation between any pair of binding partners. Here we establish a general framework for the construction of IINs that merges computational structure-based interface assignment with careful curation of available literature. To complement limited structural data, the inclusion of biochemical data is critical for achieving the accuracy and completeness necessary to analyze the specificity and competition between the protein interactions. Firstly, this procedure provides a means to clarify the information content of existing data on purported protein interactions and to remove indirect and spurious interactions. Secondly, the IIN we have constructed here for proteins involved in clathrin-mediated endocytosis (CME exhibits distinctive topological properties. In contrast to PPI networks with their global and relatively dense connectivity, the fragmentation of the IIN into distinctive network modules suggests that different functional pressures act on the evolution of its topology. Large modules in the IIN are formed by interfaces sharing specificity for certain domain types, such as SH3 domains distributed across different proteins. The shared and distinct specificity of an interface is necessary for effective negative and positive design of highly selective binding targets. Lastly, the organization of detailed structural data in a network format allows one to identify pathways of specific binding interactions and thereby predict effects of mutations at specific surfaces on a protein and of specific binding inhibitors, as we explore in several examples. Overall, the endocytosis IIN is remarkably complex and rich in features masked

  18. Stabilization and inhibition of protein-protein interactions: the 14-3-3 case study.

    Science.gov (United States)

    Milroy, Lech-Gustav; Brunsveld, Luc; Ottmann, Christian

    2013-01-18

    Small-molecule modulation of protein-protein interactions (PPIs) is one of the most exciting but also difficult fields in chemical biology and drug development. As one of the most important "hub" proteins with at least 200-300 interaction partners, the 14-3-3 proteins are an especially fruitful case for PPI intervention. Here, we summarize recent success stories in small-molecule modulation, both inhibition and stabilization, of 14-3-3 PPIs. The chemical breath of modulators includes natural products such as fusicoccin A and derivatives but also compounds identified via high-throughput and in silico screening, which has yielded a toolbox of useful inhibitors and stabilizers for this interesting class of adapter proteins. Protein-protein interactions (PPIs) are involved in almost all biological processes, with any given protein typically engaged in complexes with other proteins for the majority of its lifetime. Hence, proteins function not simply as single, isolated entities but display their roles by interacting with other cellular components. These different interaction patterns are presumably as important as the intrinsic biochemical activity status of the protein itself. The biological role of a protein is therefore decisively dependent on the underlying PPI network that furthermore can show great spatial and temporal variations. A thorough appreciation and understanding of this concept and its regulation mechanisms could help to develop new therapeutic agents and concepts.

  19. Surface energetics and protein-protein interactions: analysis and mechanistic implications

    Science.gov (United States)

    Peri, Claudio; Morra, Giulia; Colombo, Giorgio

    2016-04-01

    Understanding protein-protein interactions (PPI) at the molecular level is a fundamental task in the design of new drugs, the prediction of protein function and the clarification of the mechanisms of (dis)regulation of biochemical pathways. In this study, we use a novel computational approach to investigate the energetics of aminoacid networks located on the surface of proteins, isolated and in complex with their respective partners. Interestingly, the analysis of individual proteins identifies patches of surface residues that, when mapped on the structure of their respective complexes, reveal regions of residue-pair couplings that extend across the binding interfaces, forming continuous motifs. An enhanced effect is visible across the proteins of the dataset forming larger quaternary assemblies. The method indicates the presence of energetic signatures in the isolated proteins that are retained in the bound form, which we hypothesize to determine binding orientation upon complex formation. We propose our method, BLUEPRINT, as a complement to different approaches ranging from the ab-initio characterization of PPIs, to protein-protein docking algorithms, for the physico-chemical and functional investigation of protein-protein interactions.

  20. New approach for predicting protein-protein interactions

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    @@ Protein-protein interactions (PPIs) are of vital importance for virtually all processes of a living cell. The study of these associations of protein molecules could improve people's understanding of diseases and provide basis for therapeutic approaches.

  1. Information assessment on predicting protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Gerstein Mark

    2004-10-01

    Full Text Available Abstract Background Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. Results Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. Conclusions In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the

  2. Laplacian Spectrum and Protein-Protein Interaction Networks

    CERN Document Server

    Banerjee, Anirban

    2007-01-01

    From the spectral plot of the (normalized) graph Laplacian, the essential qualitative properties of a network can be simultaneously deduced. Given a class of empirical networks, reconstruction schemes for elucidating the evolutionary dynamics leading to those particular data can then be developed. This method is exemplified for protein-protein interaction networks. Traces of their evolutionary history of duplication and divergence processes are identified. In particular, we can identify typical specific features that robustly distinguish protein-protein interaction networks from other classes of networks, in spite of possible statistical fluctuations of the underlying data.

  3. Understanding Protein-Protein Interactions Using Local Structural Features

    DEFF Research Database (Denmark)

    Planas-Iglesias, Joan; Bonet, Jaume; García-García, Javier;

    2013-01-01

    Protein-protein interactions (PPIs) play a relevant role among the different functions of a cell. Identifying the PPI network of a given organism (interactome) is useful to shed light on the key molecular mechanisms within a biological system. In this work, we show the role of structural features...... (loops and domains) to comprehend the molecular mechanisms of PPIs. A paradox in protein-protein binding is to explain how the unbound proteins of a binary complex recognize each other among a large population within a cell and how they find their best docking interface in a short timescale. We use...

  4. Small-molecule stabilization of the p53 - 14-3-3 protein-protein interaction.

    Science.gov (United States)

    Doveston, Richard G; Kuusk, Ave; Andrei, Sebastian A; Leysen, Seppe; Cao, Qing; Castaldi, Maria P; Hendricks, Adam; Brunsveld, Luc; Chen, Hongming; Boyd, Helen; Ottmann, Christian

    2017-08-01

    14-3-3 proteins are positive regulators of the tumor suppressor p53, the mutation of which is implicated in many human cancers. Current strategies for targeting of p53 involve restoration of wild-type function or inhibition of the interaction with MDM2, its key negative regulator. Despite the efficacy of these strategies, the alternate approach of stabilizing the interaction of p53 with positive regulators and, thus, enhancing tumor suppressor activity, has not been explored. Here, we report the first example of small-molecule stabilization of the 14-3-3 - p53 protein-protein interaction (PPI) and demonstrate the potential of this approach as a therapeutic modality. We also observed a disconnect between biophysical and crystallographic data in the presence of a stabilizing molecule, which is unusual in 14-3-3 PPIs. © 2017 Federation of European Biochemical Societies.

  5. Coarse-grain modelling of protein-protein interactions

    NARCIS (Netherlands)

    Baaden, Marc; Marrink, Siewert J.

    2013-01-01

    Here, we review recent advances towards the modelling of protein-protein interactions (PPI) at the coarse-grained (CG) level, a technique that is now widely used to understand protein affinity, aggregation and self-assembly behaviour. PPI models of soluble proteins and membrane proteins are separate

  6. Protein-Protein Interaction Reagents | Office of Cancer Genomics

    Science.gov (United States)

    The CTD2 Center at Emory University has a library of genes used to study protein-protein interactions in mammalian cells. These genes are cloned in different mammalian expression vectors. A list of available cancer-associated genes can be accessed below. Emory_CTD^2_PPI_Reagents.xlsx Contact: Haian Fu

  7. Protein-Protein Interactions (PPI) reagents: | Office of Cancer Genomics

    Science.gov (United States)

    The CTD2 Center at Emory University has a library of genes used to study protein-protein interactions in mammalian cells. These genes are cloned in different mammalian expression vectors. A list of available cancer-associated genes can be accessed below.

  8. Protein-Protein Interactions (PPI) reagents: | Office of Cancer Genomics

    Science.gov (United States)

    The CTD2 Center at Emory University has a library of genes used to study protein-protein interactions in mammalian cells. These genes are cloned in different mammalian expression vectors. A list of available cancer-associated genes can be accessed below.

  9. Predicting where small molecules bind at protein-protein interfaces.

    Directory of Open Access Journals (Sweden)

    Peter Walter

    Full Text Available Small molecules that bind at protein-protein interfaces may either block or stabilize protein-protein interactions in cells. Thus, some of these binding interfaces may turn into prospective targets for drug design. Here, we collected 175 pairs of protein-protein (PP complexes and protein-ligand (PL complexes with known three-dimensional structures for which (1 one protein from the PP complex shares at least 40% sequence identity with the protein from the PL complex, and (2 the interface regions of these proteins overlap at least partially with each other. We found that those residues of the interfaces that may bind the other protein as well as the small molecule are evolutionary more conserved on average, have a higher tendency of being located in pockets and expose a smaller fraction of their surface area to the solvent than the remaining protein-protein interface region. Based on these findings we derived a statistical classifier that predicts patches at binding interfaces that have a higher tendency to bind small molecules. We applied this new prediction method to more than 10,000 interfaces from the protein data bank. For several complexes related to apoptosis the predicted binding patches were in direct contact to co-crystallized small molecules.

  10. Mapping Protein-Protein Interactions by Quantitative Proteomics

    DEFF Research Database (Denmark)

    Dengjel, Joern; Kratchmarova, Irina; Blagoev, Blagoy

    2010-01-01

    spectrometry (MS)-based proteomics in combination with affinity purification protocols has become the method of choice to map and track the dynamic changes in protein-protein interactions, including the ones occurring during cellular signaling events. Different quantitative MS strategies have been used...

  11. Biochemical Engineering.

    Science.gov (United States)

    Dunnill, P.

    1979-01-01

    Biochemical engineering as a scientific discipline is becoming accepted in England and is drawing many young men and women to its ranks. This article focuses on how engineering came to embrace the biological sciences. (Author/SA)

  12. Information-driven structural modelling of protein-protein interactions.

    Science.gov (United States)

    Rodrigues, João P G L M; Karaca, Ezgi; Bonvin, Alexandre M J J

    2015-01-01

    Protein-protein docking aims at predicting the three-dimensional structure of a protein complex starting from the free forms of the individual partners. As assessed in the CAPRI community-wide experiment, the most successful docking algorithms combine pure laws of physics with information derived from various experimental or bioinformatics sources. Of these so-called "information-driven" approaches, HADDOCK stands out as one of the most successful representatives. In this chapter, we briefly summarize which experimental information can be used to drive the docking prediction in HADDOCK, and then focus on the docking protocol itself. We discuss and illustrate with a tutorial example a "classical" protein-protein docking prediction, as well as more recent developments for modelling multi-body systems and large conformational changes.

  13. Laplacian Spectrum and Protein-Protein Interaction Networks

    OpenAIRE

    Banerjee, Anirban; Jost, Jürgen

    2007-01-01

    From the spectral plot of the (normalized) graph Laplacian, the essential qualitative properties of a network can be simultaneously deduced. Given a class of empirical networks, reconstruction schemes for elucidating the evolutionary dynamics leading to those particular data can then be developed. This method is exemplified for protein-protein interaction networks. Traces of their evolutionary history of duplication and divergence processes are identified. In particular, we can identify typic...

  14. Protein docking prediction using predicted protein-protein interface

    Directory of Open Access Journals (Sweden)

    Li Bin

    2012-01-01

    Full Text Available Abstract Background Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. Results We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm, is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. Conclusion We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

  15. Protein-protein interaction assays: eliminating false positive interactions

    OpenAIRE

    Nguyen, Tuan N.; Goodrich, James A.

    2006-01-01

    Many methods commonly used to identify and characterize interactions between two or more proteins are variations of the immobilized protein-protein interaction assay (for example, glutathione S-transferase (GST) pulldown and coimmunoprecipitation). A potential, and often overlooked, problem with these assays is the possibility that an observed interaction is mediated not by direct contact between proteins, but instead by nucleic acid contaminating the protein preparations. As a negatively cha...

  16. Understanding and Manipulating Electrostatic Fields at the Protein-Protein Interface Using Vibrational Spectroscopy and Continuum Electrostatics Calculations.

    Science.gov (United States)

    Ritchie, Andrew W; Webb, Lauren J

    2015-11-05

    Biological function emerges in large part from the interactions of biomacromolecules in the complex and dynamic environment of the living cell. For this reason, macromolecular interactions in biological systems are now a major focus of interest throughout the biochemical and biophysical communities. The affinity and specificity of macromolecular interactions are the result of both structural and electrostatic factors. Significant advances have been made in characterizing structural features of stable protein-protein interfaces through the techniques of modern structural biology, but much less is understood about how electrostatic factors promote and stabilize specific functional macromolecular interactions over all possible choices presented to a given molecule in a crowded environment. In this Feature Article, we describe how vibrational Stark effect (VSE) spectroscopy is being applied to measure electrostatic fields at protein-protein interfaces, focusing on measurements of guanosine triphosphate (GTP)-binding proteins of the Ras superfamily binding with structurally related but functionally distinct downstream effector proteins. In VSE spectroscopy, spectral shifts of a probe oscillator's energy are related directly to that probe's local electrostatic environment. By performing this experiment repeatedly throughout a protein-protein interface, an experimental map of measured electrostatic fields generated at that interface is determined. These data can be used to rationalize selective binding of similarly structured proteins in both in vitro and in vivo environments. Furthermore, these data can be used to compare to computational predictions of electrostatic fields to explore the level of simulation detail that is necessary to accurately predict our experimental findings.

  17. An information-theoretic classification of amino acids for the assessment of interfaces in protein-protein docking.

    Science.gov (United States)

    Jardin, Christophe; Stefani, Arno G; Eberhardt, Martin; Huber, Johannes B; Sticht, Heinrich

    2013-09-01

    Docking represents a versatile and powerful method to predict the geometry of protein-protein complexes. However, despite significant methodical advances, the identification of good docking solutions among a large number of false solutions still remains a difficult task. We have previously demonstrated that the formalism of mutual information (MI) from information theory can be adapted to protein docking, and we have now extended this approach to enhance its robustness and applicability. A large dataset consisting of 22,934 docking decoys derived from 203 different protein-protein complexes was used for an MI-based optimization of reduced amino acid alphabets representing the protein-protein interfaces. This optimization relied on a clustering analysis that allows one to estimate the mutual information of whole amino acid alphabets by considering all structural features simultaneously, rather than by treating them individually. This clustering approach is fast and can be applied in a similar fashion to the generation of reduced alphabets for other biological problems like fold recognition, sequence data mining, or secondary structure prediction. The reduced alphabets derived from the present work were converted into a scoring function for the evaluation of docking solutions, which is available for public use via the web service score-MI: http://score-MI.biochem.uni-erlangen.de.

  18. Noninvasive imaging of protein-protein interactions in living animals

    Science.gov (United States)

    Luker, Gary D.; Sharma, Vijay; Pica, Christina M.; Dahlheimer, Julie L.; Li, Wei; Ochesky, Joseph; Ryan, Christine E.; Piwnica-Worms, Helen; Piwnica-Worms, David

    2002-05-01

    Protein-protein interactions control transcription, cell division, and cell proliferation as well as mediate signal transduction, oncogenic transformation, and regulation of cell death. Although a variety of methods have been used to investigate protein interactions in vitro and in cultured cells, none can analyze these interactions in intact, living animals. To enable noninvasive molecular imaging of protein-protein interactions in vivo by positron-emission tomography and fluorescence imaging, we engineered a fusion reporter gene comprising a mutant herpes simplex virus 1 thymidine kinase and green fluorescent protein for readout of a tetracycline-inducible, two-hybrid system in vivo. By using micro-positron-emission tomography, interactions between p53 tumor suppressor and the large T antigen of simian virus 40 were visualized in tumor xenografts of HeLa cells stably transfected with the imaging constructs. Imaging protein-binding partners in vivo will enable functional proteomics in whole animals and provide a tool for screening compounds targeted to specific protein-protein interactions in living animals.

  19. Characterization of protein-protein interactions by isothermal titration calorimetry.

    Science.gov (United States)

    Velazquez-Campoy, Adrian; Leavitt, Stephanie A; Freire, Ernesto

    2015-01-01

    The analysis of protein-protein interactions has attracted the attention of many researchers from both a fundamental point of view and a practical point of view. From a fundamental point of view, the development of an understanding of the signaling events triggered by the interaction of two or more proteins provides key information to elucidate the functioning of many cell processes. From a practical point of view, understanding protein-protein interactions at a quantitative level provides the foundation for the development of antagonists or agonists of those interactions. Isothermal Titration Calorimetry (ITC) is the only technique with the capability of measuring not only binding affinity but the enthalpic and entropic components that define affinity. Over the years, isothermal titration calorimeters have evolved in sensitivity and accuracy. Today, TA Instruments and MicroCal market instruments with the performance required to evaluate protein-protein interactions. In this methods paper, we describe general procedures to analyze heterodimeric (porcine pancreatic trypsin binding to soybean trypsin inhibitor) and homodimeric (bovine pancreatic α-chymotrypsin) protein associations by ITC.

  20. On the analysis of protein-protein interactions via knowledge-based potentials for the prediction of protein-protein docking

    DEFF Research Database (Denmark)

    Feliu, Elisenda; Aloy, Patrick; Oliva, Baldo

    2011-01-01

    Development of effective methods to screen binary interactions obtained by rigid-body protein-protein docking is key for structure prediction of complexes and for elucidating physicochemical principles of protein-protein binding. We have derived empirical knowledge-based potential functions...... results were compared with a residue-pair potential scoring function (RPScore) and an atomic-detailed scoring function (Zrank). We have combined knowledge-based potentials to score protein-protein poses of decoys of complexes classified either as transient or as permanent protein-protein interactions...

  1. Exposing the Alkanesulfonate Monooxygenase Protein-Protein Interaction Sites.

    Science.gov (United States)

    Dayal, Paritosh V; Singh, Harsimran; Busenlehner, Laura S; Ellis, Holly R

    2015-12-29

    The alkanesulfonate monooxygenase enzymes (SsuE and SsuD) catalyze the desulfonation of diverse alkanesulfonate substrates. The SsuE enzyme is an NADPH-dependent FMN reductase that provides reduced flavin to the SsuD monooxygenase enzyme. Previous studies have highlighted the presence of protein-protein interactions between SsuE and SsuD thought to be important in the flavin transfer event, but the putative interaction sites have not been identified. Protected sites on specific regions of SsuE and SsuD were identified by hydrogen-deuterium exchange mass spectrometry. An α-helix on SsuD containing conserved charged amino acids showed a decrease in percent deuteration in the presence of SsuE. The α-helical region of SsuD is part of an insertion sequence and is adjacent to the active site opening. A SsuD variant containing substitutions of the charged residues showed a 4-fold decrease in coupled assays that included SsuE to provide reduced FMN, but there was no activity observed with an SsuD variant containing a deletion of the α-helix under similar conditions. Desulfonation by the SsuD deletion variant was only observed with an increase in enzyme and substrate concentrations. Although activity was observed under certain conditions, there were no protein-protein interactions observed with the SsuD variants and SsuE in pull-down assays and fluorimetric titrations. The results from these studies suggest that optimal transfer of reduced flavin from SsuE to SsuD requires defined protein-protein interactions, but diffusion can occur under specified conditions. A basis is established for further studies to evaluate the structural features of the alkanesulfonate monooxygenase enzymes that promote desulfonation.

  2. Geometric de-noising of protein-protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Oleksii Kuchaiev

    2009-08-01

    Full Text Available Understanding complex networks of protein-protein interactions (PPIs is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H, tandem affinity purification (TAP and other high-throughput methods for protein-protein interaction (PPI detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.

  3. Sentence Simplification Aids Protein-Protein Interaction Extraction

    CERN Document Server

    Jonnalagadda, Siddhartha

    2010-01-01

    Accurate systems for extracting Protein-Protein Interactions (PPIs) automatically from biomedical articles can help accelerate biomedical research. Biomedical Informatics researchers are collaborating to provide metaservices and advance the state-of-art in PPI extraction. One problem often neglected by current Natural Language Processing systems is the characteristic complexity of the sentences in biomedical literature. In this paper, we report on the impact that automatic simplification of sentences has on the performance of a state-of-art PPI extraction system, showing a substantial improvement in recall (8%) when the sentence simplification method is applied, without significant impact to precision.

  4. Protein-protein interactions as druggable targets: recent technological advances.

    Science.gov (United States)

    Higueruelo, Alicia P; Jubb, Harry; Blundell, Tom L

    2013-10-01

    Classical target-based drug discovery, where large chemical libraries are screened using inhibitory assays for a single target, has struggled to find ligands that inhibit protein-protein interactions (PPI). Nevertheless, in the past decade there have been successes that have demonstrated that PPI can be useful drug targets, and the field is now evolving fast. This review focuses on the new approaches and concepts that are being developed to tackle these challenging targets: the use of fragment based methods to explore the chemical space, stapled peptides to regulate intracellular PPI, alternatives to competitive inhibition and the use of antibodies to enable small molecule discovery for these targets.

  5. Human carotid atherosclerotic plaque protein(s) change HDL protein(s) composition and impair HDL anti-oxidant activity.

    Science.gov (United States)

    Cohen, Elad; Aviram, Michael; Khatib, Soliman; Volkova, Nina; Vaya, Jacob

    2016-01-01

    High density lipoprotein (HDL) anti-atherogenic functions are closely associated with cardiovascular disease risk factor, and are dictated by its composition, which is often affected by environmental factors. The present study investigates the effects of the human carotid plaque constituents on HDL composition and biological functions. To this end, human carotid plaques were homogenized and incubated with HDL. Results showed that after incubation, most of the apolipoprotein A1 (Apo A1) protein was released from the HDL, and HDL diameter increased by an average of approximately 2 nm. In parallel, HDL antioxidant activity was impaired. In response to homogenate treatment HDL could not prevent the accelerated oxidation of LDL caused by the homogenate. Boiling of the homogenate prior to its incubation with HDL abolished its effects on HDL composition changes. Moreover, tryptophan fluorescence quenching assay revealed an interaction between plaque component(s) and HDL, an interaction that was reduced by 50% upon using pre-boiled homogenate. These results led to hypothesize that plaque protein(s) interacted with HDL-associated Apo A1 and altered the HDL composition. Immuno-precipitation of Apo A1 that was released from the HDL after its incubation with the homogenate revealed a co-precipitation of three isomers of actin. However, beta-actin alone did not significantly affect the HDL composition, and yet the active protein within the plaque was elusive. In conclusion then, protein(s) in the homogenate interact with HDL protein(s), leading to release of Apo A1 from the HDL particle, a process that was associated with an increase in HDL diameter and with impaired HDL anti-oxidant activity.

  6. Potential disruption of protein-protein interactions by graphene oxide.

    Science.gov (United States)

    Feng, Mei; Kang, Hongsuk; Yang, Zaixing; Luan, Binquan; Zhou, Ruhong

    2016-06-14

    Graphene oxide (GO) is a promising novel nanomaterial with a wide range of potential biomedical applications due to its many intriguing properties. However, very little research has been conducted to study its possible adverse effects on protein-protein interactions (and thus subsequent toxicity to human). Here, the potential cytotoxicity of GO is investigated at molecular level using large-scale, all-atom molecular dynamics simulations to explore the interaction mechanism between a protein dimer and a GO nanosheet oxidized at different levels. Our theoretical results reveal that GO nanosheet could intercalate between the two monomers of HIV-1 integrase dimer, disrupting the protein-protein interactions and eventually lead to dimer disassociation as graphene does [B. Luan et al., ACS Nano 9(1), 663 (2015)], albeit its insertion process is slower when compared with graphene due to the additional steric and attractive interactions. This study helps to better understand the toxicity of GO to cell functions which could shed light on how to improve its biocompatibility and biosafety for its wide potential biomedical applications.

  7. Potential disruption of protein-protein interactions by graphene oxide

    Science.gov (United States)

    Feng, Mei; Kang, Hongsuk; Yang, Zaixing; Luan, Binquan; Zhou, Ruhong

    2016-06-01

    Graphene oxide (GO) is a promising novel nanomaterial with a wide range of potential biomedical applications due to its many intriguing properties. However, very little research has been conducted to study its possible adverse effects on protein-protein interactions (and thus subsequent toxicity to human). Here, the potential cytotoxicity of GO is investigated at molecular level using large-scale, all-atom molecular dynamics simulations to explore the interaction mechanism between a protein dimer and a GO nanosheet oxidized at different levels. Our theoretical results reveal that GO nanosheet could intercalate between the two monomers of HIV-1 integrase dimer, disrupting the protein-protein interactions and eventually lead to dimer disassociation as graphene does [B. Luan et al., ACS Nano 9(1), 663 (2015)], albeit its insertion process is slower when compared with graphene due to the additional steric and attractive interactions. This study helps to better understand the toxicity of GO to cell functions which could shed light on how to improve its biocompatibility and biosafety for its wide potential biomedical applications.

  8. Protein-protein interaction network analysis of cirrhosis liver disease.

    Science.gov (United States)

    Safaei, Akram; Rezaei Tavirani, Mostafa; Arefi Oskouei, Afsaneh; Zamanian Azodi, Mona; Mohebbi, Seyed Reza; Nikzamir, Abdol Rahim

    2016-01-01

    Evaluation of biological characteristics of 13 identified proteins of patients with cirrhotic liver disease is the main aim of this research. In clinical usage, liver biopsy remains the gold standard for diagnosis of hepatic fibrosis. Evaluation and confirmation of liver fibrosis stages and severity of chronic diseases require a precise and noninvasive biomarkers. Since the early detection of cirrhosis is a clinical problem, achieving a sensitive, specific and predictive novel method based on biomarkers is an important task. Essential analysis, such as gene ontology (GO) enrichment and protein-protein interactions (PPI) was undergone EXPASy, STRING Database and DAVID Bioinformatics Resources query. Based on GO analysis, most of proteins are located in the endoplasmic reticulum lumen, intracellular organelle lumen, membrane-enclosed lumen, and extracellular region. The relevant molecular functions are actin binding, metal ion binding, cation binding and ion binding. Cell adhesion, biological adhesion, cellular amino acid derivative, metabolic process and homeostatic process are the related processes. Protein-protein interaction network analysis introduced five proteins (fibroblast growth factor receptor 4, tropomyosin 4, tropomyosin 2 (beta), lectin, Lectin galactoside-binding soluble 3 binding protein and apolipoprotein A-I) as hub and bottleneck proteins. Our result indicates that regulation of lipid metabolism and cell survival are important biological processes involved in cirrhosis disease. More investigation of above mentioned proteins will provide a better understanding of cirrhosis disease.

  9. Stabilization of protein-protein interactions by small molecules.

    Science.gov (United States)

    Giordanetto, Fabrizio; Schäfer, Anja; Ottmann, Christian

    2014-11-01

    Protein-protein interactions (PPIs) are implicated in every disease and mastering the ability to influence PPIs with small molecules would considerably enlarge the druggable genome. Whereas inhibition of PPIs has repeatedly been shown to work successfully, targeted stabilization of PPIs is underrepresented in the literature. This is all the more surprising because natural products like FK506, rapamycin, brefeldin, forskolin and fusicoccin confer their physiological activity by stabilizing specific PPIs. However, recently a number of very interesting synthetic molecules have been reported from drug discovery projects that indeed achieve their desired activities by stabilizing either homo- or hetero-oligomeric complexes of their target proteins. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. Next-Generation Sequencing for Binary Protein-Protein Interactions

    Directory of Open Access Journals (Sweden)

    Bernhard eSuter

    2015-12-01

    Full Text Available The yeast two-hybrid (Y2H system exploits host cell genetics in order to display binary protein-protein interactions (PPIs via defined and selectable phenotypes. Numerous improvements have been made to this method, adapting the screening principle for diverse applications, including drug discovery and the scale-up for proteome wide interaction screens in human and other organisms. Here we discuss a systematic workflow and analysis scheme for screening data generated by Y2H and related assays that includes high-throughput selection procedures, readout of comprehensive results via next-generation sequencing (NGS, and the interpretation of interaction data via quantitative statistics. The novel assays and tools will serve the broader scientific community to harness the power of NGS technology to address PPI networks in health and disease. We discuss examples of how this next-generation platform can be applied to address specific questions in diverse fields of biology and medicine.

  11. Protein-protein interaction predictions using text mining methods.

    Science.gov (United States)

    Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Iliopoulos, Ioannis

    2015-03-01

    It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools.

  12. NOXclass: prediction of protein-protein interaction types

    Directory of Open Access Journals (Sweden)

    Sommer Ingolf

    2006-01-01

    Full Text Available Abstract Background Structural models determined by X-ray crystallography play a central role in understanding protein-protein interactions at the molecular level. Interpretation of these models requires the distinction between non-specific crystal packing contacts and biologically relevant interactions. This has been investigated previously and classification approaches have been proposed. However, less attention has been devoted to distinguishing different types of biological interactions. These interactions are classified as obligate and non-obligate according to the effect of the complex formation on the stability of the protomers. So far no automatic classification methods for distinguishing obligate, non-obligate and crystal packing interactions have been made available. Results Six interface properties have been investigated on a dataset of 243 protein interactions. The six properties have been combined using a support vector machine algorithm, resulting in NOXclass, a classifier for distinguishing obligate, non-obligate and crystal packing interactions. We achieve an accuracy of 91.8% for the classification of these three types of interactions using a leave-one-out cross-validation procedure. Conclusion NOXclass allows the interpretation and analysis of protein quaternary structures. In particular, it generates testable hypotheses regarding the nature of protein-protein interactions, when experimental results are not available. We expect this server will benefit the users of protein structural models, as well as protein crystallographers and NMR spectroscopists. A web server based on the method and the datasets used in this study are available at http://noxclass.bioinf.mpi-inf.mpg.de/.

  13. N-way FRET microscopy of multiple protein-protein interactions in live cells.

    Directory of Open Access Journals (Sweden)

    Adam D Hoppe

    Full Text Available Fluorescence Resonance Energy Transfer (FRET microscopy has emerged as a powerful tool to visualize nanoscale protein-protein interactions while capturing their microscale organization and millisecond dynamics. Recently, FRET microscopy was extended to imaging of multiple donor-acceptor pairs, thereby enabling visualization of multiple biochemical events within a single living cell. These methods require numerous equations that must be defined on a case-by-case basis. Here, we present a universal multispectral microscopy method (N-Way FRET to enable quantitative imaging for any number of interacting and non-interacting FRET pairs. This approach redefines linear unmixing to incorporate the excitation and emission couplings created by FRET, which cannot be accounted for in conventional linear unmixing. Experiments on a three-fluorophore system using blue, yellow and red fluorescent proteins validate the method in living cells. In addition, we propose a simple linear algebra scheme for error propagation from input data to estimate the uncertainty in the computed FRET images. We demonstrate the strength of this approach by monitoring the oligomerization of three FP-tagged HIV Gag proteins whose tight association in the viral capsid is readily observed. Replacement of one FP-Gag molecule with a lipid raft-targeted FP allowed direct observation of Gag oligomerization with no association between FP-Gag and raft-targeted FP. The N-Way FRET method provides a new toolbox for capturing multiple molecular processes with high spatial and temporal resolution in living cells.

  14. Arabinogalactan Glycosyltransferases: Enzyme Assay, Protein-Protein Interaction, Subcellular Localization, and Perspectives for Application

    Directory of Open Access Journals (Sweden)

    Naomi Geshi

    2014-01-01

    Full Text Available Arabinogalactan proteins (AGPs are abundant extracellular proteoglycans that are found in most plant species and involved in many cellular processes, such as cell proliferation and survival, pattern formation, and growth, and in plant microbe interaction. AGPs are synthesized by posttranslational O-glycosylation of proteins and attached glycan part often constitutes greater than 90% of the molecule. Subtle altered glycan structures during development have been considered to function as developmental markers on the cell surface, but little is known concerning the molecular mechanisms. My group has been working on glycosylation enzymes (glycosyltransferases of AGPs to investigate glycan function of the molecule. This review summarizes the recent findings from my group as for AtGalT31A, AtGlcAT14A-C, and AtGalT29A of Arabidopsis thaliana with a specific focus on the (i biochemical enzyme activities; (ii subcellular compartments targeted by the glycosyltransferases; and (iii protein-protein interactions. I also discuss application aspect of glycosyltransferase in improving AGP-based product used in industry, for example, gum arabic.

  15. Coordination of Pancreatic HCO3- Secretion by Protein-Protein Interaction between Membrane Transporters

    Directory of Open Access Journals (Sweden)

    Lee MG

    2001-07-01

    Full Text Available Increasing evidence suggests that protein-protein interaction is essential in many biological processes including epithelial transport. In this report, we discuss the significance of protein interactions to HCO(3(- secretion in pancreatic duct cells. In pancreatic ducts HCO(3(- secretion is mediated by cystic fibrosis transmembrane conductance regulator (CFTR activated luminal Cl(-/HCO(3(- exchange activity and HCO(3(- absorption is achieved by Na(+-dependent mechanisms including Na(+/H(+ exchanger 3 (NHE3. We found biochemical and functional association between CFTR and NHE3. In addition, protein binding through PDZ modules is needed for this regulatory interaction. CFTR affected NHE3 activities in two ways. Acutely, CFTR augmented the cAMP-dependent inhibition of NHE3. In a chronic mechanism, CFTR increases the luminal expression of Na(+/H(+ exchange in pancreatic duct cells. These findings reveal that protein complexes in the plasma membrane of pancreatic duct cells are highly organized for efficient HCO(3(- secretion.

  16. Mapping of protein-protein interaction sites in the plant-type [2Fe-2S] ferredoxin.

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    Haruka Kameda

    Full Text Available Knowing the manner of protein-protein interactions is vital for understanding biological events. The plant-type [2Fe-2S] ferredoxin (Fd, a well-known small iron-sulfur protein with low redox potential, partitions electrons to a variety of Fd-dependent enzymes via specific protein-protein interactions. Here we have refined the crystal structure of a recombinant plant-type Fd I from the blue green alga Aphanothece sacrum (AsFd-I at 1.46 Å resolution on the basis of the synchrotron radiation data. Incorporating the revised amino-acid sequence, our analysis corrects the 3D structure previously reported; we identified the short α-helix (67-71 near the active center, which is conserved in other plant-type [2Fe-2S] Fds. Although the 3D structures of the four molecules in the asymmetric unit are similar to each other, detailed comparison of the four structures revealed the segments whose conformations are variable. Structural comparison between the Fds from different sources showed that the distribution of the variable segments in AsFd-I is highly conserved in other Fds, suggesting the presence of intrinsically flexible regions in the plant-type [2Fe-2S] Fd. A few structures of the complexes with Fd-dependent enzymes clearly demonstrate that the protein-protein interactions are achieved through these variable regions in Fd. The results described here will provide a guide for interpreting the biochemical and mutational studies that aim at the manner of interactions with Fd-dependent enzymes.

  17. Analysis and application of large-scale protein-protein interaction data sets

    Institute of Scientific and Technical Information of China (English)

    SUN Jingchun; XU Jinlin; LI Yixue; SHI Tieliu

    2005-01-01

    Protein-protein interactions play key roles in cells. Lots of experimental approaches and in silico methods have been developed to identify and predict large-scale protein-protein interactions. However, compared with the traditionally experimental results, the high-throughput protein-protein interaction data often contain the false positives in high probability. In order to fully utilize the large-scale data, it is necessary to develop bioinformatic methods for systematically evaluating those data in order to further improve the data reliability and mine biological information. This review summarizes the methodologies of analysis and application of high-throughput protein-protein interaction data, including the evaluation methods, the relationship between protein-protein interaction data and other protein biological information, and their applications in biological study. In addition, this paper also suggests some interesting topics on mining high-throughput protein-protein interaction data.

  18. Genome-wide analysis of protein-protein interactions and involvement of viral proteins in SARS-CoV replication.

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    Ji'an Pan

    Full Text Available Analyses of viral protein-protein interactions are an important step to understand viral protein functions and their underlying molecular mechanisms. In this study, we adopted a mammalian two-hybrid system to screen the genome-wide intraviral protein-protein interactions of SARS coronavirus (SARS-CoV and therefrom revealed a number of novel interactions which could be partly confirmed by in vitro biochemical assays. Three pairs of the interactions identified were detected in both directions: non-structural protein (nsp 10 and nsp14, nsp10 and nsp16, and nsp7 and nsp8. The interactions between the multifunctional nsp10 and nsp14 or nsp16, which are the unique proteins found in the members of Nidovirales with large RNA genomes including coronaviruses and toroviruses, may have important implication for the mechanisms of replication/transcription complex assembly and functions of these viruses. Using a SARS-CoV replicon expressing a luciferase reporter under the control of a transcription regulating sequence, it has been shown that several viral proteins (N, X and SUD domains of nsp3, and nsp12 provided in trans stimulated the replicon reporter activity, indicating that these proteins may regulate coronavirus replication and transcription. Collectively, our findings provide a basis and platform for further characterization of the functions and mechanisms of coronavirus proteins.

  19. Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

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    Rushikesh Sable

    2015-06-01

    Full Text Available Blocking protein-protein interactions (PPI using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

  20. Oligomeric protein structure networks: insights into protein-protein interactions

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    Brinda KV

    2005-12-01

    Full Text Available Abstract Background Protein-protein association is essential for a variety of cellular processes and hence a large number of investigations are being carried out to understand the principles of protein-protein interactions. In this study, oligomeric protein structures are viewed from a network perspective to obtain new insights into protein association. Structure graphs of proteins have been constructed from a non-redundant set of protein oligomer crystal structures by considering amino acid residues as nodes and the edges are based on the strength of the non-covalent interactions between the residues. The analysis of such networks has been carried out in terms of amino acid clusters and hubs (highly connected residues with special emphasis to protein interfaces. Results A variety of interactions such as hydrogen bond, salt bridges, aromatic and hydrophobic interactions, which occur at the interfaces are identified in a consolidated manner as amino acid clusters at the interface, from this study. Moreover, the characterization of the highly connected hub-forming residues at the interfaces and their comparison with the hubs from the non-interface regions and the non-hubs in the interface regions show that there is a predominance of charged interactions at the interfaces. Further, strong and weak interfaces are identified on the basis of the interaction strength between amino acid residues and the sizes of the interface clusters, which also show that many protein interfaces are stronger than their monomeric protein cores. The interface strengths evaluated based on the interface clusters and hubs also correlate well with experimentally determined dissociation constants for known complexes. Finally, the interface hubs identified using the present method correlate very well with experimentally determined hotspots in the interfaces of protein complexes obtained from the Alanine Scanning Energetics database (ASEdb. A few predictions of interface hot

  1. Comprehensive peptidomimetic libraries targeting protein-protein interactions.

    Science.gov (United States)

    Whitby, Landon R; Boger, Dale L

    2012-10-16

    Transient protein-protein interactions (PPIs) are essential components in cellular signaling pathways as well as in important processes such as viral infection, replication, and immune suppression. The unknown or uncharacterized PPIs involved in such interaction networks often represent compelling therapeutic targets for drug discovery. To date, however, the main strategies for discovery of small molecule modulators of PPIs are typically limited to structurally characterized targets. Recent developments in molecular scaffolds that mimic the side chain display of peptide secondary structures have yielded effective designs, but few screening libraries of such mimetics are available to interrogate PPI targets. We initiated a program to prepare a comprehensive small molecule library designed to mimic the three major recognition motifs that mediate PPIs (α-helix, β-turn, and β-strand). Three libraries would be built around templates designed to mimic each such secondary structure and substituted with all triplet combinations of groups representing the 20 natural amino acid side chains. When combined, the three libraries would contain a member capable of mimicking the key interaction and recognition residues of most targetable PPIs. In this Account, we summarize the results of the design, synthesis, and validation of an 8000 member α-helix mimetic library and a 4200 member β-turn mimetic library. We expect that the screening of these libraries will not only provide lead structures against α-helix- or β-turn-mediated protein-protein or peptide-receptor interactions, even if the nature of the interaction is unknown, but also yield key insights into the recognition motif (α-helix or β-turn) and identify the key residues mediating the interaction. Consistent with this expectation, the screening of the libraries against p53/MDM2 and HIV-1 gp41 (α-helix mimetic library) or the opioid receptors (β-turn mimetic library) led to the discovery of library members expected

  2. PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs

    Directory of Open Access Journals (Sweden)

    Greenblatt Jack

    2006-07-01

    Full Text Available Abstract Background Identification of protein interaction networks has received considerable attention in the post-genomic era. The currently available biochemical approaches used to detect protein-protein interactions are all time and labour intensive. Consequently there is a growing need for the development of computational tools that are capable of effectively identifying such interactions. Results Here we explain the development and implementation of a novel Protein-Protein Interaction Prediction Engine termed PIPE. This tool is capable of predicting protein-protein interactions for any target pair of the yeast Saccharomyces cerevisiae proteins from their primary structure and without the need for any additional information or predictions about the proteins. PIPE showed a sensitivity of 61% for detecting any yeast protein interaction with 89% specificity and an overall accuracy of 75%. This rate of success is comparable to those associated with the most commonly used biochemical techniques. Using PIPE, we identified a novel interaction between YGL227W (vid30 and YMR135C (gid8 yeast proteins. This lead us to the identification of a novel yeast complex that here we term vid30 complex (vid30c. The observed interaction was confirmed by tandem affinity purification (TAP tag, verifying the ability of PIPE to predict novel protein-protein interactions. We then used PIPE analysis to investigate the internal architecture of vid30c. It appeared from PIPE analysis that vid30c may consist of a core and a secondary component. Generation of yeast gene deletion strains combined with TAP tagging analysis indicated that the deletion of a member of the core component interfered with the formation of vid30c, however, deletion of a member of the secondary component had little effect (if any on the formation of vid30c. Also, PIPE can be used to analyse yeast proteins for which TAP tagging fails, thereby allowing us to predict protein interactions that are not

  3. Alternative protein-protein interfaces are frequent exceptions.

    Directory of Open Access Journals (Sweden)

    Tobias Hamp

    Full Text Available The intricate molecular details of protein-protein interactions (PPIs are crucial for function. Therefore, measuring the same interacting protein pair again, we expect the same result. This work measured the similarity in the molecular details of interaction for the same and for homologous protein pairs between different experiments. All scores analyzed suggested that different experiments often find exceptions in the interfaces of similar PPIs: up to 22% of all comparisons revealed some differences even for sequence-identical pairs of proteins. The corresponding number for pairs of close homologs reached 68%. Conversely, the interfaces differed entirely for 12-29% of all comparisons. All these estimates were calculated after redundancy reduction. The magnitude of interface differences ranged from subtle to the extreme, as illustrated by a few examples. An extreme case was a change of the interacting domains between two observations of the same biological interaction. One reason for different interfaces was the number of copies of an interaction in the same complex: the probability of observing alternative binding modes increases with the number of copies. Even after removing the special cases with alternative hetero-interfaces to the same homomer, a substantial variability remained. Our results strongly support the surprising notion that there are many alternative solutions to make the intricate molecular details of PPIs crucial for function.

  4. Protein-protein interaction based on pairwise similarity

    Directory of Open Access Journals (Sweden)

    Zaki Nazar

    2009-05-01

    Full Text Available Abstract Background Protein-protein interaction (PPI is essential to most biological processes. Abnormal interactions may have implications in a number of neurological syndromes. Given that the association and dissociation of protein molecules is crucial, computational tools capable of effectively identifying PPI are desirable. In this paper, we propose a simple yet effective method to detect PPI based on pairwise similarity and using only the primary structure of the protein. The PPI based on Pairwise Similarity (PPI-PS method consists of a representation of each protein sequence by a vector of pairwise similarities against large subsequences of amino acids created by a shifting window which passes over concatenated protein training sequences. Each coordinate of this vector is typically the E-value of the Smith-Waterman score. These vectors are then used to compute the kernel matrix which will be exploited in conjunction with support vector machines. Results To assess the ability of the proposed method to recognize the difference between "interacted" and "non-interacted" proteins pairs, we applied it on different datasets from the available yeast saccharomyces cerevisiae protein interaction. The proposed method achieved reasonable improvement over the existing state-of-the-art methods for PPI prediction. Conclusion Pairwise similarity score provides a relevant measure of similarity between protein sequences. This similarity incorporates biological knowledge about proteins and it is extremely powerful when combined with support vector machine to predict PPI.

  5. Protein-Protein Interaction Network and Gene Ontology

    Science.gov (United States)

    Choi, Yunkyu; Kim, Seok; Yi, Gwan-Su; Park, Jinah

    Evolution of computer technologies makes it possible to access a large amount and various kinds of biological data via internet such as DNA sequences, proteomics data and information discovered about them. It is expected that the combination of various data could help researchers find further knowledge about them. Roles of a visualization system are to invoke human abilities to integrate information and to recognize certain patterns in the data. Thus, when the various kinds of data are examined and analyzed manually, an effective visualization system is an essential part. One instance of these integrated visualizations can be combination of protein-protein interaction (PPI) data and Gene Ontology (GO) which could help enhance the analysis of PPI network. We introduce a simple but comprehensive visualization system that integrates GO and PPI data where GO and PPI graphs are visualized side-by-side and supports quick reference functions between them. Furthermore, the proposed system provides several interactive visualization methods for efficiently analyzing the PPI network and GO directedacyclic- graph such as context-based browsing and common ancestors finding.

  6. Drug Target Protein-Protein Interaction Networks: A Systematic Perspective

    Directory of Open Access Journals (Sweden)

    Yanghe Feng

    2017-01-01

    Full Text Available The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target’s homologue set containing 102 potential target proteins is predicted in the paper.

  7. Prediction of Protein-Protein Interactions Using Protein Signature Profiling

    Institute of Scientific and Technical Information of China (English)

    Mahmood A. Mahdavi; Yen-Han Lin

    2007-01-01

    Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain inter- actions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as "interacting". In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis ele- gans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area un- der the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs in- creased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on aver- age 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.

  8. Modulators of 14-3-3 Protein-Protein Interactions.

    Science.gov (United States)

    Stevers, Loes M; Sijbesma, Eline; Botta, Maurizio; MacKintosh, Carol; Obsil, Tomas; Landrieu, Isabelle; Cau, Ylenia; Wilson, Andrew J; Karawajczyk, Anna; Eickhoff, Jan; Davis, Jeremy; Hann, Michael M; O'Mahony, Gavin; Doveston, Richard G; Brunsveld, Luc; Ottmann, Christian

    2017-10-02

    Direct interactions between proteins are essential for the regulation of their functions in biological pathways. Targeting the complex network of protein-protein interactions (PPIs) has now been widely recognized as an attractive means to therapeutically intervene in disease states. Even though this is a challenging endeavor and PPIs have long been regarded as 'undruggable' targets, the last two decades have seen an increasing number of successful examples of PPI modulators resulting in a growing interest in this field. PPI modulation requires novel approaches and the integrated efforts of multiple disciplines to be a fruitful strategy. This Perspective focuses on the hub protein 14-3-3, which has several hundred identified protein interaction partners and is therefore involved in a wide range of cellular processes and diseases. Here, we aim to provide an integrated overview of the approaches explored for the modulation of 14-3-3 PPIs and review the examples resulting from these efforts in both inhibiting and stabilizing specific 14-3-3 protein complexes by small molecules, peptide-mimetics and natural products.

  9. Stabilizer-Guided Inhibition of Protein-Protein Interactions.

    Science.gov (United States)

    Milroy, Lech-Gustav; Bartel, Maria; Henen, Morkos A; Leysen, Seppe; Adriaans, Joris M C; Brunsveld, Luc; Landrieu, Isabelle; Ottmann, Christian

    2015-12-21

    The discovery of novel protein-protein interaction (PPI) modulators represents one of the great molecular challenges of the modern era. PPIs can be modulated by either inhibitor or stabilizer compounds, which target different though proximal regions of the protein interface. In principle, protein-stabilizer complexes can guide the design of PPI inhibitors (and vice versa). In the present work, we combine X-ray crystallographic data from both stabilizer and inhibitor co-crystal complexes of the adapter protein 14-3-3 to characterize, down to the atomic scale, inhibitors of the 14-3-3/Tau PPI, a potential drug target to treat Alzheimer's disease. The most potent compound notably inhibited the binding of phosphorylated full-length Tau to 14-3-3 according to NMR spectroscopy studies. Our work sets a precedent for the rational design of PPI inhibitors guided by PPI stabilizer-protein complexes while potentially enabling access to new synthetically tractable stabilizers of 14-3-3 and other PPIs. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. Evolvability of yeast protein-protein interaction interfaces.

    Science.gov (United States)

    Talavera, David; Williams, Simon G; Norris, Matthew G S; Robertson, David L; Lovell, Simon C

    2012-06-22

    The functional importance of protein-protein interactions indicates that there should be strong evolutionary constraint on their interaction interfaces. However, binding interfaces are frequently affected by amino acid replacements. Change due to coevolution within interfaces can contribute to variability but is not ubiquitous. An alternative explanation for the ability of surfaces to accept replacements may be that many residues can be changed without affecting the interaction. Candidates for these types of residues are those that make interchain interaction only through the protein main chain, β-carbon, or associated hydrogen atoms. Since almost all residues have these atoms, we hypothesize that this subset of interface residues may be more easily substituted than those that make interactions through other atoms. We term such interactions "residue type independent." Investigating this hypothesis, we find that nearly a quarter of residues in protein interaction interfaces make exclusively interchain residue-type-independent contacts. These residues are less structurally constrained and less conserved than residues making residue-type-specific interactions. We propose that residue-type-independent interactions allow substitutions in binding interfaces while the specificity of binding is maintained.

  11. Pathogen mimicry of host protein-protein interfaces modulates immunity.

    Science.gov (United States)

    Guven-Maiorov, Emine; Tsai, Chung-Jung; Nussinov, Ruth

    2016-10-01

    Signaling pathways shape and transmit the cell's reaction to its changing environment; however, pathogens can circumvent this response by manipulating host signaling. To subvert host defense, they beat it at its own game: they hijack host pathways by mimicking the binding surfaces of host-encoded proteins. For this, it is not necessary to achieve global protein homology; imitating merely the interaction surface is sufficient. Different protein folds often interact via similar protein-protein interface architectures. This similarity in binding surfaces permits the pathogenic protein to compete with a host target protein. Thus, rather than binding a host-encoded partner, the host protein hub binds the pathogenic surrogate. The outcome can be dire: rewiring or repurposing the host pathways, shifting the cell signaling landscape and consequently the immune response. They can also cause persistent infections as well as cancer by modulating key signaling pathways, such as those involving Ras. Mapping the rewired host-pathogen 'superorganism' interaction network - along with its structural details - is critical for in-depth understanding of pathogenic mechanisms and developing efficient therapeutics. Here, we overview the role of molecular mimicry in pathogen host evasion as well as types of molecular mimicry mechanisms that emerged during evolution.

  12. Bioinformatic Prediction of WSSV-Host Protein-Protein Interaction

    Directory of Open Access Journals (Sweden)

    Zheng Sun

    2014-01-01

    Full Text Available WSSV is one of the most dangerous pathogens in shrimp aquaculture. However, the molecular mechanism of how WSSV interacts with shrimp is still not very clear. In the present study, bioinformatic approaches were used to predict interactions between proteins from WSSV and shrimp. The genome data of WSSV (NC_003225.1 and the constructed transcriptome data of F. chinensis were used to screen potentially interacting proteins by searching in protein interaction databases, including STRING, Reactome, and DIP. Forty-four pairs of proteins were suggested to have interactions between WSSV and the shrimp. Gene ontology analysis revealed that 6 pairs of these interacting proteins were classified into “extracellular region” or “receptor complex” GO-terms. KEGG pathway analysis showed that they were involved in the “ECM-receptor interaction pathway.” In the 6 pairs of interacting proteins, an envelope protein called “collagen-like protein” (WSSV-CLP encoded by an early virus gene “wsv001” in WSSV interacted with 6 deduced proteins from the shrimp, including three integrin alpha (ITGA, two integrin beta (ITGB, and one syndecan (SDC. Sequence analysis on WSSV-CLP, ITGA, ITGB, and SDC revealed that they possessed the sequence features for protein-protein interactions. This study might provide new insights into the interaction mechanisms between WSSV and shrimp.

  13. Protein-protein interaction network of celiac disease.

    Science.gov (United States)

    Zamanian Azodi, Mona; Peyvandi, Hassan; Rostami-Nejad, Mohammad; Safaei, Akram; Rostami, Kamran; Vafaee, Reza; Heidari, Mohammadhossein; Hosseini, Mostafa; Zali, Mohammad Reza

    2016-01-01

    The aim of this study is to investigate the Protein-Protein Interaction Network of Celiac Disease. Celiac disease (CD) is an autoimmune disease with susceptibility of individuals to gluten of wheat, rye and barley. Understanding the molecular mechanisms and involved pathway may lead to the development of drug target discovery. The protein interaction network is one of the supportive fields to discover the pathogenesis biomarkers for celiac disease. In the present study, we collected the articles that focused on the proteomic data in celiac disease. According to the gene expression investigations of these articles, 31 candidate proteins were selected for this study. The networks of related differentially expressed protein were explored using Cytoscape 3.3 and the PPI analysis methods such as MCODE and ClueGO. According to the network analysis Ubiquitin C, Heat shock protein 90kDa alpha (cytosolic and Grp94); class A, B and 1 member, Heat shock 70kDa protein, and protein 5 (glucose-regulated protein, 78kDa), T-complex, Chaperon in containing TCP1; subunit 7 (beta) and subunit 4 (delta) and subunit 2 (beta), have been introduced as hub-bottlnecks proteins. HSP90AA1, MKKS, EZR, HSPA14, APOB and CAD have been determined as seed proteins. Chaperons have a bold presentation in curtail area in network therefore these key proteins beside the other hub-bottlneck proteins may be a suitable candidates biomarker panel for diagnosis, prognosis and treatment processes in celiac disease.

  14. Dynamics of protein-protein interactions studied by paramagnetic NMR spectroscopy

    NARCIS (Netherlands)

    Somireddy Venkata, Bharat Kumar Reddy

    2012-01-01

    Protein-protein interactions play an important role in all cellular processes such as signal transduction, electron transfer, gene regulation, transcription, and translation. Understanding these protein-protein interactions at the molecular level, is an important aim in structural biology. The

  15. Dynamics of protein-protein interactions studied by paramagnetic NMR spectroscopy

    NARCIS (Netherlands)

    Somireddy Venkata, Bharat Kumar Reddy

    2012-01-01

    Protein-protein interactions play an important role in all cellular processes such as signal transduction, electron transfer, gene regulation, transcription, and translation. Understanding these protein-protein interactions at the molecular level, is an important aim in structural biology. The prote

  16. A scored human protein-protein interaction network to catalyze genomic interpretation

    DEFF Research Database (Denmark)

    Li, Taibo; Wernersson, Rasmus; Hansen, Rasmus B;

    2017-01-01

    Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (In...

  17. A Laboratory-Intensive Course on the Experimental Study of Protein-Protein Interactions

    Science.gov (United States)

    Witherow, D. Scott; Carson, Sue

    2011-01-01

    The study of protein-protein interactions is important to scientists in a wide range of disciplines. We present here the assessment of a lab-intensive course that teaches students techniques used to identify and further study protein-protein interactions. One of the unique elements of the course is that students perform a yeast two-hybrid screen…

  18. A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

    NARCIS (Netherlands)

    Melo, Rita; Fieldhouse, Robert; Melo, André; Correia, João D G; Cordeiro, Maria Natália D S; Gümüş, Zeynep H; Costa, Joaquim; Bonvin, Alexandre M J J|info:eu-repo/dai/nl/113691238; de Sousa Moreira, Irina|info:eu-repo/dai/nl/412025000

    2016-01-01

    Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model

  19. Discovering functional interaction patterns in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Can Tolga

    2008-06-01

    Full Text Available Abstract Background In recent years, a considerable amount of research effort has been directed to the analysis of biological networks with the availability of genome-scale networks of genes and/or proteins of an increasing number of organisms. A protein-protein interaction (PPI network is a particular biological network which represents physical interactions between pairs of proteins of an organism. Major research on PPI networks has focused on understanding the topological organization of PPI networks, evolution of PPI networks and identification of conserved subnetworks across different species, discovery of modules of interaction, use of PPI networks for functional annotation of uncharacterized proteins, and improvement of the accuracy of currently available networks. Results In this article, we map known functional annotations of proteins onto a PPI network in order to identify frequently occurring interaction patterns in the functional space. We propose a new frequent pattern identification technique, PPISpan, adapted specifically for PPI networks from a well-known frequent subgraph identification method, gSpan. Existing module discovery techniques either look for specific clique-like highly interacting protein clusters or linear paths of interaction. However, our goal is different; instead of single clusters or pathways, we look for recurring functional interaction patterns in arbitrary topologies. We have applied PPISpan on PPI networks of Saccharomyces cerevisiae and identified a number of frequently occurring functional interaction patterns. Conclusion With the help of PPISpan, recurring functional interaction patterns in an organism's PPI network can be identified. Such an analysis offers a new perspective on the modular organization of PPI networks. The complete list of identified functional interaction patterns is available at http://bioserver.ceng.metu.edu.tr/PPISpan/.

  20. Protein-protein interactions within late pre-40S ribosomes.

    Directory of Open Access Journals (Sweden)

    Melody G Campbell

    Full Text Available Ribosome assembly in eukaryotic organisms requires more than 200 assembly factors to facilitate and coordinate rRNA transcription, processing, and folding with the binding of the ribosomal proteins. Many of these assembly factors bind and dissociate at defined times giving rise to discrete assembly intermediates, some of which have been partially characterized with regards to their protein and RNA composition. Here, we have analyzed the protein-protein interactions between the seven assembly factors bound to late cytoplasmic pre-40S ribosomes using recombinant proteins in binding assays. Our data show that these factors form two modules: one comprising Enp1 and the export adaptor Ltv1 near the beak structure, and the second comprising the kinase Rio2, the nuclease Nob1, and a regulatory RNA binding protein Dim2/Pno1 on the front of the head. The GTPase-like Tsr1 and the universally conserved methylase Dim1 are also peripherally connected to this second module. Additionally, in an effort to further define the locations for these essential proteins, we have analyzed the interactions between these assembly factors and six ribosomal proteins: Rps0, Rps3, Rps5, Rps14, Rps15 and Rps29. Together, these results and previous RNA-protein crosslinking data allow us to propose a model for the binding sites of these seven assembly factors. Furthermore, our data show that the essential kinase Rio2 is located at the center of the pre-ribosomal particle and interacts, directly or indirectly, with every other assembly factor, as well as three ribosomal proteins required for cytoplasmic 40S maturation. These data suggest that Rio2 could play a central role in regulating cytoplasmic maturation steps.

  1. Protein-protein interaction network of celiac disease

    Science.gov (United States)

    Zamanian Azodi, Mona; Peyvandi, Hassan; Rostami-Nejad, Mohammad; Safaei, Akram; Rostami, Kamran; Vafaee, Reza; Heidari, Mohammadhossein; Hosseini, Mostafa; Zali, Mohammad Reza

    2016-01-01

    Aim: The aim of this study is to investigate the Protein-Protein Interaction Network of Celiac Disease. Background: Celiac disease (CD) is an autoimmune disease with susceptibility of individuals to gluten of wheat, rye and barley. Understanding the molecular mechanisms and involved pathway may lead to the development of drug target discovery. The protein interaction network is one of the supportive fields to discover the pathogenesis biomarkers for celiac disease. Material and methods: In the present study, we collected the articles that focused on the proteomic data in celiac disease. According to the gene expression investigations of these articles, 31 candidate proteins were selected for this study. The networks of related differentially expressed protein were explored using Cytoscape 3.3 and the PPI analysis methods such as MCODE and ClueGO. Results: According to the network analysis Ubiquitin C, Heat shock protein 90kDa alpha (cytosolic and Grp94); class A, B and 1 member, Heat shock 70kDa protein, and protein 5 (glucose-regulated protein, 78kDa), T-complex, Chaperon in containing TCP1; subunit 7 (beta) and subunit 4 (delta) and subunit 2 (beta), have been introduced as hub-bottlnecks proteins. HSP90AA1, MKKS, EZR, HSPA14, APOB and CAD have been determined as seed proteins. Conclusion: Chaperons have a bold presentation in curtail area in network therefore these key proteins beside the other hub-bottlneck proteins may be a suitable candidates biomarker panel for diagnosis, prognosis and treatment processes in celiac disease. PMID:27895852

  2. Inferring high-confidence human protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Yu Xueping

    2012-05-01

    Full Text Available Abstract Background As numerous experimental factors drive the acquisition, identification, and interpretation of protein-protein interactions (PPIs, aggregated assemblies of human PPI data invariably contain experiment-dependent noise. Ascertaining the reliability of PPIs collected from these diverse studies and scoring them to infer high-confidence networks is a non-trivial task. Moreover, a large number of PPIs share the same number of reported occurrences, making it impossible to distinguish the reliability of these PPIs and rank-order them. For example, for the data analyzed here, we found that the majority (>83% of currently available human PPIs have been reported only once. Results In this work, we proposed an unsupervised statistical approach to score a set of diverse, experimentally identified PPIs from nine primary databases to create subsets of high-confidence human PPI networks. We evaluated this ranking method by comparing it with other methods and assessing their ability to retrieve protein associations from a number of diverse and independent reference sets. These reference sets contain known biological data that are either directly or indirectly linked to interactions between proteins. We quantified the average effect of using ranked protein interaction data to retrieve this information and showed that, when compared to randomly ranked interaction data sets, the proposed method created a larger enrichment (~134% than either ranking based on the hypergeometric test (~109% or occurrence ranking (~46%. Conclusions From our evaluations, it was clear that ranked interactions were always of value because higher-ranked PPIs had a higher likelihood of retrieving high-confidence experimental data. Reducing the noise inherent in aggregated experimental PPIs via our ranking scheme further increased the accuracy and enrichment of PPIs derived from a number of biologically relevant data sets. These results suggest that using our high

  3. Optical methods in the study of protein-protein interactions.

    Science.gov (United States)

    Masi, Alessio; Cicchi, Riccardo; Carloni, Adolfo; Pavone, Francesco Saverio; Arcangeli, Annarosa

    2010-01-01

    Förster (or Fluorescence) resonance energy transfer (FRET) is a physical process in which energy is transferred nonradiatively from an excited fluorophore, serving as a donor, to another chromophore (acceptor). Among the techniques related to fluorescence microscopy, FRET is unique in providing signals sensitive to intra- and intermolecular distances in the 1-10 nm range. Because of its potency, FRET is increasingly used to visualize and quantify the dynamics of protein-protein interaction in living cells, with high spatio-temporal resolution. Here we describe the physical bases of FRET, detailing the principal methods applied: (1) measurement of signal intensity and (2) analysis of fluorescence lifetime (FLIM). Although several technical complications must be carefully considered, both methods can be applied fruitfully to specific fields. For example, FRET based on intensity detection is more suitable to follow biological phenomena at a finely tuned spatial and temporal scale. Furthermore, a specific fluorescence signal occurring close to the plasma membrane (advantage of the discovery and use of spontaneously fluorescent proteins, like the green fluorescent protein (GFP). Until now, FRET has been widely used to analyze the structural characteristics of several proteins, including integrins and ion channels. More recently, this method has been applied to clarify the interaction dynamics of these classes of membrane proteins with cytosolic signaling proteins. We report two examples in which the interaction dynamics between integrins and ion channels have been studied with FRET methods. Using fluorescent antibodies and applying FRET-FLIM, the direct interaction of beta1 integrin with the receptor for Epidermal Growth Factor (EGF-R) has been proved in living endothelial cells. A different approach, based on TIRFM measurement of the FRET intensity of fluorescently labeled recombinant proteins, suggests that a direct interaction also occurs between integrins and the

  4. Targeting protein-protein interactions for parasite control.

    Directory of Open Access Journals (Sweden)

    Christina M Taylor

    Full Text Available Finding new drug targets for pathogenic infections would be of great utility for humanity, as there is a large need to develop new drugs to fight infections due to the developing resistance and side effects of current treatments. Current drug targets for pathogen infections involve only a single protein. However, proteins rarely act in isolation, and the majority of biological processes occur via interactions with other proteins, so protein-protein interactions (PPIs offer a realm of unexplored potential drug targets and are thought to be the next-generation of drug targets. Parasitic worms were chosen for this study because they have deleterious effects on human health, livestock, and plants, costing society billions of dollars annually and many sequenced genomes are available. In this study, we present a computational approach that utilizes whole genomes of 6 parasitic and 1 free-living worm species and 2 hosts. The species were placed in orthologous groups, then binned in species-specific orthologous groups. Proteins that are essential and conserved among species that span a phyla are of greatest value, as they provide foundations for developing broad-control strategies. Two PPI databases were used to find PPIs within the species specific bins. PPIs with unique helminth proteins and helminth proteins with unique features relative to the host, such as indels, were prioritized as drug targets. The PPIs were scored based on RNAi phenotype and homology to the PDB (Protein DataBank. EST data for the various life stages, GO annotation, and druggability were also taken into consideration. Several PPIs emerged from this study as potential drug targets. A few interactions were supported by co-localization of expression in M. incognita (plant parasite and B. malayi (H. sapiens parasite, which have extremely different modes of parasitism. As more genomes of pathogens are sequenced and PPI databases expanded, this methodology will become increasingly

  5. Protein-Protein Interactions in Virus-Host Systems.

    Science.gov (United States)

    Brito, Anderson F; Pinney, John W

    2017-01-01

    To study virus-host protein interactions, knowledge about viral and host protein architectures and repertoires, their particular evolutionary mechanisms, and information on relevant sources of biological data is essential. The purpose of this review article is to provide a thorough overview about these aspects. Protein domains are basic units defining protein interactions, and the uniqueness of viral domain repertoires, their mode of evolution, and their roles during viral infection make viruses interesting models of study. Mutations at protein interfaces can reduce or increase their binding affinities by changing protein electrostatics and structural properties. During the course of a viral infection, both pathogen and cellular proteins are constantly competing for binding partners. Endogenous interfaces mediating intraspecific interactions-viral-viral or host-host interactions-are constantly targeted and inhibited by exogenous interfaces mediating viral-host interactions. From a biomedical perspective, blocking such interactions is the main mechanism underlying antiviral therapies. Some proteins are able to bind multiple partners, and their modes of interaction define how fast these "hub proteins" evolve. "Party hubs" have multiple interfaces; they establish simultaneous/stable (domain-domain) interactions, and tend to evolve slowly. On the other hand, "date hubs" have few interfaces; they establish transient/weak (domain-motif) interactions by means of short linear peptides (15 or fewer residues), and can evolve faster. Viral infections are mediated by several protein-protein interactions (PPIs), which can be represented as networks (protein interaction networks, PINs), with proteins being depicted as nodes, and their interactions as edges. It has been suggested that viral proteins tend to establish interactions with more central and highly connected host proteins. In an evolutionary arms race, viral and host proteins are constantly changing their interface

  6. Studying protein-protein interactions via blot overlay/far western blot.

    Science.gov (United States)

    Hall, Randy A

    2015-01-01

    Blot overlay is a useful method for studying protein-protein interactions. This technique involves fractionating proteins on SDS-PAGE, blotting to nitrocellulose or PVDF membrane, and then incubating with a probe of interest. The probe is typically a protein that is radiolabeled, biotinylated, or simply visualized with a specific antibody. When the probe is visualized via antibody detection, this technique is often referred to as "Far Western blot." Many different kinds of protein-protein interactions can be studied via blot overlay, and the method is applicable to screens for unknown protein-protein interactions as well as to the detailed characterization of known interactions.

  7. A reliability measure of protein-protein interactions and a reliability measure-based search engine.

    Science.gov (United States)

    Park, Byungkyu; Han, Kyungsook

    2010-02-01

    Many methods developed for estimating the reliability of protein-protein interactions are based on the topology of protein-protein interaction networks. This paper describes a new reliability measure for protein-protein interactions, which does not rely on the topology of protein interaction networks, but expresses biological information on functional roles, sub-cellular localisations and protein classes as a scoring schema. The new measure is useful for filtering many spurious interactions, as well as for estimating the reliability of protein interaction data. In particular, the reliability measure can be used to search protein-protein interactions with the desired reliability in databases. The reliability-based search engine is available at http://yeast.hpid.org. We believe this is the first search engine for interacting proteins, which is made available to public. The search engine and the reliability measure of protein interactions should provide useful information for determining proteins to focus on.

  8. Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners

    National Research Council Canada - National Science Library

    Shoemaker, Benjamin A; Panchenko, Anna R

    2007-01-01

    .... In this review we describe different approaches to predict protein interaction partners as well as highlight recent achievements in the prediction of specific domains mediating protein-protein interactions...

  9. Single methyl groups can act as toggle switches to specify transmembrane protein-protein interactions

    DEFF Research Database (Denmark)

    He, Li; Steinocher, Helena; Shelar, Ashish

    2017-01-01

    Transmembrane domains (TMDs) engage in protein-protein interactions that regulate many cellular processes, but the rules governing the specificity of these interactions are poorly understood. To discover these principles, we analyzed 26-residue model transmembrane proteins consisting exclusively ...

  10. Stabilized helical peptides: a strategy to target protein-protein interactions.

    Science.gov (United States)

    Klein, Mark A

    2014-08-14

    Protein-protein interactions are critical for cell proliferation, differentiation, and function. Peptides hold great promise for clinical applications focused on targeting protein-protein interactions. Advantages of peptides include a large chemical space and potential diversity of sequences and structures. However, peptides do present well-known challenges for drug development. Progress has been made in the development of stabilizing alpha helices for potential therapeutic applications. Advantages and disadvantages of different methods of helical peptide stabilization are discussed.

  11. Quantitative Analysis of Spatial Protein-protein Proximity in Fluorescence Confocal Microscopy

    Science.gov (United States)

    Wu, Yong; Liu, Yi-Kuang; Eghbali, Mansoureh; Stefani, Enrico

    2009-02-01

    To quantify spatial protein-protein proximity (colocalization) in fluorescence microscopic images, cross-correlation and autocorrelation functions were decomposed into fast and slowly decaying components. The fast component results from clusters of proteins specifically labeled and the slow one from background/image heterogeneity. We show that the calculation of the protein-protein proximity index and the correlation coefficient are more reliably determined by extracting the fast-decaying component.

  12. Computational biology for target discovery and characterization: a feasibility study in protein-protein interaction detection

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, C; Zemla, A

    2009-02-25

    In this work we developed new code for detecting putative multi-domain protein-protein interactions for a small network of bacterial pathogen proteins, and determined how structure-driven domain-fusion (DF) methods should be scaled up for whole-proteome analysis. Protein-protein interactions are of great interest in structural biology and are important for understanding the biology of pathogens. The ability to predict protein-protein interactions provides a means for development of anti-microbials that may interfer with key processes in pathogenicity. The function of a protein-protein complex can be elucidated through knowledge of its structure. The overall goal of this project was to determine the feasibility of extending current LLNL capabilities to produce a high-throughput systems bio-informatics capability for identification and characterization of putative interacting protein partners within known or suspected small protein networks. We extended an existing LLNL methodology for identification of putative protein-protein interacting partners (Chakicherla et al (in review)) by writing a new code to identify multi-domain-fusion linkages (3 or more per complex). We applied these codes to the proteins in the Yersinia pestis quorum sensing network, known as the lsr operon, which comprises a virulence mechanism in this pathogen. We determined that efficient application of our computational algorithms in high-throughput for detection of putative protein-protein complexes genome wide would require pre-computation of PDB domains and construction of a domain-domain association database.

  13. Protein-protein interactions: principles, techniques, and their potential role in new drug development.

    Science.gov (United States)

    Khan, Shagufta H; Ahmad, Faizan; Ahmad, Nihal; Flynn, Daniel C; Kumar, Raj

    2011-06-01

    A vast network of genes is inter-linked through protein-protein interactions and is critical component of almost every biological process under physiological conditions. Any disruption of the biologically essential network leads to pathological conditions resulting into related diseases. Therefore, proper understanding of biological functions warrants a comprehensive knowledge of protein-protein interactions and the molecular mechanisms that govern such processes. The importance of protein-protein interaction process is highlighted by the fact that a number of powerful techniques/methods have been developed to understand how such interactions take place under various physiological and pathological conditions. Many of the key protein-protein interactions are known to participate in disease-associated signaling pathways, and represent novel targets for therapeutic intervention. Thus, controlling protein-protein interactions offers a rich dividend for the discovery of new drug targets. Availability of various tools to study and the knowledge of human genome have put us in a unique position to understand highly complex biological network, and the mechanisms involved therein. In this review article, we have summarized protein-protein interaction networks, techniques/methods of their binding/kinetic parameters, and the role of these interactions in the development of potential tools for drug designing.

  14. Protein-protein interface analysis and hot spots identification for chemical ligand design.

    Science.gov (United States)

    Chen, Jing; Ma, Xiaomin; Yuan, Yaxia; Pei, Jianfeng; Lai, Luhua

    2014-01-01

    Rational design for chemical compounds targeting protein-protein interactions has grown from a dream to reality after a decade of efforts. There are an increasing number of successful examples, though major challenges remain in the field. In this paper, we will first give a brief review of the available methods that can be used to analyze protein-protein interface and predict hot spots for chemical ligand design. New developments of binding sites detection, ligandability and hot spots prediction from the author's group will also be described. Pocket V.3 is an improved program for identifying hot spots in protein-protein interface using only an apo protein structure. It has been developed based on Pocket V.2 that can derive receptor-based pharmacophore model for ligand binding cavity. Given similarities and differences between the essence of pharmacophore and hot spots for guiding design of chemical compounds, not only energetic but also spatial properties of protein-protein interface are used in Pocket V.3 for dealing with protein-protein interface. In order to illustrate the capability of Pocket V.3, two datasets have been used. One is taken from ASEdb and BID having experimental alanine scanning results for testing hot spots prediction. The other is taken from the 2P2I database containing complex structures of protein-ligand binding at the original protein-protein interface for testing hot spots application in ligand design.

  15. A simple dependence between protein evolution rate and the number of protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Hirsh Aaron E

    2003-05-01

    Full Text Available Abstract Background It has been shown for an evolutionarily distant genomic comparison that the number of protein-protein interactions a protein has correlates negatively with their rates of evolution. However, the generality of this observation has recently been challenged. Here we examine the problem using protein-protein interaction data from the yeast Saccharomyces cerevisiae and genome sequences from two other yeast species. Results In contrast to a previous study that used an incomplete set of protein-protein interactions, we observed a highly significant correlation between number of interactions and evolutionary distance to either Candida albicans or Schizosaccharomyces pombe. This study differs from the previous one in that it includes all known protein interactions from S. cerevisiae, and a larger set of protein evolutionary rates. In both evolutionary comparisons, a simple monotonic relationship was found across the entire range of the number of protein-protein interactions. In agreement with our earlier findings, this relationship cannot be explained by the fact that proteins with many interactions tend to be important to yeast. The generality of these correlations in other kingdoms of life unfortunately cannot be addressed at this time, due to the incompleteness of protein-protein interaction data from organisms other than S. cerevisiae. Conclusions Protein-protein interactions tend to slow the rate at which proteins evolve. This may be due to structural constraints that must be met to maintain interactions, but more work is needed to definitively establish the mechanism(s behind the correlations we have observed.

  16. Exploring NMR ensembles of calcium binding proteins: Perspectives to design inhibitors of protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Craescu Constantin T

    2011-05-01

    Full Text Available Abstract Background Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding. Results In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed in silico structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces. Conclusions NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions.

  17. The role of electrostatics in protein-protein interactions of a monoclonal antibody.

    Science.gov (United States)

    Roberts, D; Keeling, R; Tracka, M; van der Walle, C F; Uddin, S; Warwicker, J; Curtis, R

    2014-07-07

    Understanding how protein-protein interactions depend on the choice of buffer, salt, ionic strength, and pH is needed to have better control over protein solution behavior. Here, we have characterized the pH and ionic strength dependence of protein-protein interactions in terms of an interaction parameter kD obtained from dynamic light scattering and the osmotic second virial coefficient B22 measured by static light scattering. A simplified protein-protein interaction model based on a Baxter adhesive potential and an electric double layer force is used to separate out the contributions of longer-ranged electrostatic interactions from short-ranged attractive forces. The ionic strength dependence of protein-protein interactions for solutions at pH 6.5 and below can be accurately captured using a Deryaguin-Landau-Verwey-Overbeek (DLVO) potential to describe the double layer forces. In solutions at pH 9, attractive electrostatics occur over the ionic strength range of 5-275 mM. At intermediate pH values (7.25 to 8.5), there is a crossover effect characterized by a nonmonotonic ionic strength dependence of protein-protein interactions, which can be rationalized by the competing effects of long-ranged repulsive double layer forces at low ionic strength and a shorter ranged electrostatic attraction, which dominates above a critical ionic strength. The change of interactions from repulsive to attractive indicates a concomitant change in the angular dependence of protein-protein interaction from isotropic to anisotropic. In the second part of the paper, we show how the Baxter adhesive potential can be used to predict values of kD from fitting to B22 measurements, thus providing a molecular basis for the linear correlation between the two protein-protein interaction parameters.

  18. Developing algorithms for predicting protein-protein interactions of homology modeled proteins.

    Energy Technology Data Exchange (ETDEWEB)

    Martin, Shawn Bryan; Sale, Kenneth L.; Faulon, Jean-Loup Michel; Roe, Diana C.

    2006-01-01

    The goal of this project was to examine the protein-protein docking problem, especially as it relates to homology-based structures, identify the key bottlenecks in current software tools, and evaluate and prototype new algorithms that may be developed to improve these bottlenecks. This report describes the current challenges in the protein-protein docking problem: correctly predicting the binding site for the protein-protein interaction and correctly placing the sidechains. Two different and complementary approaches are taken that can help with the protein-protein docking problem. The first approach is to predict interaction sites prior to docking, and uses bioinformatics studies of protein-protein interactions to predict theses interaction site. The second approach is to improve validation of predicted complexes after docking, and uses an improved scoring function for evaluating proposed docked poses, incorporating a solvation term. This scoring function demonstrates significant improvement over current state-of-the art functions. Initial studies on both these approaches are promising, and argue for full development of these algorithms.

  19. [The effect of isatin on protein-protein interactions between cytochrome b5 and cytochromes P450].

    Science.gov (United States)

    Ershov, P V; Yablokov, E O; Mezentsev, Yu V; Kalushskiy, L A; Florinskaya, A V; Veselovsky, A V; Gnedenko, O V; Gilep, A A; Usanov, S A; Medvedev, A E; Ivanov, A S

    2017-03-01

    Cytochromes P450 (CYP) are involved in numerous biochemical processes including metabolism of xenobiotics, biosynthesis of cholesterol, steroid hormones etc. Since some CYP catalyze indol oxidation to isatin, we have hypothesized that isatin can regulate protein-protein interactions (PPI) between components of the CYP system thus representing a (negative?) feedback mechanism. The aim of this study was to investigate a possible effect of isatin on interaction of human CYP with cytochrome b5 (CYB5A). Using the optical biosensor test system employing surface plasmon resonance (SPR) we have investigated interaction of immobilized CYB5A with various CYP in the absence and in the presence of isatin. The SPR-based experiments have shown that a high concentration of isatin (270 mM) increases Kd values for complexes CYB5A/CYP3А5 and CYB5A/CYP3A4 (twofold and threefold, respectively), but has no influence on complex formation between CYB5A and other CYP (including indol-metabolizing CYP2C19 and CYP2E1). Isatin injection to the optical biosensor chip with the preformed molecular complex CYB5A/CYP3A4 caused a 30%-increase in its dissociation rate. Molecular docking manipulations have shown that isatin can influence interaction of CYP3А5 or CYP3A4 with CYB5A acting at the contact region of CYB5A/CYP.

  20. Small-Molecule Stabilization of the 14-3-3/Gab2 Protein-Protein Interaction (PPI) Interface.

    Science.gov (United States)

    Bier, David; Bartel, Maria; Sies, Katharina; Halbach, Sebastian; Higuchi, Yusuke; Haranosono, Yu; Brummer, Tilman; Kato, Nobuo; Ottmann, Christian

    2016-04-19

    Small-molecule modulation of protein-protein interactions (PPIs) is one of the most promising new areas in drug discovery. In the vast majority of cases only inhibition or disruption of PPIs is realized, whereas the complementary strategy of targeted stabilization of PPIs is clearly under-represented. Here, we report the example of a semi-synthetic natural product derivative--ISIR-005--that stabilizes the cancer-relevant interaction of the adaptor protein 14-3-3 and Gab2. The crystal structure of ISIR-005 in complex with 14-3-3 and the binding motif of Gab2 comprising two phosphorylation sites (Gab2pS210pT391) showed how the stabilizing molecule binds to the rim-of-the-interface of the protein complex. Only in the direct vicinity of 14-3-3/Gab2pT391 site is a pre-formed pocket occupied by ISIR-005; binding of the Gab2pS210 motif to 14-3-3 does not create an interface pocket suitable for the molecule. Accordingly, ISIR-005 only stabilizes the binding of the Gab2pT391 but not the Gab2pS210 site. This study represents structural and biochemical proof of the druggability of the 14-3-3/Gab2 PPI interface with important implications for the development of PPI stabilizers. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Inhibition of Protein-Protein Interactions and Signaling by Small Molecules

    Science.gov (United States)

    Freire, Ernesto

    2010-03-01

    Protein-protein interactions are at the core of cell signaling pathways as well as many bacterial and viral infection processes. As such, they define critical targets for drug development against diseases such as cancer, arthritis, obesity, AIDS and many others. Until now, the clinical inhibition of protein-protein interactions and signaling has been accomplished with the use of antibodies or soluble versions of receptor molecules. Small molecule replacements of these therapeutic agents have been extremely difficult to develop; either the necessary potency has been hard to achieve or the expected biological effect has not been obtained. In this presentation, we show that a rigorous thermodynamic approach that combines differential scanning calorimetry (DSC) and isothermal titration calorimetry (ITC) provides a unique platform for the identification and optimization of small molecular weight inhibitors of protein-protein interactions. Recent advances in the development of cell entry inhibitors of HIV-1 using this approach will be discussed.

  2. Dynamics Govern Specificity of a Protein-Protein Interface: Substrate Recognition by Thrombin.

    Directory of Open Access Journals (Sweden)

    Julian E Fuchs

    Full Text Available Biomolecular recognition is crucial in cellular signal transduction. Signaling is mediated through molecular interactions at protein-protein interfaces. Still, specificity and promiscuity of protein-protein interfaces cannot be explained using simplistic static binding models. Our study rationalizes specificity of the prototypic protein-protein interface between thrombin and its peptide substrates relying solely on binding site dynamics derived from molecular dynamics simulations. We find conformational selection and thus dynamic contributions to be a key player in biomolecular recognition. Arising entropic contributions complement chemical intuition primarily reflecting enthalpic interaction patterns. The paradigm "dynamics govern specificity" might provide direct guidance for the identification of specific anchor points in biomolecular recognition processes and structure-based drug design.

  3. Dynamics Govern Specificity of a Protein-Protein Interface: Substrate Recognition by Thrombin.

    Science.gov (United States)

    Fuchs, Julian E; Huber, Roland G; Waldner, Birgit J; Kahler, Ursula; von Grafenstein, Susanne; Kramer, Christian; Liedl, Klaus R

    2015-01-01

    Biomolecular recognition is crucial in cellular signal transduction. Signaling is mediated through molecular interactions at protein-protein interfaces. Still, specificity and promiscuity of protein-protein interfaces cannot be explained using simplistic static binding models. Our study rationalizes specificity of the prototypic protein-protein interface between thrombin and its peptide substrates relying solely on binding site dynamics derived from molecular dynamics simulations. We find conformational selection and thus dynamic contributions to be a key player in biomolecular recognition. Arising entropic contributions complement chemical intuition primarily reflecting enthalpic interaction patterns. The paradigm "dynamics govern specificity" might provide direct guidance for the identification of specific anchor points in biomolecular recognition processes and structure-based drug design.

  4. Role of protein-protein interactions in cytochrome P450-mediated drug metabolism and toxicity.

    Science.gov (United States)

    Kandel, Sylvie E; Lampe, Jed N

    2014-09-15

    Through their unique oxidative chemistry, cytochrome P450 monooxygenases (CYPs) catalyze the elimination of most drugs and toxins from the human body. Protein-protein interactions play a critical role in this process. Historically, the study of CYP-protein interactions has focused on their electron transfer partners and allosteric mediators, cytochrome P450 reductase and cytochrome b5. However, CYPs can bind other proteins that also affect CYP function. Some examples include the progesterone receptor membrane component 1, damage resistance protein 1, human and bovine serum albumin, and intestinal fatty acid binding protein, in addition to other CYP isoforms. Furthermore, disruption of these interactions can lead to altered paths of metabolism and the production of toxic metabolites. In this review, we summarize the available evidence for CYP protein-protein interactions from the literature and offer a discussion of the potential impact of future studies aimed at characterizing noncanonical protein-protein interactions with CYP enzymes.

  5. A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions.

    Science.gov (United States)

    Birlutiu, Adriana; d'Alché-Buc, Florence; Heskes, Tom

    2015-01-01

    Computational methods for predicting protein-protein interactions are important tools that can complement high-throughput technologies and guide biologists in designing new laboratory experiments. The proteins and the interactions between them can be described by a network which is characterized by several topological properties. Information about proteins and interactions between them, in combination with knowledge about topological properties of the network, can be used for developing computational methods that can accurately predict unknown protein-protein interactions. This paper presents a supervised learning framework based on Bayesian inference for combining two types of information: i) network topology information, and ii) information related to proteins and the interactions between them. The motivation of our model is that by combining these two types of information one can achieve a better accuracy in predicting protein-protein interactions, than by using models constructed from these two types of information independently.

  6. Essential multimeric enzymes in kinetoplastid parasites: A host of potentially druggable protein-protein interactions.

    Science.gov (United States)

    Wachsmuth, Leah M; Johnson, Meredith G; Gavenonis, Jason

    2017-06-01

    Parasitic diseases caused by kinetoplastid parasites of the genera Trypanosoma and Leishmania are an urgent public health crisis in the developing world. These closely related species possess a number of multimeric enzymes in highly conserved pathways involved in vital functions, such as redox homeostasis and nucleotide synthesis. Computational alanine scanning of these protein-protein interfaces has revealed a host of potentially ligandable sites on several established and emerging anti-parasitic drug targets. Analysis of interfaces with multiple clustered hotspots has suggested several potentially inhibitable protein-protein interactions that may have been overlooked by previous large-scale analyses focusing solely on secondary structure. These protein-protein interactions provide a promising lead for the development of new peptide and macrocycle inhibitors of these enzymes.

  7. A binary logistic regression model for discriminating real protein-protein interface

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    The selection and study of descriptive variables of protein-protein complex interface is a major question that many biologists come across when the research of protein-protein recognition is concerned. Several variables have been proposed to understand the structural or energetic features of complex interfaces. Here a systematic study of some of these "traditional" variables, as well as a few new ones, is introduced. With the values of these variables extracted from 42 PDB samples with real or false complex interfaces, a binary logistic regression analysis is performed, which results in an effective empirical model for the evaluation of binding probabilities of protein-protein interfaces. The model is validated with 12 samples, and satisfactory results are obtained for both the training and validation sets. Meanwhile, three potential dimeric interfaces of staphylokinase have been investigated and one with the best suitability to our model is proposed.

  8. History of protein-protein interactions: from egg-white to complex networks.

    Science.gov (United States)

    Braun, Pascal; Gingras, Anne-Claude

    2012-05-01

    Today, it is widely appreciated that protein-protein interactions play a fundamental role in biological processes. This was not always the case. The study of protein interactions started slowly and evolved considerably, together with conceptual and technological progress in different areas of research through the late 19th and the 20th centuries. In this review, we present some of the key experiments that have introduced major conceptual advances in biochemistry and molecular biology, and review technological breakthroughs that have paved the way for today's systems-wide approaches to protein-protein interaction analysis. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. SwarmDock and the Use of Normal Modes in Protein-Protein Docking

    Directory of Open Access Journals (Sweden)

    Paul A. Bates

    2010-09-01

    Full Text Available Here is presented an investigation of the use of normal modes in protein-protein docking, both in theory and in practice. Upper limits of the ability of normal modes to capture the unbound to bound conformational change are calculated on a large test set, with particular focus on the binding interface, the subset of residues from which the binding energy is calculated. Further, the SwarmDock algorithm is presented, to demonstrate that the modelling of conformational change as a linear combination of normal modes is an effective method of modelling flexibility in protein-protein docking.

  10. A modified resonant recognition model to predict protein-protein interaction

    Institute of Scientific and Technical Information of China (English)

    LIU Xiang; WANG Yifei

    2007-01-01

    Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement.This paper briefly introduced the original Resonant Recognition Model (RRM),and then modified it by using the wavelet transform to acquire the Modified Resonant Recognition Model (MRRM).The key characteristic of the new model is that it can predict directly the proteinprotein interaction from the primary sequence,and the MRRM is more suitable than the RRM for this prediction.The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction.

  11. A holistic molecular docking approach for predicting protein-protein complex structure

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    A holistic protein-protein molecular docking approach,HoDock,was established,composed of such steps as binding site prediction,initial complex structure sampling,refined complex structure sampling,structure clustering,scoring and final structure selection.This article explains the detailed steps and applications for CAPRI Target 39.The CAPRI result showed that three predicted binding site residues,A191HIS,B512ARG and B531ARG,were correct,and there were five submitted structures with a high fraction of correct receptor-ligand interface residues,indicating that this docking approach may improve prediction accuracy for protein-protein complex structures.

  12. Quantitative Determination of Spatial Protein-protein Proximity in Fluorescence Confocal Microscopy

    CERN Document Server

    Wu, Yong; Ou, Jimmy; Li, Min; Toro, Ligia; Stefani, Enrico

    2009-01-01

    To quantify spatial protein-protein proximity (colocalization) in fluorescence microscopic images, cross-correlation and autocorrelation functions were decomposed into fast and slowly decaying components. The fast component results from clusters of proteins specifically labeled and the slow one from background/image heterogeneity. We show that the calculation of the protein-protein proximity index and the correlation coefficient are more reliably determined by extracting the fast-decaying component. This new method is illustrated by analyzing colocalization in both simulated and biological images.

  13. Manipulating Fatty Acid Biosynthesis in Microalgae for Biofuel through Protein-Protein Interactions

    Science.gov (United States)

    Blatti, Jillian L.; Beld, Joris; Behnke, Craig A.; Mendez, Michael; Mayfield, Stephen P.; Burkart, Michael D.

    2012-01-01

    Microalgae are a promising feedstock for renewable fuels, and algal metabolic engineering can lead to crop improvement, thus accelerating the development of commercially viable biodiesel production from algae biomass. We demonstrate that protein-protein interactions between the fatty acid acyl carrier protein (ACP) and thioesterase (TE) govern fatty acid hydrolysis within the algal chloroplast. Using green microalga Chlamydomonas reinhardtii (Cr) as a model, a structural simulation of docking CrACP to CrTE identifies a protein-protein recognition surface between the two domains. A virtual screen reveals plant TEs with similar in silico binding to CrACP. Employing an activity-based crosslinking probe designed to selectively trap transient protein-protein interactions between the TE and ACP, we demonstrate in vitro that CrTE must functionally interact with CrACP to release fatty acids, while TEs of vascular plants show no mechanistic crosslinking to CrACP. This is recapitulated in vivo, where overproduction of the endogenous CrTE increased levels of short-chain fatty acids and engineering plant TEs into the C. reinhardtii chloroplast did not alter the fatty acid profile. These findings highlight the critical role of protein-protein interactions in manipulating fatty acid biosynthesis for algae biofuel engineering as illuminated by activity-based probes. PMID:23028438

  14. Manipulating fatty acid biosynthesis in microalgae for biofuel through protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Jillian L Blatti

    Full Text Available Microalgae are a promising feedstock for renewable fuels, and algal metabolic engineering can lead to crop improvement, thus accelerating the development of commercially viable biodiesel production from algae biomass. We demonstrate that protein-protein interactions between the fatty acid acyl carrier protein (ACP and thioesterase (TE govern fatty acid hydrolysis within the algal chloroplast. Using green microalga Chlamydomonas reinhardtii (Cr as a model, a structural simulation of docking CrACP to CrTE identifies a protein-protein recognition surface between the two domains. A virtual screen reveals plant TEs with similar in silico binding to CrACP. Employing an activity-based crosslinking probe designed to selectively trap transient protein-protein interactions between the TE and ACP, we demonstrate in vitro that CrTE must functionally interact with CrACP to release fatty acids, while TEs of vascular plants show no mechanistic crosslinking to CrACP. This is recapitulated in vivo, where overproduction of the endogenous CrTE increased levels of short-chain fatty acids and engineering plant TEs into the C. reinhardtii chloroplast did not alter the fatty acid profile. These findings highlight the critical role of protein-protein interactions in manipulating fatty acid biosynthesis for algae biofuel engineering as illuminated by activity-based probes.

  15. A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling

    DEFF Research Database (Denmark)

    Blagoev, B.; Kratchmarova, I.; Ong, S.E.

    2003-01-01

    Mass spectrometry-based proteomics can reveal protein-protein interactions on a large scale, but it has been difficult to separate background binding from functionally important interactions and still preserve weak binders. To investigate the epidermal growth factor receptor (EGFR) pathway, we...

  16. The effect of protein-protein and protein-membrane interactions on membrane fouling in ultrafiltration

    NARCIS (Netherlands)

    Huisman, I.H.; Prádanos, P.; Hernández, A.

    2000-01-01

    It was studied how protein-protein and protein-membrane interactions influence the filtration performance during the ultrafiltration of protein solutions over polymeric membranes. This was done by measuring flux, streaming potential, and protein transmission during filtration of bovine serum albumin

  17. Identifying functional modules in protein-protein interaction networks: An integrated exact approach

    NARCIS (Netherlands)

    Dittrich, M.; Klau, G.W.; Rosenwald, A.; Dandekar, T.; et al, not CWI

    2008-01-01

    Motivation: With the exponential growth of expression and protein-protein interaction (PPI) data, the frontier of research in system biology shifts more and more to the integrated analysis of these large datasets. Of particular interest is the identification of functional modules in PPI networks, sh

  18. Analysis of MADS box protein-protein interactions in living plant cells

    NARCIS (Netherlands)

    Immink, R.G.H.; Gadella, T.W.J.; Ferrario, S.I.T.; Busscher, M.; Angenent, G.C.

    2002-01-01

    Over the last decade, the yeast two-hybrid system has become the tool to use for the identification of protein-protein interactions and recently, even complete interactomes were elucidated by this method. Nevertheless, it is an artificial system that is sensitive to errors resulting in the identific

  19. A fluorescent probe for imaging p53-MDM2 protein-protein interaction.

    Science.gov (United States)

    Liu, Zhenzhen; Miao, Zhenyuan; Li, Jin; Fang, Kun; Zhuang, Chunlin; Du, Lupei; Sheng, Chunquan; Li, Minyong

    2015-04-01

    In this article, we describe a no-wash small-molecule fluorescent probe for detecting and imaging p53-MDM2 protein-protein interaction based on an environment-sensitive fluorescent turn-on mechanism. After extensive biological examination, this probe L1 exhibited practical activity and selectivity in vitro and in cellulo.

  20. Analysis of correlations between protein complex and protein-protein interaction and mRNA expression

    Institute of Scientific and Technical Information of China (English)

    CAI Lun; XUE Hong; LU Hongchao; ZHAO Yi; ZHU Xiaopeng; BU Dongbo; LING Lunjiang; CHEN Runsheng

    2003-01-01

    Protein-protein interaction is a physical interaction of two proteins in living cells. In budding yeast Saccharomyces cerevisiae, large-scale protein-protein interaction data have been obtained through high-throughput yeast two-hybrid systems (Y2H) and protein complex purification techniques based on mass-spectrometry. Here, we collect 11855 interactions between total 2617 proteins. Through seriate genome-wide mRNA expression data, similarity between two genes could be measured. Protein complex data can also be obtained publicly and can be translated to pair relationship that any two proteins can only exist in the same complex or not. Analysis of protein complex data, protein-protein interaction data and mRNA expression data can elucidate correlations between them. The results show that proteins that have interactions or similar expression patterns have a higher possibility to be in the same protein complex than randomized selected proteins, and proteins which have interactions and similar expression patterns are even more possible to exist in the same protein complex. The work indicates that comprehensive integration and analysis of public large-scale bioinformatical data, such as protein complex data, protein-protein interaction data and mRNA expression data, may help to uncover their relationships and common biological information underlying these data. The strategies described here may help to integrate and analyze other functional genomic and proteomic data, such as gene expression profiling, protein-localization mapping and large-scale phenotypic data, both in yeast and in other organisms.

  1. Categorizing Biases in High-Confidence High-Throughput Protein-Protein Interaction Data Sets

    Science.gov (United States)

    2011-01-01

    Interactions Functional Diversity in Protein Interaction Data Sets—Al- though genomic-scale protein-protein interaction detection campaigns are by design...mapped out in Fig. 2 show that the different data sets covered distinct parts of the interaction space, with some FIG. 1. Functional diversity among

  2. Enabling large-scale design, synthesis and validation of small molecule protein-protein antagonists

    NARCIS (Netherlands)

    Koes, David; Khoury, Kareem; Huang, Yijun; Wang, Wei; Bista, Michal; Popowicz, Grzegorz M; Wolf, Siglinde; Holak, Tad A; Dömling, Alexander; Camacho, Carlos J

    2012-01-01

    Although there is no shortage of potential drug targets, there are only a handful known low-molecular-weight inhibitors of protein-protein interactions (PPIs). One problem is that current efforts are dominated by low-yield high-throughput screening, whose rigid framework is not suitable for the dive

  3. Mining minimal motif pair sets maximally covering interactions in a protein-protein interaction network

    NARCIS (Netherlands)

    Boyen, P.; Neven, F.; Valentim, F.L.; Dijk, van A.D.J.

    2013-01-01

    Correlated motif covering (CMC) is the problem of finding a set of motif pairs, i.e., pairs of patterns, in the sequences of proteins from a protein-protein interaction network (PPI-network) that describe the interactions in the network as concisely as possible. In other words, a perfect solution fo

  4. The effect of protein-protein and protein-membrane interactions on membrane fouling in ultrafiltration

    NARCIS (Netherlands)

    Huisman, I.H.; Prádanos, P.; Hernández, A.

    2000-01-01

    It was studied how protein-protein and protein-membrane interactions influence the filtration performance during the ultrafiltration of protein solutions over polymeric membranes. This was done by measuring flux, streaming potential, and protein transmission during filtration of bovine serum albumin

  5. Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.

    Science.gov (United States)

    Zhang, Shu-Bo; Tang, Qiang-Rong

    2016-07-21

    Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein-protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein-protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein-protein interaction.

  6. Quantifying protein-protein interactions in the ubiquitin pathway by surface plasmon resonance

    DEFF Research Database (Denmark)

    Hartmann-Petersen, Rasmus; Gordon, Colin

    2005-01-01

    The commercial availability of instruments, such as Biacore, that are capable of monitoring surface plasmon resonance (SPR) has greatly simplified the quantification of protein-protein interactions. Already, this technique has been used for some studies of the ubiquitin-proteasome system. Here we...

  7. In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

    DEFF Research Database (Denmark)

    Folador, Edson Luiz; de Carvalho, Paulo Vinícius Sanches Daltro; Silva, Wanderson Marques;

    2016-01-01

    and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. RESULTS: Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts...

  8. Coevolution study of mitochondria respiratory chain proteins:Toward the understanding of protein-protein interaction

    Institute of Scientific and Technical Information of China (English)

    Ming Yang; Yan Ge; Jiayan Wu; Jingfa Xiao; Jun Yu

    2011-01-01

    Coevolution can be seen as the interdependency between evolutionary histories. In the context of protein evolution, functional correlation proteins are ever-present coordinated evolutionary characters without disruption of organismal integrity. As to complex system, there are two forms of protein-protein interactions in vivo, which refer to inter-complex interaction and intra-complex interaction. In this paper, we studied the difference of coevolution characters between inter-complex interaction and intra-complex interaction using "Mirror tree" method on the respiratory chain (RC) proteins. We divided the correlation coefficients of every pairwise RC proteins into two groups corresponding to the binary protein-protein interaction in intra-complex and the binary protein-protein interaction in inter-complex, respectively. A dramatical discrepancy is detected between the coevolution characters of the two sets of protein interactions (Wilcoxon test, p-value = 4.4 x 10-6). Our finding reveals some critical information on coevolutionary study and assists the mechanical investigation of protein-protein interaction.Furthermore, the results also provide some unique clue for supramolecular organization of protein complexes in the mitochondrial inner membrane. More detailed binding sites map and genome information of nuclear encoded RC proteins will be extraordinary valuable for the further mitochondria dynamics study.

  9. Enabling large-scale design, synthesis and validation of small molecule protein-protein antagonists

    NARCIS (Netherlands)

    Koes, David; Khoury, Kareem; Huang, Yijun; Wang, Wei; Bista, Michal; Popowicz, Grzegorz M; Wolf, Siglinde; Holak, Tad A; Dömling, Alexander; Camacho, Carlos J

    2012-01-01

    Although there is no shortage of potential drug targets, there are only a handful known low-molecular-weight inhibitors of protein-protein interactions (PPIs). One problem is that current efforts are dominated by low-yield high-throughput screening, whose rigid framework is not suitable for the dive

  10. Biochemical composition of copepods for evaluation of feed quality in production of juvenile marine fish

    OpenAIRE

    van der Meeren, Terje; Olsen, Rolf Erik; Hamre, Kristin; Fyhn, Hans Jørgen

    2007-01-01

    To increase current knowledge on the nutritional value of natural prey organisms, the biochemical components of mainly three copepods (Acartia grani, Centropages hamatus, and Eurytemora affinis) from a marine pond system were analysed once a week from spring until late fall, over two years. The analysed components were total lipid, lipid class composition, total lipid fatty acid composition, free amino acids, total protein, protein-bound amino acids, pigment (astaxanthin and ß-carotene), and ...

  11. Binding of small molecules at interface of protein-protein complex - A newer approach to rational drug design.

    Science.gov (United States)

    Gurung, A B; Bhattacharjee, A; Ajmal Ali, M; Al-Hemaid, F; Lee, Joongku

    2017-02-01

    Protein-protein interaction is a vital process which drives many important physiological processes in the cell and has also been implicated in several diseases. Though the protein-protein interaction network is quite complex but understanding its interacting partners using both in silico as well as molecular biology techniques can provide better insights for targeting such interactions. Targeting protein-protein interaction with small molecules is a challenging task because of druggability issues. Nevertheless, several studies on the kinetics as well as thermodynamic properties of protein-protein interactions have immensely contributed toward better understanding of the affinity of these complexes. But, more recent studies on hot spots and interface residues have opened up new avenues in the drug discovery process. This approach has been used in the design of hot spot based modulators targeting protein-protein interaction with the objective of normalizing such interactions.

  12. Specific ion and buffer effects on protein-protein interactions of a monoclonal antibody.

    Science.gov (United States)

    Roberts, D; Keeling, R; Tracka, M; van der Walle, C F; Uddin, S; Warwicker, J; Curtis, R

    2015-01-05

    Better predictive ability of salt and buffer effects on protein-protein interactions requires separating out contributions due to ionic screening, protein charge neutralization by ion binding, and salting-in(out) behavior. We have carried out a systematic study by measuring protein-protein interactions for a monoclonal antibody over an ionic strength range of 25 to 525 mM at 4 pH values (5, 6.5, 8, and 9) in solutions containing sodium chloride, calcium chloride, sodium sulfate, or sodium thiocyante. The salt ions are chosen so as to represent a range of affinities for protein charged and noncharged groups. The results are compared to effects of various buffers including acetate, citrate, phosphate, histidine, succinate, or tris. In low ionic strength solutions, anion binding affinity is reflected by the ability to reduce protein-protein repulsion, which follows the order thiocyanate > sulfate > chloride. The sulfate specific effect is screened at the same ionic strength required to screen the pH dependence of protein-protein interactions indicating sulfate binding only neutralizes protein charged groups. Thiocyanate specific effects occur over a larger ionic strength range reflecting adsorption to charged and noncharged regions of the protein. The latter leads to salting-in behavior and, at low pH, a nonmonotonic interaction profile with respect to sodium thiocyanate concentration. The effects of thiocyanate can not be rationalized in terms of only neutralizing double layer forces indicating the presence of an additional short-ranged protein-protein attraction at moderate ionic strength. Conversely, buffer specific effects can be explained through a charge neutralization mechanism, where buffers with greater valency are more effective at reducing double layer forces at low pH. Citrate binding at pH 6.5 leads to protein charge inversion and the formation of attractive electrostatic interactions. Throughout the report, we highlight similarities in the measured

  13. Biochemical Engineering Fundamentals

    Science.gov (United States)

    Bailey, J. E.; Ollis, D. F.

    1976-01-01

    Discusses a biochemical engineering course that is offered as part of a chemical engineering curriculum and includes topics that influence the behavior of man-made or natural microbial or enzyme reactors. (MLH)

  14. A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling

    DEFF Research Database (Denmark)

    Blagoev, B.; Kratchmarova, I.; Ong, S.E.

    2003-01-01

    Mass spectrometry-based proteomics can reveal protein-protein interactions on a large scale, but it has been difficult to separate background binding from functionally important interactions and still preserve weak binders. To investigate the epidermal growth factor receptor (EGFR) pathway, we...... and Src homologous and collagen (Shc) protein. We identified 228 proteins, of which 28 were selectively enriched upon stimulation. EGFR and Shc, which interact directly with the bait, had large differential ratios. Many signaling molecules specifically formed complexes with the activated EGFR-Shc, as did...... plectin, epiplakin, cytokeratin networks, histone H3, the glycosylphosphatidylinositol (GPI)-anchored molecule CD59, and two novel proteins. SILAC combined with modification-based affinity purification is a useful approach to detect specific and functional protein-protein interactions....

  15. Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information

    Directory of Open Access Journals (Sweden)

    Kershenbaum Aaron

    2005-06-01

    Full Text Available Abstract Background The rapid publication of important research in the biomedical literature makes it increasingly difficult for researchers to keep current with significant work in their area of interest. Results This paper reports a scalable method for the discovery of protein-protein interactions in Medline abstracts, using a combination of text analytics, statistical and graphical analysis, and a set of easily implemented rules. Applying these techniques to 12,300 abstracts, a precision of 0.61 and a recall of 0.97 were obtained, (f = 0.74 and when allowing for two-hop and three-hop relations discovered by graphical analysis, the precision was 0.74 (f = 0.83. Conclusion This combination of linguistic and statistical approaches appears to provide the highest precision and recall thus far reported in detecting protein-protein relations using text analytic approaches.

  16. Predicting disease-related proteins based on clique backbone in protein-protein interaction network.

    Science.gov (United States)

    Yang, Lei; Zhao, Xudong; Tang, Xianglong

    2014-01-01

    Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

  17. CPL:Detecting Protein Complexes by Propagating Labels on Protein-Protein Interaction Network

    Institute of Scientific and Technical Information of China (English)

    代启国; 郭茂祖; 刘晓燕; 滕志霞; 王春宇

    2014-01-01

    Proteins usually bind together to form complexes, which play an important role in cellular activities. Many graph clustering methods have been proposed to identify protein complexes by finding dense regions in protein-protein interaction networks. We present a novel framework (CPL) that detects protein complexes by propagating labels through interactions in a network, in which labels denote complex identifiers. With proper propagation in CPL, proteins in the same complex will be assigned with the same labels. CPL does not make any strong assumptions about the topological structures of the complexes, as in previous methods. The CPL algorithm is tested on several publicly available yeast protein-protein interaction networks and compared with several state-of-the-art methods. The results suggest that CPL performs better than the existing methods. An analysis of the functional homogeneity based on a gene ontology analysis shows that the detected complexes of CPL are highly biologically relevant.

  18. Composition of Overlapping Protein-Protein and Protein-Ligand Interfaces.

    Directory of Open Access Journals (Sweden)

    Ruzianisra Mohamed

    Full Text Available Protein-protein interactions (PPIs play a major role in many biological processes and they represent an important class of targets for therapeutic intervention. However, targeting PPIs is challenging because often no convenient natural substrates are available as starting point for small-molecule design. Here, we explored the characteristics of protein interfaces in five non-redundant datasets of 174 protein-protein (PP complexes, and 161 protein-ligand (PL complexes from the ABC database, 436 PP complexes, and 196 PL complexes from the PIBASE database and a dataset of 89 PL complexes from the Timbal database. In all cases, the small molecule ligands must bind at the respective PP interface. We observed similar amino acid frequencies in all three datasets. Remarkably, also the characteristics of PP contacts and overlapping PL contacts are highly similar.

  19. Pin-Align: a new dynamic programming approach to align protein-protein interaction networks.

    Science.gov (United States)

    Amir-Ghiasvand, Farid; Nowzari-Dalini, Abbas; Momenzadeh, Vida

    2014-01-01

    To date, few tools for aligning protein-protein interaction networks have been suggested. These tools typically find conserved interaction patterns using various local or global alignment algorithms. However, the improvement of the speed, scalability, simplification, and accuracy of network alignment tools is still the target of new researches. In this paper, we introduce Pin-Align, a new tool for local alignment of protein-protein interaction networks. Pin-Align accuracy is tested on protein interaction networks from IntAct, DIP, and the Stanford Network Database and the results are compared with other well-known algorithms. It is shown that Pin-Align has higher sensitivity and specificity in terms of KEGG Ortholog groups.

  20. Pin-Align: A New Dynamic Programming Approach to Align Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Farid Amir-Ghiasvand

    2014-01-01

    Full Text Available To date, few tools for aligning protein-protein interaction networks have been suggested. These tools typically find conserved interaction patterns using various local or global alignment algorithms. However, the improvement of the speed, scalability, simplification, and accuracy of network alignment tools is still the target of new researches. In this paper, we introduce Pin-Align, a new tool for local alignment of protein-protein interaction networks. Pin-Align accuracy is tested on protein interaction networks from IntAct, DIP, and the Stanford Network Database and the results are compared with other well-known algorithms. It is shown that Pin-Align has higher sensitivity and specificity in terms of KEGG Ortholog groups.

  1. Multifunctionality of the linker histones: an emerging role for protein-protein interactions.

    Science.gov (United States)

    McBryant, Steven J; Lu, Xu; Hansen, Jeffrey C

    2010-05-01

    Linker histones, e.g., H1, are best known for their ability to bind to nucleosomes and stabilize both nucleosome structure and condensed higher-order chromatin structures. However, over the years many investigators have reported specific interactions between linker histones and proteins involved in important cellular processes. The purpose of this review is to highlight evidence indicating an important alternative mode of action for H1, namely protein-protein interactions. We first review key aspects of the traditional view of linker histone action, including the importance of the H1 C-terminal domain. We then discuss the current state of knowledge of linker histone interactions with other proteins, and, where possible, highlight the mechanism of linker histone-mediated protein-protein interactions. Taken together, the data suggest a combinatorial role for the linker histones, functioning both as primary chromatin architectural proteins and simultaneously as recruitment hubs for proteins involved in accessing and modifying the chromatin fiber.

  2. Using the clustered circular layout as an informative method for visualizing protein-protein interaction networks.

    Science.gov (United States)

    Fung, David C Y; Wilkins, Marc R; Hart, David; Hong, Seok-Hee

    2010-07-01

    The force-directed layout is commonly used in computer-generated visualizations of protein-protein interaction networks. While it is good for providing a visual outline of the protein complexes and their interactions, it has two limitations when used as a visual analysis method. The first is poor reproducibility. Repeated running of the algorithm does not necessarily generate the same layout, therefore, demanding cognitive readaptation on the investigator's part. The second limitation is that it does not explicitly display complementary biological information, e.g. Gene Ontology, other than the protein names or gene symbols. Here, we present an alternative layout called the clustered circular layout. Using the human DNA replication protein-protein interaction network as a case study, we compared the two network layouts for their merits and limitations in supporting visual analysis.

  3. A new scoring function for protein-protein docking that identifies native structures with unprecedented accuracy

    DEFF Research Database (Denmark)

    Moreira, Irina S.; da Silva Martins, João Miguel; Coimbra, João T.S.

    2015-01-01

    Protein-protein (P-P) 3D structures are fundamental to structural biology and drug discovery. However, most of them have never been determined. Many docking algorithms were developed for that purpose, but they have a very limited accuracy in generating native-like structures and identifying...... the most correct one, in particular when a single answer is asked for. With such a low success rate it is difficult to point out one docked structure as being native-like. Here we present a new, high accuracy, scoring method to identify the 3D structure of P-P complexes among a set of trial poses...... the trial structures and identifies the native-like structures with unprecedented accuracy (∼94%), providing the correct P-P 3D structures that biochemists and molecular biologists need to pursue their studies. With such a success rate, the bottleneck of protein-protein docking moves from the scoring...

  4. Analysis of signaling networks distributed over intracellular compartments based on protein-protein interactions

    OpenAIRE

    2014-01-01

    BackgroundBiological processes are usually distributed over various intracellular compartments. Proteins from diverse cellular compartments are often involved in similar signaling networks. However, the difference in the reaction rates between similar proteins among different compartments is usually quite high. We suggest that the estimation of frequency of intracompartmental as well as intercompartmental protein-protein interactions is an appropriate approach to predict the efficiency of a p...

  5. A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction

    OpenAIRE

    2016-01-01

    The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on sta...

  6. A Novel Approach for Protein-Named Entity Recognition and Protein-Protein Interaction Extraction

    OpenAIRE

    Meijing Li; Tsendsuren Munkhdalai; Xiuming Yu; Keun Ho Ryu

    2015-01-01

    Many researchers focus on developing protein-named entity recognition (Protein-NER) or PPI extraction systems. However, the studies about these two topics cannot be merged well; then existing PPI extraction systems’ Protein-NER still needs to improve. In this paper, we developed the protein-protein interaction extraction system named PPIMiner based on Support Vector Machine (SVM) and parsing tree. PPIMiner consists of three main models: natural language processing (NLP) model, Protein-NER mod...

  7. Cross-species Virus-host Protein-Protein Interactions Inhibiting Innate Immunity

    Science.gov (United States)

    2016-07-01

    SUBJECTTERMS viral pathogen, zoonotic, arenavirus, host tropism, protein - protein interactions, RIG-I, Z protein , CARD domain, MAVS 16. SECURITY...individually subcloned into Checkmate M2H System (Promega) bait and prey reporter plasmids. The genes encoding the viral Z proteins were synthesized... viral proteins were calculated with PhyML. While several residue positions are highly conserved across Z proteins (Figure 8), significant sequence

  8. Filtering high-throughput protein-protein interaction data using a combination of genomic features

    Directory of Open Access Journals (Sweden)

    Patil Ashwini

    2005-04-01

    Full Text Available Abstract Background Protein-protein interaction data used in the creation or prediction of molecular networks is usually obtained from large scale or high-throughput experiments. This experimental data is liable to contain a large number of spurious interactions. Hence, there is a need to validate the interactions and filter out the incorrect data before using them in prediction studies. Results In this study, we use a combination of 3 genomic features – structurally known interacting Pfam domains, Gene Ontology annotations and sequence homology – as a means to assign reliability to the protein-protein interactions in Saccharomyces cerevisiae determined by high-throughput experiments. Using Bayesian network approaches, we show that protein-protein interactions from high-throughput data supported by one or more genomic features have a higher likelihood ratio and hence are more likely to be real interactions. Our method has a high sensitivity (90% and good specificity (63%. We show that 56% of the interactions from high-throughput experiments in Saccharomyces cerevisiae have high reliability. We use the method to estimate the number of true interactions in the high-throughput protein-protein interaction data sets in Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens to be 27%, 18% and 68% respectively. Our results are available for searching and downloading at http://helix.protein.osaka-u.ac.jp/htp/. Conclusion A combination of genomic features that include sequence, structure and annotation information is a good predictor of true interactions in large and noisy high-throughput data sets. The method has a very high sensitivity and good specificity and can be used to assign a likelihood ratio, corresponding to the reliability, to each interaction.

  9. α/β-Peptide Foldamers Targeting Intracellular Protein-Protein Interactions with Activity in Living Cells.

    Science.gov (United States)

    Checco, James W; Lee, Erinna F; Evangelista, Marco; Sleebs, Nerida J; Rogers, Kelly; Pettikiriarachchi, Anne; Kershaw, Nadia J; Eddinger, Geoffrey A; Belair, David G; Wilson, Julia L; Eller, Chelcie H; Raines, Ronald T; Murphy, William L; Smith, Brian J; Gellman, Samuel H; Fairlie, W Douglas

    2015-09-09

    Peptides can be developed as effective antagonists of protein-protein interactions, but conventional peptides (i.e., oligomers of l-α-amino acids) suffer from significant limitations in vivo. Short half-lives due to rapid proteolytic degradation and an inability to cross cell membranes often preclude biological applications of peptides. Oligomers that contain both α- and β-amino acid residues ("α/β-peptides") manifest decreased susceptibility to proteolytic degradation, and when properly designed these unnatural oligomers can mimic the protein-recognition properties of analogous "α-peptides". This report documents an extension of the α/β-peptide approach to target intracellular protein-protein interactions. Specifically, we have generated α/β-peptides based on a "stapled" Bim BH3 α-peptide, which contains a hydrocarbon cross-link to enhance α-helix stability. We show that a stapled α/β-peptide can structurally and functionally mimic the parent stapled α-peptide in its ability to enter certain types of cells and block protein-protein interactions associated with apoptotic signaling. However, the α/β-peptide is nearly 100-fold more resistant to proteolysis than is the parent stapled α-peptide. These results show that backbone modification, a strategy that has received relatively little attention in terms of peptide engineering for biomedical applications, can be combined with more commonly deployed peripheral modifications such as side chain cross-linking to produce synergistic benefits.

  10. A gateway-based system for fast evaluation of protein-protein interactions in bacteria.

    Directory of Open Access Journals (Sweden)

    Thorsten Wille

    Full Text Available Protein-protein interactions are important layers of regulation in all kingdoms of life. Identification and characterization of these interactions is one challenging task of the post-genomic era and crucial for understanding of molecular processes within a cell. Several methods have been successfully employed during the past decades to identify protein-protein interactions in bacteria, but most of them include tedious and time-consuming manipulations of DNA. In contrast, the MultiSite Gateway system is a fast tool for transfer of multiple DNA fragments between plasmids enabling simultaneous and site directed cloning of up to four fragments into one construct. Here we developed a new set of Gateway vectors including custom made entry vectors and modular Destination vectors for studying protein-protein interactions via Fluorescence Resonance Energy Transfer (FRET, Bacterial two Hybrid (B2H and split Gaussia luciferase (Gluc, as well as for fusions with SNAP-tag and HaloTag for dual-color super-resolution microscopy. As proof of principle, we characterized the interaction between the Salmonella effector SipA and its chaperone InvB via split Gluc and B2H approach. The suitability for FRET analysis as well as functionality of fusions with SNAP- and HaloTag could be demonstrated by studying the transient interaction between chemotaxis response regulator CheY and its phosphatase CheZ.

  11. Improving accuracy of protein-protein interaction prediction by considering the converse problem for sequence representation

    Directory of Open Access Journals (Sweden)

    Wang Yong

    2011-10-01

    Full Text Available Abstract Background With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched. Results In this study, we interrogate this problem theoretically by an optimization scheme. Motivated by the theoretical investigation, we find novel encoding methods for both protein sequences and protein pairs. Our new methods exploit sufficiently the information of protein sequences and reduce artificial bias and computational cost. Thus, it significantly outperforms the available methods regarding sensitivity, specificity, precision, and recall with cross-validation evaluation and reaches ~80% and ~90% accuracy in Escherichia coli and Saccharomyces cerevisiae respectively. Our findings here hold important implication for other sequence-based prediction tasks because representation of biological sequence is always the first step in computational biology. Conclusions By considering the converse problem, we propose new representation methods for both protein sequences and protein pairs. The results show that our method significantly improves the accuracy of protein-protein interaction predictions.

  12. PathFinder: mining signal transduction pathway segments from protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Yang Jiong

    2007-09-01

    Full Text Available Abstract Background A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem. Results In this study we present a new framework for identifying signaling pathways in protein-protein interaction networks. Our goal is to find biologically significant pathway segments in a given interaction network. Currently, protein-protein interaction data has excessive amount of noise, e.g., false positive and false negative interactions. First, we eliminate false positives in the protein-protein interaction network by integrating the network with microarray expression profiles, protein subcellular localization and sequence information. In addition, protein families are used to repair false negative interactions. Then the characteristics of known signal transduction pathways and their functional annotations are extracted in the form of association rules. Conclusion Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives. In our study, S. cerevisiae (yeast data is used to demonstrate the effectiveness of our method.

  13. Topological Properties of Protein-Protein and Metabolic Interaction Networks of Drosophila melanogaster

    Institute of Scientific and Technical Information of China (English)

    Thanigaimani Rajarathinam; Yen-Han Lin

    2006-01-01

    The underlying principle governing the natural phenomena of life is one of the critical issues receiving due importance in recent years. A key feature of the scale-free architecture is the vitality of the most connected nodes (hubs). The major objective of this article was to analyze the protein-protein and metabolic interaction networks of Drosophila melanogaster by considering the architectural patterns and the consequence of removal of hubs on the topological parameter of the two interaction systems. Analysis showed that both interaction networks follow a scale-free model, establishing the fact that most real world networks,from varied situations, conform to the small world pattern. The average path length showed a two-fold and a three-fold increase (changing from 9.42 to 20.93 and from 5.29 to 17.75, respectively) for the protein-protein and metabolic interaction networks, respectively, due to the deletion of hubs. On the contrary, the arbitrary elimination of nodes did not show any remarkable disparity in the topological parameter of the protein-protein and metabolic interaction networks (average path length: 9.42±0.02 and 5.27±0.01, respectively). This aberrant behavior for the two cases underscores the significance of the most linked nodes to the natural topology of the networks.

  14. Homology inference of protein-protein interactions via conserved binding sites.

    Directory of Open Access Journals (Sweden)

    Manoj Tyagi

    Full Text Available The coverage and reliability of protein-protein interactions determined by high-throughput experiments still needs to be improved, especially for higher organisms, therefore the question persists, how interactions can be verified and predicted by computational approaches using available data on protein structural complexes. Recently we developed an approach called IBIS (Inferred Biomolecular Interaction Server to predict and annotate protein-protein binding sites and interaction partners, which is based on the assumption that the structural location and sequence patterns of protein-protein binding sites are conserved between close homologs. In this study first we confirmed high accuracy of our method and found that its accuracy depends critically on the usage of all available data on structures of homologous complexes, compared to the approaches where only a non-redundant set of complexes is employed. Second we showed that there exists a trade-off between specificity and sensitivity if we employ in the prediction only evolutionarily conserved binding site clusters or clusters supported by only one observation (singletons. Finally we addressed the question of identifying the biologically relevant interactions using the homology inference approach and demonstrated that a large majority of crystal packing interactions can be correctly identified and filtered by our algorithm. At the same time, about half of biological interfaces that are not present in the protein crystallographic asymmetric unit can be reconstructed by IBIS from homologous complexes without the prior knowledge of crystal parameters of the query protein.

  15. Coverage of protein domain families with structural protein-protein interactions: current progress and future trends.

    Science.gov (United States)

    Goncearenco, Alexander; Shoemaker, Benjamin A; Zhang, Dachuan; Sarychev, Alexey; Panchenko, Anna R

    2014-01-01

    Protein interactions have evolved into highly precise and regulated networks adding an immense layer of complexity to cellular systems. The most accurate atomistic description of protein binding sites can be obtained directly from structures of protein complexes. The availability of structurally characterized protein interfaces significantly improves our understanding of interactomes, and the progress in structural characterization of protein-protein interactions (PPIs) can be measured by calculating the structural coverage of protein domain families. We analyze the coverage of protein domain families (defined according to CDD and Pfam databases) by structures, structural protein-protein complexes and unique protein binding sites. Structural PPI coverage of currently available protein families is about 30% without any signs of saturation in coverage growth dynamics. Given the current growth rates of domain databases and structural PPI deposition, complete domain coverage with PPIs is not expected in the near future. As a result of this study we identify families without any protein-protein interaction evidence (listed on a supporting website http://www.ncbi.nlm.nih.gov/Structure/ibis/coverage/) and propose them as potential targets for structural studies with a focus on protein interactions. Published by Elsevier Ltd.

  16. An evaluation of in vitro protein-protein interaction techniques: assessing contaminating background proteins.

    Science.gov (United States)

    Howell, Jenika M; Winstone, Tara L; Coorssen, Jens R; Turner, Raymond J

    2006-04-01

    Determination of protein-protein interactions is an important component in assigning function and discerning the biological relevance of proteins within a broader cellular context. In vitro protein-protein interaction methodologies, including affinity chromatography, coimmunoprecipitation, and newer approaches such as protein chip arrays, hold much promise in the detection of protein interactions, particularly in well-characterized organisms with sequenced genomes. However, each of these approaches attracts certain background proteins that can thwart detection and identification of true interactors. In addition, recombinant proteins expressed in Escherichia coli are also extensively used to assess protein-protein interactions, and background proteins in these isolates can thus contaminate interaction studies. Rigorous validation of a true interaction thus requires not only that an interaction be found by alternate techniques, but more importantly that researchers be aware of and control for matrix/support dependence. Here, we evaluate these methods for proteins interacting with DmsD (an E. coli redox enzyme maturation protein chaperone), in vitro, using E. coli subcellular fractions as prey sources. We compare and contrast the various in vitro interaction methods to identify some of the background proteins and protein profiles that are inherent to each of the methods in an E. coli system.

  17. Inferring domain-domain interactions from protein-protein interactions with formal concept analysis.

    Directory of Open Access Journals (Sweden)

    Susan Khor

    Full Text Available Identifying reliable domain-domain interactions will increase our ability to predict novel protein-protein interactions, to unravel interactions in protein complexes, and thus gain more information about the function and behavior of genes. One of the challenges of identifying reliable domain-domain interactions is domain promiscuity. Promiscuous domains are domains that can occur in many domain architectures and are therefore found in many proteins. This becomes a problem for a method where the score of a domain-pair is the ratio between observed and expected frequencies because the protein-protein interaction network is sparse. As such, many protein-pairs will be non-interacting and domain-pairs with promiscuous domains will be penalized. This domain promiscuity challenge to the problem of inferring reliable domain-domain interactions from protein-protein interactions has been recognized, and a number of work-arounds have been proposed. This paper reports on an application of Formal Concept Analysis to this problem. It is found that the relationship between formal concepts provides a natural way for rare domains to elevate the rank of promiscuous domain-pairs and enrich highly ranked domain-pairs with reliable domain-domain interactions. This piggybacking of promiscuous domain-pairs onto less promiscuous domain-pairs is possible only with concept lattices whose attribute-labels are not reduced and is enhanced by the presence of proteins that comprise both promiscuous and rare domains.

  18. Identification of hot-spot residues in protein-protein interactions by computational docking

    Directory of Open Access Journals (Sweden)

    Fernández-Recio Juan

    2008-10-01

    Full Text Available Abstract Background The study of protein-protein interactions is becoming increasingly important for biotechnological and therapeutic reasons. We can define two major areas therein: the structural prediction of protein-protein binding mode, and the identification of the relevant residues for the interaction (so called 'hot-spots'. These hot-spot residues have high interest since they are considered one of the possible ways of disrupting a protein-protein interaction. Unfortunately, large-scale experimental measurement of residue contribution to the binding energy, based on alanine-scanning experiments, is costly and thus data is fairly limited. Recent computational approaches for hot-spot prediction have been reported, but they usually require the structure of the complex. Results We have applied here normalized interface propensity (NIP values derived from rigid-body docking with electrostatics and desolvation scoring for the prediction of interaction hot-spots. This parameter identifies hot-spot residues on interacting proteins with predictive rates that are comparable to other existing methods (up to 80% positive predictive value, and the advantage of not requiring any prior structural knowledge of the complex. Conclusion The NIP values derived from rigid-body docking can reliably identify a number of hot-spot residues whose contribution to the interaction arises from electrostatics and desolvation effects. Our method can propose residues to guide experiments in complexes of biological or therapeutic interest, even in cases with no available 3D structure of the complex.

  19. Cell penetrating peptides to dissect host-pathogen protein-protein interactions in Theileria -transformed leukocytes

    KAUST Repository

    Haidar, Malak

    2017-09-08

    One powerful application of cell penetrating peptides is the delivery into cells of molecules that function as specific competitors or inhibitors of protein-protein interactions. Ablating defined protein-protein interactions is a refined way to explore their contribution to a particular cellular phenotype in a given disease context. Cell-penetrating peptides can be synthetically constrained through various chemical modifications that stabilize a given structural fold with the potential to improve competitive binding to specific targets. Theileria-transformed leukocytes display high PKA activity, but PKAis an enzyme that plays key roles in multiple cellular processes; consequently genetic ablation of kinase activity gives rise to a myriad of confounding phenotypes. By contrast, ablation of a specific kinase-substrate interaction has the potential to give more refined information and we illustrate this here by describing how surgically ablating PKA interactions with BAD gives precise information on the type of glycolysis performed by Theileria-transformed leukocytes. In addition, we provide two other examples of how ablating specific protein-protein interactions in Theileria-infected leukocytes leads to precise phenotypes and argue that constrained penetrating peptides have great therapeutic potential to combat infectious diseases in general.

  20. What Evidence Is There for the Homology of Protein-Protein Interactions?

    Science.gov (United States)

    Lewis, Anna C. F.; Jones, Nick S.; Porter, Mason A.; Deane, Charlotte M.

    2012-01-01

    The notion that sequence homology implies functional similarity underlies much of computational biology. In the case of protein-protein interactions, an interaction can be inferred between two proteins on the basis that sequence-similar proteins have been observed to interact. The use of transferred interactions is common, but the legitimacy of such inferred interactions is not clear. Here we investigate transferred interactions and whether data incompleteness explains the lack of evidence found for them. Using definitions of homology associated with functional annotation transfer, we estimate that conservation rates of interactions are low even after taking interactome incompleteness into account. For example, at a blastp -value threshold of , we estimate the conservation rate to be about between S. cerevisiae and H. sapiens. Our method also produces estimates of interactome sizes (which are similar to those previously proposed). Using our estimates of interaction conservation we estimate the rate at which protein-protein interactions are lost across species. To our knowledge, this is the first such study based on large-scale data. Previous work has suggested that interactions transferred within species are more reliable than interactions transferred across species. By controlling for factors that are specific to within-species interaction prediction, we propose that the transfer of interactions within species might be less reliable than transfers between species. Protein-protein interactions appear to be very rarely conserved unless very high sequence similarity is observed. Consequently, inferred interactions should be used with care. PMID:23028270

  1. A new protein-protein docking scoring function based on interface residue properties.

    Science.gov (United States)

    Bernauer, J; Azé, J; Janin, J; Poupon, A

    2007-03-01

    Protein-protein complexes are known to play key roles in many cellular processes. However, they are often not accessible to experimental study because of their low stability and difficulty to produce the proteins and assemble them in native conformation. Thus, docking algorithms have been developed to provide an in silico approach of the problem. A protein-protein docking procedure traditionally consists of two successive tasks: a search algorithm generates a large number of candidate solutions, and then a scoring function is used to rank them. To address the second step, we developed a scoring function based on a Voronoï tessellation of the protein three-dimensional structure. We showed that the Voronoï representation may be used to describe in a simplified but useful manner, the geometric and physico-chemical complementarities of two molecular surfaces. We measured a set of parameters on native protein-protein complexes and on decoys, and used them as attributes in several statistical learning procedures: a logistic function, Support Vector Machines (SVM), and a genetic algorithm. For the later, we used ROGER, a genetic algorithm designed to optimize the area under the receiver operating characteristics curve. To further test the scores derived with ROGER, we ranked models generated by two different docking algorithms on targets of a blind prediction experiment, improving in almost all cases the rank of native-like solutions. http://genomics.eu.org/spip/-Bioinformatics-tools-

  2. Sequence and structural features of binding site residues in protein-protein complexes: comparison with protein-nucleic acid complexes

    Directory of Open Access Journals (Sweden)

    Selvaraj S

    2011-10-01

    Full Text Available Abstract Background Protein-protein interactions are important for several cellular processes. Understanding the mechanism of protein-protein recognition and predicting the binding sites in protein-protein complexes are long standing goals in molecular and computational biology. Methods We have developed an energy based approach for identifying the binding site residues in protein–protein complexes. The binding site residues have been analyzed with sequence and structure based parameters such as binding propensity, neighboring residues in the vicinity of binding sites, conservation score and conformational switching. Results We observed that the binding propensities of amino acid residues are specific for protein-protein complexes. Further, typical dipeptides and tripeptides showed high preference for binding, which is unique to protein-protein complexes. Most of the binding site residues are highly conserved among homologous sequences. Our analysis showed that 7% of residues changed their conformations upon protein-protein complex formation and it is 9.2% and 6.6% in the binding and non-binding sites, respectively. Specifically, the residues Glu, Lys, Leu and Ser changed their conformation from coil to helix/strand and from helix to coil/strand. Leu, Ser, Thr and Val prefer to change their conformation from strand to coil/helix. Conclusions The results obtained in this study will be helpful for understanding and predicting the binding sites in protein-protein complexes.

  3. Physicochemical descriptors to discriminate protein-protein interactions in permanent and transient complexes selected by means of machine learning algorithms.

    Science.gov (United States)

    Block, Peter; Paern, Juri; Hüllermeier, Eyke; Sanschagrin, Paul; Sotriffer, Christoph A; Klebe, Gerhard

    2006-11-15

    Analyzing protein-protein interactions at the atomic level is critical for our understanding of the principles governing the interactions involved in protein-protein recognition. For this purpose, descriptors explaining the nature of different protein-protein complexes are desirable. In this work, the authors introduced Epic Protein Interface Classification as a framework handling the preparation, processing, and analysis of protein-protein complexes for classification with machine learning algorithms. We applied four different machine learning algorithms: Support Vector Machines, C4.5 Decision Trees, K Nearest Neighbors, and Naïve Bayes algorithm in combination with three feature selection methods, Filter (Relief F), Wrapper, and Genetic Algorithms, to extract discriminating features from the protein-protein complexes. To compare protein-protein complexes to each other, the authors represented the physicochemical characteristics of their interfaces in four different ways, using two different atomic contact vectors, DrugScore pair potential vectors and SFCscore descriptor vectors. We classified two different datasets: (A) 172 protein-protein complexes comprising 96 monomers, forming contacts enforced by the crystallographic packing environment (crystal contacts), and 76 biologically functional homodimer complexes; (B) 345 protein-protein complexes containing 147 permanent complexes and 198 transient complexes. We were able to classify up to 94.8% of the packing enforced/functional and up to 93.6% of the permanent/transient complexes correctly. Furthermore, we were able to extract relevant features from the different protein-protein complexes and introduce an approach for scoring the importance of the extracted features. (c) 2006 Wiley-Liss, Inc.

  4. Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

    Directory of Open Access Journals (Sweden)

    Vandepoele Klaas

    2009-06-01

    Full Text Available Abstract Background Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome. Results In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization and components (e.g. ARPs, actin-related proteins exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively. Conclusion We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

  5. Photo-initiated crosslinking extends mapping of the protein-protein interface to membrane-embedded portions of cytochromes P450 2B4 and b₅.

    Science.gov (United States)

    Ječmen, Tomáš; Ptáčková, Renata; Černá, Věra; Dračínská, Helena; Hodek, Petr; Stiborová, Marie; Hudeček, Jiří; Šulc, Miroslav

    2015-11-01

    Protein-protein interactions play a central role in the regulation of many biochemical processes (e.g. the system participating in enzyme catalysis). Therefore, a deeper understanding of protein-protein interactions may contribute to the elucidation of many biologically important mechanisms. For this purpose, it is necessary to establish the composition and stoichiometry of supramolecular complexes and to identify the crucial portions of the interacting molecules. This study is devoted to structure-functional relationships in the microsomal Mixed Function Oxidase (MFO) complex, which is responsible for biotransformation of many hydrophobic endogenous compounds and xenobiotics. In particular, the cytochrome b5 interaction with MFO terminal oxygenase cytochrome P-450 (P450) was studied. To create photolabile probes suitable for this purpose, we prepared cytochrome b5 which had a photolabile diazirine analog of methionine (pMet) incorporated into the protein sequence, employing recombinant expression in Escherichia coli. In addition to wild-type cytochrome b5, where three methionines (Met) are located at positions 96, 126, and 131, six mutants containing only one Met in the sequence were designed and expressed (see Table 1). In these mutants, a single Met was engineered into the catalytic domain (at positions 23, 41, or 46), into the linker between the protein domains (at position 96), or into the membrane region (at positions 126 or 131). These mutants should confirm or exclude these portions of cytochrome b5 which are involved in the interaction with P450. After UV irradiation, the pMet group(s) in the photolabile cytochrome b5 probe was(were) activated, producing covalent crosslinks with the interacting parts of P450 2B4 in the close vicinity. The covalent complexes were analyzed by the "bottom up" approach with high-accuracy mass spectrometry. The analysis provided an identification of the contacts in the supramolecular complex with low structural resolution. We

  6. Measures of Biochemical Sociology

    Science.gov (United States)

    Snell, Joel; Marsh, Mitchell

    2008-01-01

    In a previous article, the authors introduced a new sub field in sociology that we labeled "biochemical sociology." We introduced the definition of a sociology that encompasses sociological measures, psychological measures, and biological indicators Snell & Marsh (2003). In this article, we want to demonstrate a research strategy that would assess…

  7. Biochemical Education in Brazil.

    Science.gov (United States)

    Vella, F.

    1988-01-01

    Described are discussions held concerning the problems of biochemical education in Brazil at a meeting of the Sociedade Brazileira de Bioquimica in April 1988. Also discussed are other visits that were made to universities in Brazil. Three major recommendations to improve the state of biochemistry education in Brazil are presented. (CW)

  8. Biochemical Education in Brazil.

    Science.gov (United States)

    Vella, F.

    1988-01-01

    Described are discussions held concerning the problems of biochemical education in Brazil at a meeting of the Sociedade Brazileira de Bioquimica in April 1988. Also discussed are other visits that were made to universities in Brazil. Three major recommendations to improve the state of biochemistry education in Brazil are presented. (CW)

  9. Measures of Biochemical Sociology

    Science.gov (United States)

    Snell, Joel; Marsh, Mitchell

    2008-01-01

    In a previous article, the authors introduced a new sub field in sociology that we labeled "biochemical sociology." We introduced the definition of a sociology that encompasses sociological measures, psychological measures, and biological indicators Snell & Marsh (2003). In this article, we want to demonstrate a research strategy that would assess…

  10. Towards a map of the Populus biomass protein-protein interaction network

    Energy Technology Data Exchange (ETDEWEB)

    Beers, Eric [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Brunner, Amy [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Helm, Richard [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Dickerman, Allan [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)

    2015-07-31

    Biofuels can be produced from a variety of plant feedstocks. The value of a particular feedstock for biofuels production depends in part on the degree of difficulty associated with the extraction of fermentable sugars from the plant biomass. The wood of trees is potentially a rich source fermentable sugars. However, the sugars in wood exist in a tightly cross-linked matrix of cellulose, hemicellulose, and lignin, making them largely recalcitrant to release and fermentation for biofuels production. Before breeders and genetic engineers can effectively develop plants with reduced recalcitrance to fermentation, it is necessary to gain a better understanding of the fundamental biology of the mechanisms responsible for wood formation. Regulatory, structural, and enzymatic proteins are required for the complicated process of wood formation. To function properly, proteins must interact with other proteins. Yet, very few of the protein-protein interactions necessary for wood formation are known. The main objectives of this project were to 1) identify new protein-protein interactions relevant to wood formation, and 2) perform in-depth characterizations of selected protein-protein interactions. To identify relevant protein-protein interactions, we cloned a set of approximately 400 genes that were highly expressed in the wood-forming tissue (known as secondary xylem) of poplar (Populus trichocarpa). We tested whether the proteins encoded by these biomass genes interacted with each other in a binary matrix design using the yeast two-hybrid (Y2H) method for protein-protein interaction discovery. We also tested a subset of the 400 biomass proteins for interactions with all proteins present in wood-forming tissue of poplar in a biomass library screen design using Y2H. Together, these two Y2H screens yielded over 270 interactions involving over 75 biomass proteins. For the second main objective we selected several interacting pairs or groups of interacting proteins for in

  11. DECK: Distance and environment-dependent, coarse-grained, knowledge-based potentials for protein-protein docking

    Directory of Open Access Journals (Sweden)

    Vakser Ilya A

    2011-07-01

    Full Text Available Abstract Background Computational approaches to protein-protein docking typically include scoring aimed at improving the rank of the near-native structure relative to the false-positive matches. Knowledge-based potentials improve modeling of protein complexes by taking advantage of the rapidly increasing amount of experimentally derived information on protein-protein association. An essential element of knowledge-based potentials is defining the reference state for an optimal description of the residue-residue (or atom-atom pairs in the non-interaction state. Results The study presents a new Distance- and Environment-dependent, Coarse-grained, Knowledge-based (DECK potential for scoring of protein-protein docking predictions. Training sets of protein-protein matches were generated based on bound and unbound forms of proteins taken from the DOCKGROUND resource. Each residue was represented by a pseudo-atom in the geometric center of the side chain. To capture the long-range and the multi-body interactions, residues in different secondary structure elements at protein-protein interfaces were considered as different residue types. Five reference states for the potentials were defined and tested. The optimal reference state was selected and the cutoff effect on the distance-dependent potentials investigated. The potentials were validated on the docking decoys sets, showing better performance than the existing potentials used in scoring of protein-protein docking results. Conclusions A novel residue-based statistical potential for protein-protein docking was developed and validated on docking decoy sets. The results show that the scoring function DECK can successfully identify near-native protein-protein matches and thus is useful in protein docking. In addition to the practical application of the potentials, the study provides insights into the relative utility of the reference states, the scope of the distance dependence, and the coarse-graining of

  12. Sequence motifs in MADS transcription factors responsible for specificity and diversification of protein-protein interaction.

    Directory of Open Access Journals (Sweden)

    Aalt D J van Dijk

    Full Text Available Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and

  13. Characterization of protein-protein interaction interfaces from a single species.

    Science.gov (United States)

    Talavera, David; Robertson, David L; Lovell, Simon C

    2011-01-01

    Most proteins attain their biological functions through specific interactions with other proteins. Thus, the study of protein-protein interactions and the interfaces that mediate these interactions is of prime importance for the understanding of biological function. In particular the precise determinants of binding specificity and their contributions to binding energy within protein interfaces are not well understood. In order to better understand these determinants an appropriate description of the interaction surface is needed. Available data from the yeast Saccharomyces cerevisiae allow us to focus on a single species and to use all the available structures, correcting for redundancy, instead of using structural representatives. This allows us to control for potentially confounding factors that may affect sequence propensities. We find a significant contribution of main-chain atoms to protein-protein interactions. These include interactions both with other main-chain and side-chain atoms on the interacting chain. We find that the type of interaction depends on both amino acid and secondary structure type involved in the contact. For example, residues in α-helices and large amino acids are the most likely to be involved in interactions through their side-chain atoms. We find an intriguing homogeneity when calculating the average solvation energy of different areas of the protein surface. Unexpectedly, homo- and hetero-complexes have quite similar results for all analyses. Our findings demonstrate that the manner in which protein-protein interactions are formed is determined by the residue type and the secondary structure found in the interface. However the homogeneity of the desolvation energy despite heterogeneity of interface properties suggests a complex relationship between interface composition and binding energy.

  14. Cerebral malaria: insights from host-parasite protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Bulusu Gopalakrishnan

    2010-06-01

    Full Text Available Abstract Background Cerebral malaria is a form of human malaria wherein Plasmodium falciparum-infected red blood cells adhere to the blood capillaries in the brain, potentially leading to coma and death. Interactions between parasite and host proteins are important in understanding the pathogenesis of this deadly form of malaria. It is, therefore, necessary to study available protein-protein interactions to identify lesser known interactions that could throw light on key events of cerebral malaria. Methods Sequestration, haemostasis dysfunction, systemic inflammation and neuronal damage are key processes of cerebral malaria. Key events were identified from literature as being crucial to these processes. An integrated interactome was created using available experimental and predicted datasets as well as from literature. Interactions from this interactome were filtered based on Gene Ontology and tissue-specific annotations, and further analysed for relevance to the key events. Results PfEMP1 presentation, platelet activation and astrocyte dysfunction were identified as the key events influencing the disease. 48896 host-parasite along with other host-parasite, host-host and parasite-parasite protein-protein interactions obtained from a disease-specific corpus were combined to form an integrated interactome. Filtering of the interactome resulted in five host-parasite PPI, six parasite-parasite and two host-host PPI. The analysis of these interactions revealed the potential significance of apolipoproteins and temperature/Hsp expression on efficient PfEMP1 presentation; role of MSP-1 in platelet activation; effect of parasite proteins in TGF-β regulation and the role of albumin in astrocyte dysfunction. Conclusions This work links key host-parasite, parasite-parasite and host-host protein-protein interactions to key processes of cerebral malaria and generates hypotheses for disease pathogenesis based on a filtered interaction dataset. These

  15. Multi-level machine learning prediction of protein-protein interactions in Saccharomyces cerevisiae.

    Science.gov (United States)

    Zubek, Julian; Tatjewski, Marcin; Boniecki, Adam; Mnich, Maciej; Basu, Subhadip; Plewczynski, Dariusz

    2015-01-01

    Accurate identification of protein-protein interactions (PPI) is the key step in understanding proteins' biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein-protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB) database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein-protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC). Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent).

  16. Improving analytical methods for protein-protein interaction through implementation of chemically inducible dimerization

    DEFF Research Database (Denmark)

    Andersen, Tonni Grube; Nintemann, Sebastian; Marek, Magdalena;

    2016-01-01

    into the widely used split ubiquitin-, bimolecular fluorescence complementation (BiFC)- and Forster resonance energy transfer (FRET)-based methods and investigated different protein-protein interactions in yeast and plants. We demonstrate the functionality of this concept by the analysis of weakly interacting...... proteins from specialized metabolism in the model plant Arabidopsis thaliana. Our results illustrate that chemically induced dimerization can function as a built-in control for split-based systems that is easily implemented and allows for direct evaluation of functionality....

  17. Assessing protein-protein interactions based on the semantic similarity of interacting proteins.

    Science.gov (United States)

    Cui, Guangyu; Kim, Byungmin; Alguwaizani, Saud; Han, Kyungsook

    2015-01-01

    The Gene Ontology (GO) has been used in estimating the semantic similarity of proteins since it has the largest and reliable vocabulary of gene products and characteristics. We developed a new method which can assess Protein-Protein Interactions (PPI) using the branching factor and information content of the common ancestor of interacting proteins in the GO hierarchy. We performed a comparative evaluation of the measure with other GO-based similarity measures and evaluation results showed that our method outperformed others in most GO domains.

  18. PROXiMATE: a database of mutant protein-protein complex thermodynamics and kinetics.

    Science.gov (United States)

    Jemimah, Sherlyn; Yugandhar, K; Michael Gromiha, M

    2017-09-01

    We have developed PROXiMATE, a database of thermodynamic data for more than 6000 missense mutations in 174 heterodimeric protein-protein complexes, supplemented with interaction network data from STRING database, solvent accessibility, sequence, structural and functional information, experimental conditions and literature information. Additional features include complex structure visualization, search and display options, download options and a provision for users to upload their data. The database is freely available at http://www.iitm.ac.in/bioinfo/PROXiMATE/ . The website is implemented in Python, and supports recent versions of major browsers such as IE10, Firefox, Chrome and Opera. gromiha@iitm.ac.in. Supplementary data are available at Bioinformatics online.

  19. Cross-Species Virus-Host Protein-Protein Interactions Inhibiting Innate Immunity

    Science.gov (United States)

    2016-07-01

    diseases are a regular occurrence globally (Figure 1). The Zika virus is the latest example gaining widespread attention. Many of the (re-)emerging...8725 John J. Kingman Road, MS 6201 Fort Belvoir, VA 22060-6201 T E C H N IC A L R E P O R T DTRA-TR-16-79 Cross-species virus -host...Cross-species virus -host protein-protein interactions inhibiting innate immunity What are the major goals of the project? List the major goals of

  20. Globular and disordered-the non-identical twins in protein-protein interactions

    DEFF Research Database (Denmark)

    Teilum, Kaare; Olsen, Johan Gotthardt; Kragelund, Birthe Brandt

    2015-01-01

    In biology proteins from different structural classes interact across and within classes in ways that are optimized to achieve balanced functional outputs. The interactions between intrinsically disordered proteins (IDPs) and other proteins rely on changes in flexibility and this is seen as a str...... of other protein-protein interactions. We find that ordered proteins and the disordered ones act as non-identical twins operating by similar principles but where the disordered proteins complexes are on average less stable by 2.5 kcal mol(-1)....

  1. HomPPI: a class of sequence homology based protein-protein interface prediction methods

    Directory of Open Access Journals (Sweden)

    Dobbs Drena

    2011-06-01

    Full Text Available Abstract Background Although homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate. Results We studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence. Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i NPS-HomPPI (Non partner-specific HomPPI, which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii PS-HomPPI (Partner-specific HomPPI, which can be used to predict the interface residues of a query protein with a specific target protein. Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of

  2. Efficient fold-change detection based on protein-protein interactions.

    Science.gov (United States)

    Buijsman, W; Sheinman, M

    2014-02-01

    Various biological sensory systems exhibit a response to a relative change of the stimulus, often referred to as fold-change detection. In the past few years, fold-change detecting mechanisms, based on transcriptional networks, have been proposed. Here we present a fold-change detecting mechanism, based on protein-protein interactions, consisting of two interacting proteins. This mechanism does not consume chemical energy and is not subject to transcriptional and translational noise, in contrast to previously proposed mechanisms. We show by analytical and numerical calculations that the mechanism is robust and can have a fast, precise, and efficient response for parameters that are relevant to eukaryotic cells.

  3. PPI layouts: BioJS components for the display of Protein-Protein Interactions.

    Science.gov (United States)

    Salazar, Gustavo A; Meintjes, Ayton; Mulder, Nicola

    2014-01-01

    We present two web-based components for the display of Protein-Protein Interaction networks using different self-organizing layout methods: force-directed and circular. These components conform to the BioJS standard and can be rendered in an HTML5-compliant browser without the need for third-party plugins. We provide examples of interaction networks and how the components can be used to visualize them, and refer to a more complex tool that uses these components. http://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.7753.

  4. Signal transduction meets systems biology: deciphering specificity determinants for protein-protein interactions

    Science.gov (United States)

    Bourret, Robert B.

    2008-01-01

    Two recent papers [Gao et al. Mol. Microbiol. 69, 1358 (2008); Skerker et al. Cell 133, 1043 (2008)] describe investigations into the specificity of protein-protein interactions that occur during signal transduction by two-component regulatory systems. This MicroCommentary summarizes and provides context for the reported findings. The results offer insights into molecular determinants that provide specificity to maintain signal separation and thus prevent deleterious crosstalk between pathways, as well as the potential extent and nature of interactions that may combine signals to achieve beneficial cross regulation among pathways. The methods employed are suitable for application to other systems. PMID:18694439

  5. Efficient fold-change detection based on protein-protein interactions

    Science.gov (United States)

    Buijsman, W.; Sheinman, M.

    2014-02-01

    Various biological sensory systems exhibit a response to a relative change of the stimulus, often referred to as fold-change detection. In the past few years, fold-change detecting mechanisms, based on transcriptional networks, have been proposed. Here we present a fold-change detecting mechanism, based on protein-protein interactions, consisting of two interacting proteins. This mechanism does not consume chemical energy and is not subject to transcriptional and translational noise, in contrast to previously proposed mechanisms. We show by analytical and numerical calculations that the mechanism is robust and can have a fast, precise, and efficient response for parameters that are relevant to eukaryotic cells.

  6. Novel Technology for Protein-Protein Interaction-based Targeted Drug Discovery

    Directory of Open Access Journals (Sweden)

    Jung Me Hwang

    2011-12-01

    Full Text Available We have developed a simple but highly efficient in-cell protein-protein interaction (PPI discovery system based on the translocation properties of protein kinase C- and its C1a domain in live cells. This system allows the visual detection of trimeric and dimeric protein interactions including cytosolic, nuclear, and/or membrane proteins with their cognate ligands. In addition, this system can be used to identify pharmacological small compounds that inhibit specific PPIs. These properties make this PPI system an attractive tool for screening drug candidates and mapping the protein interactome.

  7. DOCK/PIERR: web server for structure prediction of protein-protein complexes.

    Science.gov (United States)

    Viswanath, Shruthi; Ravikant, D V S; Elber, Ron

    2014-01-01

    In protein docking we aim to find the structure of the complex formed when two proteins interact. Protein-protein interactions are crucial for cell function. Here we discuss the usage of DOCK/PIERR. In DOCK/PIERR, a uniformly discrete sampling of orientations of one protein with respect to the other, are scored, followed by clustering, refinement, and reranking of structures. The novelty of this method lies in the scoring functions used. These are obtained by examining hundreds of millions of correctly and incorrectly docked structures, using an algorithm based on mathematical programming, with provable convergence properties.

  8. DrugScorePPI knowledge-based potentials used as scoring and objective function in protein-protein docking.

    Directory of Open Access Journals (Sweden)

    Dennis M Krüger

    Full Text Available The distance-dependent knowledge-based DrugScore(PPI potentials, previously developed for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes, are evaluated as a scoring and objective function for the structure prediction of protein-protein complexes. When applied for ranking "unbound perturbation" ("unbound docking" decoys generated by Baker and coworkers a 4-fold (1.5-fold enrichment of acceptable docking solutions in the top ranks compared to a random selection is found. When applied as an objective function in FRODOCK for bound protein-protein docking on 97 complexes of the ZDOCK benchmark 3.0, DrugScore(PPI/FRODOCK finds up to 10% (15% more high accuracy solutions in the top 1 (top 10 predictions than the original FRODOCK implementation. When used as an objective function for global unbound protein-protein docking, fair docking success rates are obtained, which improve by ∼ 2-fold to 18% (58% for an at least acceptable solution in the top 10 (top 100 predictions when performing knowledge-driven unbound docking. This suggests that DrugScore(PPI balances well several different types of interactions important for protein-protein recognition. The results are discussed in view of the influence of crystal packing and the type of protein-protein complex docked. Finally, a simple criterion is provided with which to estimate a priori if unbound docking with DrugScore(PPI/FRODOCK will be successful.

  9. Recovering protein-protein and domain-domain interactions from aggregation of IP-MS proteomics of coregulator complexes.

    Directory of Open Access Journals (Sweden)

    Amin R Mazloom

    2011-12-01

    Full Text Available Coregulator proteins (CoRegs are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP followed by mass spectrometry (MS applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/.

  10. DUF581 is plant specific FCS-like zinc finger involved in protein-protein interaction.

    Science.gov (United States)

    K, Muhammed Jamsheer; Laxmi, Ashverya

    2014-01-01

    Zinc fingers are a ubiquitous class of protein domain with considerable variation in structure and function. Zf-FCS is a highly diverged group of C2-C2 zinc finger which is present in animals, prokaryotes and viruses, but not in plants. In this study we identified that a plant specific domain of unknown function, DUF581 is a zf-FCS type zinc finger. Based on HMM-HMM comparison and signature motif similarity we named this domain as FCS-Like Zinc finger (FLZ) domain. A genome wide survey identified that FLZ domain containing genes are bryophytic in origin and this gene family is expanded in spermatophytes. Expression analysis of selected FLZ gene family members of A. thaliana identified an overlapping expression pattern suggesting a possible redundancy in their function. Unlike the zf-FCS domain, the FLZ domain found to be highly conserved in sequence and structure. Using a combination of bioinformatic and protein-protein interaction tools, we identified that FLZ domain is involved in protein-protein interaction.

  11. Integrating semantic information into multiple kernels for protein-protein interaction extraction from biomedical literatures.

    Directory of Open Access Journals (Sweden)

    Lishuang Li

    Full Text Available Protein-Protein Interaction (PPI extraction is an important task in the biomedical information extraction. Presently, many machine learning methods for PPI extraction have achieved promising results. However, the performance is still not satisfactory. One reason is that the semantic resources were basically ignored. In this paper, we propose a multiple-kernel learning-based approach to extract PPIs, combining the feature-based kernel, tree kernel and semantic kernel. Particularly, we extend the shortest path-enclosed tree kernel (SPT by a dynamic extended strategy to retrieve the richer syntactic information. Our semantic kernel calculates the protein-protein pair similarity and the context similarity based on two semantic resources: WordNet and Medical Subject Heading (MeSH. We evaluate our method with Support Vector Machine (SVM and achieve an F-score of 69.40% and an AUC of 92.00%, which show that our method outperforms most of the state-of-the-art systems by integrating semantic information.

  12. A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

    Directory of Open Access Journals (Sweden)

    Rita Melo

    2016-07-01

    Full Text Available Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM, for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.

  13. Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks

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    Sandip Chakraborty

    2016-01-01

    Full Text Available Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes’ adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another.

  14. Using singular value decomposition to characterize protein-protein interactions by in-cell NMR spectroscopy.

    Science.gov (United States)

    Majumder, Subhabrata; DeMott, Christopher M; Burz, David S; Shekhtman, Alexander

    2014-05-05

    Distinct differences between how model proteins interact in-cell and in vitro suggest that the cytosol might have a profound effect in modulating protein-protein and/or protein-ligand interactions that are not observed in vitro. Analyses of in-cell NMR spectra of target proteins interacting with physiological partners are further complicated by low signal-to-noise ratios, and the long overexpression times used in protein-protein interaction studies may lead to changes in the in-cell spectra over the course of the experiment. To unambiguously resolve the principal binding mode between two interacting species against the dynamic cellular background, we analyzed in-cell spectral data of a target protein over the time course of overexpression of its interacting partner by using single-value decomposition (SVD). SVD differentiates between concentration-dependent and concentration-independent events and identifies the principal binding mode between the two species. The analysis implicates a set of amino acids involved in the specific interaction that differs from previous NMR analyses but is in good agreement with crystallographic data. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. A novel functional module detection algorithm for protein-protein interaction networks

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    Zhang Aidong

    2006-12-01

    Full Text Available Abstract Background The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM. Results STM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be effective at detecting clusters with a diverse range of interaction structures that are significant on measures of biological relevance. The STM approach is compared to six competing approaches including the maximum clique, quasi-clique, minimum cut, betweeness cut and Markov Clustering (MCL algorithms. The clusters obtained by each technique are compared for enrichment of biological function. STM generates larger clusters and the clusters identified have p-values that are approximately 125-fold better than the other methods on biological function. An important strength of STM is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches. Conclusion STM outperforms competing approaches and is capable of effectively detecting both densely and sparsely connected, biologically relevant functional modules with fewer discards.

  16. Optimization and dynamics of protein-protein complexes using B-splines.

    Science.gov (United States)

    Gillilan, Richard E; Lilien, Ryan H

    2004-10-01

    A moving-grid approach for optimization and dynamics of protein-protein complexes is introduced, which utilizes cubic B-spline interpolation for rapid energy and force evaluation. The method allows for the efficient use of full electrostatic potentials joined smoothly to multipoles at long distance so that multiprotein simulation is possible. Using a recently published benchmark of 58 protein complexes, we examine the performance and quality of the grid approximation, refining cocrystallized complexes to within 0.68 A RMSD of interface atoms, close to the optimum 0.63 A produced by the underlying MMFF94 force field. We quantify the theoretical statistical advantage of using minimization in a stochastic search in the case of two rigid bodies, and contrast it with the underlying cost of conjugate gradient minimization using B-splines. The volumes of conjugate gradient minimization basins of attraction in cocrystallized systems are generally orders of magnitude larger than well volumes based on energy thresholds needed to discriminate native from nonnative states; nonetheless, computational cost is significant. Molecular dynamics using B-splines is doubly efficient due to the combined advantages of rapid force evaluation and large simulation step sizes. Large basins localized around the native state and other possible binding sites are identifiable during simulations of protein-protein motion. In addition to providing increased modeling detail, B-splines offer new algorithmic possibilities that should be valuable in refining docking candidates and studying global complex behavior.

  17. Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Furuya Toshio

    2011-02-01

    Full Text Available Abstract Background The amount of data on protein-protein interactions (PPIs available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine. Results To address this need, we have developed the 'Druggable Protein-protein Interaction Assessment System' (Dr. PIAS. Dr. PIAS has a meta-database that stores various types of information (tertiary structures, drugs/chemicals, and biological functions associated with PPIs retrieved from public sources. By integrating this information, Dr. PIAS assesses whether a PPI is druggable as a target for small chemical ligands by using a supervised machine-learning method, support vector machine (SVM. Dr. PIAS holds not only known druggable PPIs but also all PPIs of human, mouse, rat, and human immunodeficiency virus (HIV proteins identified to date. Conclusions The design concept of Dr. PIAS is distinct from other published PPI databases in that it focuses on selecting the PPIs most likely to make good drug targets, rather than merely collecting PPI data.

  18. Protein-Protein Interactions in the Regulation of WRKY Transcription Factors

    Institute of Scientific and Technical Information of China (English)

    Yingjun Chi; Yan Yang; Yuan Zhou; Jie Zhou; Baofang Fan; Jing-Quan Yu; Zhixiang Chen

    2013-01-01

    It has been almost 20 years since the first report of a WRKY transcription factor,SPF1,from sweet potato.Great progress has been made since then in establishing the diverse biological roles of WRKY transcription factors in plant growth,development,and responses to biotic and abiotic stress.Despite the functional diversity,almost all analyzed WRKY proteins recognize the TrGACC/T W-box sequences and,therefore,mechanisms other than mere recognition of the core W-box promoter elements are necessary to achieve the regulatory specificity of WRKY transcription factors.Research over the past several years has revealed that WRKY transcription factors physically interact with a wide range of proteins with roles in signaling,transcription,and chromatin remodeling.Studies of WRKY-interacting proteins have provided important insights into the regulation and mode of action of members of the important family of transcription factors.It has also emerged that the slightly varied WRKY domains and other protein motifs conserved within each of the seven WRKY subfamilies participate in protein-protein interactions and mediate complex functional interactions between WRKY proteins and between WRKY and other regulatory proteins in the modulation of important biological processes.In this review,we summarize studies of protein-protein interactions for WRKY transcription factors and discuss how the interacting partners contribute,at different levels,to the establishment of the complex regulatory and functional network of WRKY transcription factors.

  19. Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

    Institute of Scientific and Technical Information of China (English)

    GAO Lei; LI Xia; GUO Zheng; ZHU MingZhu; LI YanHui; RAO ShaoQi

    2007-01-01

    GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interaction data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automatically selects the most appropriate functional classes as specific as possible during the learning process, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to "biology process" by three measures particularly designed for functional classes organized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.

  20. WATGEN: an algorithm for modeling water networks at protein-protein interfaces.

    Science.gov (United States)

    Bui, Huynh-Hoa; Schiewe, Alexandra J; Haworth, Ian S

    2007-11-15

    Water molecules at protein-protein interfaces contribute to the close packing of atoms and ensure complementarity between the protein surfaces, as well as mediating polar interactions. Therefore, modeling of interface water is of importance in understanding the structural basis of biomolecular association. We present an algorithm, WATGEN, which predicts locations for water molecules at a protein-protein or protein-peptide interface, given the atomic coordinates of the protein and peptide. A key element of the WATGEN algorithm is the prediction of water sites that can form multiple hydrogen bonds that bridge the binding interface. Trial calculations were performed on water networks predicted by WATGEN at 126 protein-peptide interfaces (X-ray resolutions algorithm predicts 72 and 88% of these sites within 1.5 and 2.0 A, respectively. The predicted number of water molecules at each interface was much higher than the number of water molecules identified experimentally. Therefore, random placement of the same number of water molecules as that predicted at each interface was performed as a control, and resulted in only 22 and 40% of water sites placed within 1.5 and 2.0 A of experimental sites, respectively. Based on these data, we conclude that WATGEN can accurately predict the location of water molecules at a protein-peptide interface, and this may be of value for understanding the energetics and specificity of biomolecular association. (c) 2007 Wiley Periodicals, Inc.

  1. Investigation of the pH-dependence of dye-doped protein-protein interactions.

    Science.gov (United States)

    Nudelman, Roman; Gloukhikh, Ekaterina; Rekun, Antonina; Richter, Shachar

    2016-11-01

    Proteins can dramatically change their conformation under environmental conditions such as temperature and pH. In this context, Glycoprotein's conformational determination is challenging. This is due to the variety of domains which contain rich chemical characters existing within this complex. Here we demonstrate a new, straightforward and efficient technique that uses the pH-dependent properties of dyes-doped Pig Gastric Mucin (PGM) for predicting and controlling protein-protein interaction and conformation. We utilize the PGM as natural host matrix which is capable of dynamically changing its conformational shape and adsorbing hydrophobic and hydrophilic dyes under different pH conditions and investigate and control the fluorescent properties of these composites in solution. It is shown at various pH conditions, a large variety of light emission from these complexes such as red, green and white is obtained. This phenomenon is explained by pH-dependent protein folding and protein-protein interactions that induce different emission spectra which are mediated and controlled by means of dye-dye interactions and surrounding environment. This process is used to form the technologically challenging white light-emitting liquid or solid coating for LED devices. © 2016 The Protein Society.

  2. Protein-protein interaction network-based detection of functionally similar proteins within species.

    Science.gov (United States)

    Song, Baoxing; Wang, Fen; Guo, Yang; Sang, Qing; Liu, Min; Li, Dengyun; Fang, Wei; Zhang, Deli

    2012-07-01

    Although functionally similar proteins across species have been widely studied, functionally similar proteins within species showing low sequence similarity have not been examined in detail. Identification of these proteins is of significant importance for understanding biological functions, evolution of protein families, progression of co-evolution, and convergent evolution and others which cannot be obtained by detection of functionally similar proteins across species. Here, we explored a method of detecting functionally similar proteins within species based on graph theory. After denoting protein-protein interaction networks using graphs, we split the graphs into subgraphs using the 1-hop method. Proteins with functional similarities in a species were detected using a method of modified shortest path to compare these subgraphs and to find the eligible optimal results. Using seven protein-protein interaction networks and this method, some functionally similar proteins with low sequence similarity that cannot detected by sequence alignment were identified. By analyzing the results, we found that, sometimes, it is difficult to separate homologous from convergent evolution. Evaluation of the performance of our method by gene ontology term overlap showed that the precision of our method was excellent. Copyright © 2012 Wiley Periodicals, Inc.

  3. Characterization of Protein-Protein Interfaces through a Protein Contact Network Approach.

    Science.gov (United States)

    Di Paola, Luisa; Platania, Chiara Bianca Maria; Oliva, Gabriele; Setola, Roberto; Pascucci, Federica; Giuliani, Alessandro

    2015-01-01

    Anthrax toxin comprises three different proteins, jointly acting to exert toxic activity: a non-toxic protective agent (PA), toxic edema factor (EF), and lethal factor (LF). Binding of PA to anthrax receptors promotes oligomerization of PA, binding of EF and LF, and then endocytosis of the complex. Homomeric forms of PA, complexes of PA bound to LF and to the endogenous receptor capillary morphogenesis gene 2 (CMG2) were analyzed. In this work, we characterized protein-protein interfaces (PPIs) and identified key residues at PPIs of complexes, by means of a protein contact network (PCN) approach. Flexibility and global and local topological properties of each PCN were computed. The vulnerability of each PCN was calculated using different node removal strategies, with reference to specific PCN topological descriptors, such as participation coefficient, contact order, and degree. The participation coefficient P, the topological descriptor of the node's ability to intervene in protein inter-module communication, was the key descriptor of PCN vulnerability of all structures. High P residues were localized both at PPIs and other regions of complexes, so that we argued an allosteric mechanism in protein-protein interactions. The identification of residues, with key role in the stability of PPIs, has a huge potential in the development of new drugs, which would be designed to target not only PPIs but also residues localized in allosteric regions of supramolecular complexes.

  4. Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.

  5. Protein-protein interaction analysis in single microfluidic droplets using FRET and fluorescence lifetime detection.

    Science.gov (United States)

    Benz, Christian; Retzbach, Heiko; Nagl, Stefan; Belder, Detlev

    2013-07-21

    Herein, we demonstrate the feasibility of a protein-protein interaction analysis and reaction progress monitoring in microfluidic droplets using FRET and microscopic fluorescence lifetime measurements. The fabrication of microdroplet chips using soft- and photolithographic techniques is demonstrated and the resulting chips reliably generate microdroplets of 630 pL and 6.71 nL at frequencies of 7.9 and 0.75 Hz, respectively. They were used for detection of protein-protein interactions in microdroplets using a model system of Alexa Fluor 488 labelled biotinylated BSA, Alexa Fluor 594 labelled streptavidin and unlabelled chicken egg white avidin. These microchips could be used for quantitative detection of avidin and streptavidin in microdroplets in direct and competitive assay formats with nanomolar detection limits, corresponding to attomole protein amounts. Four droplets were found to be sufficient for analytical determination. Fluorescence intensity ratio and fluorescence lifetime measurements were performed and compared for microdroplet FRET determination. A competitive on-chip binding assay for determination of unlabelled avidin using fluorescence lifetime detection could be performed within 135 s only.

  6. Novel protein-protein interaction family proteins involved in chloroplast movement response.

    Science.gov (United States)

    Kodama, Yutaka; Suetsugu, Noriyuki; Wada, Masamitsu

    2011-04-01

    To optimize photosynthetic activity, chloroplasts change their intracellular location in response to ambient light conditions; chloroplasts move toward low intensity light to maximize light capture, and away from high intensity light to avoid photodamage. Although several proteins have been reported to be involved in the chloroplast photorelocation movement response, any physical interaction among them was not found so far. We recently found a physical interaction between two plant-specific coiled-coil proteins, WEB1 (Weak Chloroplast Movement under Blue Light 1) and PMI2 (Plastid Movement Impaired 2), that were identified to regulate chloroplast movement velocity. Since the both coiled-coil regions of WEB1 and PMI2 were classified into an uncharacterized protein family having DUF827 (DUF: Domain of Unknown Function) domain, it was the first report that DUF827 proteins could mediate protein-protein interaction. In this mini-review article, we discuss regarding molecular function of WEB1 and PMI2, and also define a novel protein family composed of WEB1, PMI2 and WEB1/PMI2-like proteins for protein-protein interaction in land plants.

  7. Direct and Propagated Effects of Small Molecules on Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Laura C Cesa

    2015-08-01

    Full Text Available Networks of protein-protein interactions (PPIs link all aspects of cellular biology. Dysfunction in the assembly or dynamics of PPI networks is a hallmark of human disease, and as such, there is growing interest in the discovery of small molecules that either promote or inhibit PPIs. Protein-protein interactions were once considered undruggable because of their relatively large buried surface areas and difficult topologies. Despite these challenges, recent advances in chemical screening methodologies, combined with improvements in structural and computational biology have made some of these targets more tractable. In this review, we highlight developments that have opened the door to potent chemical modulators. We focus on how allostery is being used to produce surprisingly robust changes in PPIs, even for the most challenging targets. We also discuss how interfering with one PPI can propagate changes through the broader web of interactions. Through this analysis, it is becoming clear that a combination of direct and propagated effects on PPI networks is ultimately how small molecules re-shape biology.

  8. DUF581 is plant specific FCS-like zinc finger involved in protein-protein interaction.

    Directory of Open Access Journals (Sweden)

    Muhammed Jamsheer K

    Full Text Available Zinc fingers are a ubiquitous class of protein domain with considerable variation in structure and function. Zf-FCS is a highly diverged group of C2-C2 zinc finger which is present in animals, prokaryotes and viruses, but not in plants. In this study we identified that a plant specific domain of unknown function, DUF581 is a zf-FCS type zinc finger. Based on HMM-HMM comparison and signature motif similarity we named this domain as FCS-Like Zinc finger (FLZ domain. A genome wide survey identified that FLZ domain containing genes are bryophytic in origin and this gene family is expanded in spermatophytes. Expression analysis of selected FLZ gene family members of A. thaliana identified an overlapping expression pattern suggesting a possible redundancy in their function. Unlike the zf-FCS domain, the FLZ domain found to be highly conserved in sequence and structure. Using a combination of bioinformatic and protein-protein interaction tools, we identified that FLZ domain is involved in protein-protein interaction.

  9. PPLook: an automated data mining tool for protein-protein interaction

    Directory of Open Access Journals (Sweden)

    Xia Li

    2010-06-01

    Full Text Available Abstract Background Extracting and visualizing of protein-protein interaction (PPI from text literatures are a meaningful topic in protein science. It assists the identification of interactions among proteins. There is a lack of tools to extract PPI, visualize and classify the results. Results We developed a PPI search system, termed PPLook, which automatically extracts and visualizes protein-protein interaction (PPI from text. Given a query protein name, PPLook can search a dataset for other proteins interacting with it by using a keywords dictionary pattern-matching algorithm, and display the topological parameters, such as the number of nodes, edges, and connected components. The visualization component of PPLook enables us to view the interaction relationship among the proteins in a three-dimensional space based on the OpenGL graphics interface technology. PPLook can also provide the functions of selecting protein semantic class, counting the number of semantic class proteins which interact with query protein, counting the literature number of articles appearing the interaction relationship about the query protein. Moreover, PPLook provides heterogeneous search and a user-friendly graphical interface. Conclusions PPLook is an effective tool for biologists and biosystem developers who need to access PPI information from the literature. PPLook is freely available for non-commercial users at http://meta.usc.edu/softs/PPLook.

  10. A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

    Science.gov (United States)

    Melo, Rita; Fieldhouse, Robert; Melo, André; Correia, João D. G.; Cordeiro, Maria Natália D. S.; Gümüş, Zeynep H.; Costa, Joaquim; Bonvin, Alexandre M. J. J.; Moreira, Irina S.

    2016-01-01

    Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set. PMID:27472327

  11. iPPI-DB: an online database of modulators of protein-protein interactions.

    Science.gov (United States)

    Labbé, Céline M; Kuenemann, Mélaine A; Zarzycka, Barbara; Vriend, Gert; Nicolaes, Gerry A F; Lagorce, David; Miteva, Maria A; Villoutreix, Bruno O; Sperandio, Olivier

    2016-01-01

    In order to boost the identification of low-molecular-weight drugs on protein-protein interactions (PPI), it is essential to properly collect and annotate experimental data about successful examples. This provides the scientific community with the necessary information to derive trends about privileged physicochemical properties and chemotypes that maximize the likelihood of promoting a given chemical probe to the most advanced stages of development. To this end we have developed iPPI-DB (freely accessible at http://www.ippidb.cdithem.fr), a database that contains the structure, some physicochemical characteristics, the pharmacological data and the profile of the PPI targets of several hundreds modulators of protein-protein interactions. iPPI-DB is accessible through a web application and can be queried according to two general approaches: using physicochemical/pharmacological criteria; or by chemical similarity to a user-defined structure input. In both cases the results are displayed as a sortable and exportable datasheet with links to external databases such as Uniprot, PubMed. Furthermore each compound in the table has a link to an individual ID card that contains its physicochemical and pharmacological profile derived from iPPI-DB data. This includes information about its binding data, ligand and lipophilic efficiencies, location in the PPI chemical space, and importantly similarity with known drugs, and links to external databases like PubChem, and ChEMBL.

  12. Mapping protein-protein interaction by {sup 13}C'-detected heteronuclear NMR spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Bertini, Ivano, E-mail: ivanobertini@cerm.unifi.it; Felli, Isabella C. [University of Florence, Department of Chemistry (Italy); Gonnelli, Leonardo [University of Florence, Magnetic Resonance Center (CERM) (Italy); Pierattelli, Roberta [University of Florence, Department of Chemistry (Italy); Spyranti, Zinovia; Spyroulias, Georgios A. [University of Patras, Department of Pharmacy (Greece)

    2006-10-15

    The copper-mediated protein-protein interaction between yeast Atx1 and Ccc2 has been examined by protonless heteronuclear NMR and compared with the already available {sup 1}H-{sup 15}N HSQC information. The observed chemical shift variations are analyzed with respect to the actual solution structure, available through intermolecular NOEs. The advantage of using the CON-IPAP spectrum with respect to the {sup 1}H-{sup 15}N HSQC resides in the increased number of signals observed, including those of prolines. CBCACO-IPAP experiments allow us to focus on the interaction region and on side-chain carbonyls, while a newly designed CEN-IPAP experiment on side-chains of lysines. An attempt is made to rationalize the chemical shift variations on the basis of the structural data involving the interface between the proteins and the nearby regions. It is here proposed that protonless{sup 13}C direct-detection NMR is a useful complement to {sup 1}H based NMR spectroscopy for monitoring protein-protein and protein-ligand interactions.

  13. Phthalic Acid Chemical Probes Synthesized for Protein-Protein Interaction Analysis

    Directory of Open Access Journals (Sweden)

    Chin-Jen Wu

    2013-06-01

    Full Text Available Plasticizers are additives that are used to increase the flexibility of plastic during manufacturing. However, in injection molding processes, plasticizers cannot be generated with monomers because they can peel off from the plastics into the surrounding environment, water, or food, or become attached to skin. Among the various plasticizers that are used, 1,2-benzenedicarboxylic acid (phthalic acid is a typical precursor to generate phthalates. In addition, phthalic acid is a metabolite of diethylhexyl phthalate (DEHP. According to Gene_Ontology gene/protein database, phthalates can cause genital diseases, cardiotoxicity, hepatotoxicity, nephrotoxicity, etc. In this study, a silanized linker (3-aminopropyl triethoxyslane, APTES was deposited on silicon dioxides (SiO2 particles and phthalate chemical probes were manufactured from phthalic acid and APTES–SiO2. These probes could be used for detecting proteins that targeted phthalic acid and for protein-protein interactions. The phthalic acid chemical probes we produced were incubated with epithelioid cell lysates of normal rat kidney (NRK-52E cells to detect the interactions between phthalic acid and NRK-52E extracted proteins. These chemical probes interacted with a number of chaperones such as protein disulfide-isomerase A6, heat shock proteins, and Serpin H1. Ingenuity Pathways Analysis (IPA software showed that these chemical probes were a practical technique for protein-protein interaction analysis.

  14. Features of protein-protein interactions in two-component signaling deduced from genomic libraries.

    Science.gov (United States)

    White, Robert A; Szurmant, Hendrik; Hoch, James A; Hwa, Terence

    2007-01-01

    As more and more sequence data become available, new approaches for extracting information from these data become feasible. This chapter reports on one such method that has been applied to elucidate protein-protein interactions in bacterial two-component signaling pathways. The method identifies residues involved in the interaction through an analysis of over 2500 functionally coupled proteins and a precise determination of the substitutional constraints placed on one protein by its signaling mate. Once identified, a simple log-likelihood scoring procedure is applied to these residues to build a predictive tool for assigning signaling mates. The ability to apply this method is based on a proliferation of related domains within multiple organisms. Paralogous evolution through gene duplication and divergence of two-component systems has commonly resulted in tens of closely related interacting pairs within one organism with a roughly one-to-one correspondence between signal and response. This provides us with roughly an order of magnitude more protein pairs than there are unique, fully sequenced bacterial species. Consequently, this chapter serves as both a detailed exposition of the method that has provided more depth to our knowledge of bacterial signaling and a look ahead to what would be possible on a more widespread scale, that is, to protein-protein interactions that have only one example per genome, as the number of genomes increases by a factor of 10.

  15. A conserved patch of hydrophobic amino acids modulates Myb activity by mediating protein-protein interactions.

    Science.gov (United States)

    Dukare, Sandeep; Klempnauer, Karl-Heinz

    2016-07-01

    The transcription factor c-Myb plays a key role in the control of proliferation and differentiation in hematopoietic progenitor cells and has been implicated in the development of leukemia and certain non-hematopoietic tumors. c-Myb activity is highly dependent on the interaction with the coactivator p300 which is mediated by the transactivation domain of c-Myb and the KIX domain of p300. We have previously observed that conservative valine-to-isoleucine amino acid substitutions in a conserved stretch of hydrophobic amino acids have a profound effect on Myb activity. Here, we have explored the function of the hydrophobic region as a mediator of protein-protein interactions. We show that the hydrophobic region facilitates Myb self-interaction and binding of the histone acetyl transferase Tip60, a previously identified Myb interacting protein. We show that these interactions are affected by the valine-to-isoleucine amino acid substitutions and suppress Myb activity by interfering with the interaction of Myb and the KIX domain of p300. Taken together, our work identifies the hydrophobic region in the Myb transactivation domain as a binding site for homo- and heteromeric protein interactions and leads to a picture of the c-Myb transactivation domain as a composite protein binding region that facilitates interdependent protein-protein interactions of Myb with regulatory proteins.

  16. Prediction of Protein-protein Interactions on the Basis of Evolutionary Conservation of Protein Functions

    Directory of Open Access Journals (Sweden)

    Ekaterina Kotelnikova

    2007-01-01

    Full Text Available Motivation: Although a great deal of progress is being made in the development of fast and reliable experimental techniques to extract genome-wide networks of protein-protein and protein-DNA interactions, the sequencing of new genomes proceeds at an even faster rate. That is why there is a considerable need for reliable methods of in-silico prediction of protein interaction based solely on sequence similarity information and known interactions from well-studied organisms. This problem can be solved if a dependency exists between sequence similarity and the conservation of the proteins’ functions.Results: In this paper, we introduce a novel probabilistic method for prediction of protein-protein interactions using a new empirical probabilistic formula describing the loss of interactions between homologous proteins during the course of evolution. This formula describes an evolutional process quite similar to the process of the Earth’s population growth. In addition, our method favors predictions confi rmed by several interacting pairs over predictions coming from a single interacting pair. Our approach is useful in working with “noisy” data such as those coming from high-throughput experiments. We have generated predictions for fi ve “model” organisms: H. sapiens, D. melanogaster, C. elegans, A. thaliana, and S. cerevisiae and evaluated the quality of these predictions.

  17. Multiplexing oscillatory biochemical signals.

    Science.gov (United States)

    de Ronde, Wiet; ten Wolde, Pieter Rein

    2014-04-01

    In recent years it has been increasingly recognized that biochemical signals are not necessarily constant in time and that the temporal dynamics of a signal can be the information carrier. Moreover, it is now well established that the protein signaling network of living cells has a bow-tie structure and that components are often shared between different signaling pathways. Here we show by mathematical modeling that living cells can multiplex a constant and an oscillatory signal: they can transmit these two signals simultaneously through a common signaling pathway, and yet respond to them specifically and reliably. We find that information transmission is reduced not only by noise arising from the intrinsic stochasticity of biochemical reactions, but also by crosstalk between the different channels. Yet, under biologically relevant conditions more than 2 bits of information can be transmitted per channel, even when the two signals are transmitted simultaneously. These observations suggest that oscillatory signals are ideal for multiplexing signals.

  18. Biochemically responsive smart surface.

    Science.gov (United States)

    Rios, Fabian; Smirnov, Sergei

    2009-04-01

    A design of smart surfaces responsive to biochemical analytes is demonstrated in the example of mixed monolayers of biotin/fluorocarbon. The contact angle of aqueous solutions on such surfaces decreases upon streptavidin binding and can be used in detecting this protein. The specificity of the effect is confirmed by the lack of a contact angle change by streptavidin blocked with biotin and by bovine serum albumin.

  19. Protein-protein docking using region-based 3D Zernike descriptors

    Directory of Open Access Journals (Sweden)

    Sael Lee

    2009-12-01

    Full Text Available Abstract Background Protein-protein interactions are a pivotal component of many biological processes and mediate a variety of functions. Knowing the tertiary structure of a protein complex is therefore essential for understanding the interaction mechanism. However, experimental techniques to solve the structure of the complex are often found to be difficult. To this end, computational protein-protein docking approaches can provide a useful alternative to address this issue. Prediction of docking conformations relies on methods that effectively capture shape features of the participating proteins while giving due consideration to conformational changes that may occur. Results We present a novel protein docking algorithm based on the use of 3D Zernike descriptors as regional features of molecular shape. The key motivation of using these descriptors is their invariance to transformation, in addition to a compact representation of local surface shape characteristics. Docking decoys are generated using geometric hashing, which are then ranked by a scoring function that incorporates a buried surface area and a novel geometric complementarity term based on normals associated with the 3D Zernike shape description. Our docking algorithm was tested on both bound and unbound cases in the ZDOCK benchmark 2.0 dataset. In 74% of the bound docking predictions, our method was able to find a near-native solution (interface C-αRMSD ≤ 2.5 Å within the top 1000 ranks. For unbound docking, among the 60 complexes for which our algorithm returned at least one hit, 60% of the cases were ranked within the top 2000. Comparison with existing shape-based docking algorithms shows that our method has a better performance than the others in unbound docking while remaining competitive for bound docking cases. Conclusion We show for the first time that the 3D Zernike descriptors are adept in capturing shape complementarity at the protein-protein interface and useful for

  20. In vivo Biotinylation Based Method for the Study of Protein-Protein Proximity in Eukaryotic Cells

    Directory of Open Access Journals (Sweden)

    Arman Kulyyassov

    2014-01-01

    Full Text Available Introduction: The spatiotemporal order plays an important role in cell functioning and is affected in many pathologies such as cancer and neurodegenerative diseases. One of the ultimate goals of molecular biology is reconstruction of the spatiotemporal structure of a living cell at the molecular level. This task includes determination of proximities between different molecular components in the cell and monitoring their time- and physiological state-dependent changes. In many cases, proximity between macromolecules arises due to their interactions; however, the contribution of dynamic self-organization in generation of spatiotemporal order is emerging as another viable possibility. Specifically, in proteomics, this implies that the detection of protein-protein proximity is a more general task than gaining information about physical interactions between proteins, as it could detail aspects of spatial order in vivo that are challenging to reconstitute in binding experiments in vitro. Methods: In this work, we have developed a method of monitoring protein-protein proximity in vivo. For this purpose, the BirA was fused to one of the interaction partners, whereas the BAP was modified to make the detection of its biotinylation possible by mass spectrometry. Results: Using several experimental systems, we showed that the biotinylation is interaction dependent. In addition, we demonstrated that BAP domains with different primary amino acid structures and thus with different molecular weights can be used in the same experiment, providing the possibility of multiplexing. Alternatively to the changes in primary amino acid structure, the stable isotope format can also be used, providing another way to perform multiplexing experiments. Finally, we also demonstrated that our system could help to overcome another limitation of current methodologies to detect protein-protein proximity. For example, one can follow the state of a protein of interest at a defined

  1. Circulating antibodies to lung protein(s) in patients with cryptogenic fibrosing alveolitis.

    Science.gov (United States)

    Wallace, W. A.; Roberts, S. N.; Caldwell, H.; Thornton, E.; Greening, A. P.; Lamb, D.; Howie, S. E.

    1994-01-01

    BACKGROUND--It has been hypothesised that cryptogenic fibrosing alveolitis has an immunological pathogenesis mediated by T lymphocytes. It is, however, recognised that patients may show dysregulation of the humoral immune system and that the presence of large numbers of B lymphocytes in open lung biopsies may be associated with a poor prognosis. Evidence of a role for the humoral immune system in the pathogenesis of cryptogenic fibrosing alveolitis has been suggested, but attempts to demonstrate circulating immunoglobulin to antigen within the lung have been inconclusive. METHODS--Plasma samples from 22 patients with cryptogenic fibrosing alveolitis, 22 patients with sarcoidosis, and 17 healthy controls were screened by SDS-PAGE and Western blotting for the presence of autoantibodies to lung proteins derived from cryptogenic fibrosing alveolitis, sarcoid and control lung tissue, as well as four normal non-pulmonary tissues. Possible site(s) of target protein(s) within the lung tissue were identified by immunohistochemical examination using IgG purified from the plasma of six patients and two controls. RESULTS--Eighteen of the plasma samples from patients with cryptogenic fibrosing alveolitis had reactive IgG to lung protein(s) in the 70-90 kDa molecular weight range compared with five of 18 plasma samples from patients with sarcoidosis and one of 17 controls. Plasma from patients with cryptogenic fibrosing alveolitis recognised antigen(s) of the same molecular weight in control and sarcoid lung tissue, but not non-pulmonary tissues, with a similar frequency. Immunohistochemical staining of cryptogenic fibrosing alveolitis biopsy material using IgG purified from plasma samples from patients with cryptogenic fibrosing alveolitis, but not control samples, revealed fine linear positivity in the lung parenchyma in a pattern suggestive of reaction with alveolar lining cells. The pattern was cytoplasmic/membranous and not nuclear. CONCLUSIONS--Patients with cryptogenic fibrosing alveolitis have a high frequency of plasma IgG autoantibodies to protein(s) within lung tissue associated with alveolar lining cells. This is believed to be the site where immunological injury occurs in cryptogenic fibrosing alveolitis, but the significance of these antibodies to the aetiology and pathogenesis is as yet unclear. Images PMID:8202877

  2. Homology modelling of protein-protein complexes: a simple method and its possibilities and limitations

    Directory of Open Access Journals (Sweden)

    Simonson Thomas

    2008-10-01

    Full Text Available Abstract Background Structure-based computational methods are needed to help identify and characterize protein-protein complexes and their function. For individual proteins, the most successful technique is homology modelling. We investigate a simple extension of this technique to protein-protein complexes. We consider a large set of complexes of known structures, involving pairs of single-domain proteins. The complexes are compared with each other to establish their sequence and structural similarities and the relation between the two. Compared to earlier studies, a simpler dataset, a simpler structural alignment procedure, and an additional energy criterion are used. Next, we compare the Xray structures to models obtained by threading the native sequence onto other, homologous complexes. An elementary requirement for a successful energy function is to rank the native structure above any threaded structure. We use the DFIREβ energy function, whose quality and complexity are typical of the models used today. Finally, we compare near-native models to distinctly non-native models. Results If weakly stable complexes are excluded (defined by a binding energy cutoff, as well as a few unusual complexes, a simple homology principle holds: complexes that share more than 35% sequence identity share similar structures and interaction modes; this principle was less clearcut in earlier studies. The energy function was then tested for its ability to identify experimental structures among sets of decoys, produced by a simple threading procedure. On average, the experimental structure is ranked above 92% of the alternate structures. Thus, discrimination of the native structure is good but not perfect. The discrimination of near-native structures is fair. Typically, a single, alternate, non-native binding mode exists that has a native-like energy. Some of the associated failures may correspond to genuine, alternate binding modes and/or native complexes that

  3. Open source tool for prediction of genome wide protein-protein interaction network based on ortholog information

    Directory of Open Access Journals (Sweden)

    Pedamallu Chandra Sekhar

    2010-08-01

    Full Text Available Abstract Background Protein-protein interactions are crucially important for cellular processes. Knowledge of these interactions improves the understanding of cell cycle, metabolism, signaling, transport, and secretion. Information about interactions can hint at molecular causes of diseases, and can provide clues for new therapeutic approaches. Several (usually expensive and time consuming experimental methods can probe protein - protein interactions. Data sets, derived from such experiments make the development of prediction methods feasible, and make the creation of protein-protein interaction network predicting tools possible. Methods Here we report the development of a simple open source program module (OpenPPI_predictor that can generate a putative protein-protein interaction network for target genomes. This tool uses the orthologous interactome network data from a related, experimentally studied organism. Results Results from our predictions can be visualized using the Cytoscape visualization software, and can be piped to downstream processing algorithms. We have employed our program to predict protein-protein interaction network for the human parasite roundworm Brugia malayi, using interactome data from the free living nematode Caenorhabditis elegans. Availability The OpenPPI_predictor source code is available from http://tools.neb.com/~posfai/.

  4. Analysis and Ranking of Protein-Protein Docking Models Using Inter-Residue Contacts and Inter-Molecular Contact Maps

    KAUST Repository

    Oliva, Romina

    2015-07-01

    In view of the increasing interest both in inhibitors of protein-protein interactions and in protein drugs themselves, analysis of the three-dimensional structure of protein-protein complexes is assuming greater relevance in drug design. In the many cases where an experimental structure is not available, protein-protein docking becomes the method of choice for predicting the arrangement of the complex. However, reliably scoring protein-protein docking poses is still an unsolved problem. As a consequence, the screening of many docking models is usually required in the analysis step, to possibly single out the correct ones. Here, making use of exemplary cases, we review our recently introduced methods for the analysis of protein complex structures and for the scoring of protein docking poses, based on the use of inter-residue contacts and their visualization in inter-molecular contact maps. We also show that the ensemble of tools we developed can be used in the context of rational drug design targeting protein-protein interactions.

  5. Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets

    Directory of Open Access Journals (Sweden)

    Chen Bor-Sen

    2008-06-01

    Full Text Available Abstract Background Cancer is caused by genetic abnormalities, such as mutations of oncogenes or tumor suppressor genes, which alter downstream signal transduction pathways and protein-protein interactions. Comparisons of the interactions of proteins in cancerous and normal cells can shed light on the mechanisms of carcinogenesis. Results We constructed initial networks of protein-protein interactions involved in the apoptosis of cancerous and normal cells by use of two human yeast two-hybrid data sets and four online databases. Next, we applied a nonlinear stochastic model, maximum likelihood parameter estimation, and Akaike Information Criteria (AIC to eliminate false-positive protein-protein interactions in our initial protein interaction networks by use of microarray data. Comparisons of the networks of apoptosis in HeLa (human cervical carcinoma cells and in normal primary lung fibroblasts provided insight into the mechanism of apoptosis and allowed identification of potential drug targets. The potential targets include BCL2, caspase-3 and TP53. Our comparison of cancerous and normal cells also allowed derivation of several party hubs and date hubs in the human protein-protein interaction networks involved in caspase activation. Conclusion Our method allows identification of cancer-perturbed protein-protein interactions involved in apoptosis and identification of potential molecular targets for development of anti-cancer drugs.

  6. High-sensitivity real-time imaging of dual protein-protein interactions in living subjects using multicolor luciferases.

    Directory of Open Access Journals (Sweden)

    Naoki Hida

    Full Text Available Networks of protein-protein interactions play key roles in numerous important biological processes in living subjects. An effective methodology to assess protein-protein interactions in living cells of interest is protein-fragment complement assay (PCA. Particularly the assays using fluorescent proteins are powerful techniques, but they do not directly track interactions because of its irreversibility or the time for chromophore formation. By contrast, PCAs using bioluminescent proteins can overcome these drawbacks. We herein describe an imaging method for real-time analysis of protein-protein interactions using multicolor luciferases with different spectral characteristics. The sensitivity and signal-to-background ratio were improved considerably by developing a carboxy-terminal fragment engineered from a click beetle luciferase. We demonstrate its utility in spatiotemporal characterization of Smad1-Smad4 and Smad2-Smad4 interactions in early developing stages of a single living Xenopus laevis embryo. We also describe the value of this method by application of specific protein-protein interactions in cell cultures and living mice. This technique supports quantitative analyses and imaging of versatile protein-protein interactions with a selective luminescence wavelength in opaque or strongly auto-fluorescent living subjects.

  7. A Biochemical Double Slit

    Science.gov (United States)

    Kominis, Iannis

    2011-03-01

    Radical-ion-pair reactions, fundamental in photosynthesis and at the basis of the avian magnetic compass mechanism, have been recently shown to offer a rich playground for applying methods and concepts from quantum measurement/quantum information science. We will demonstrate that radical-ion-pair reactions are almost the exact analog of the optical double slit experiment, i.e. Nature has already engineered biochemical reactions performing the act of quantum interference. We will further elaborate on the non-trivial quantum effects pertaining in these reactions and the recent debate on their fundamental theoretical description that these effects have sparked.

  8. Improving protein-protein interaction article classification using biological domain knowledge.

    Science.gov (United States)

    Chen, Yifei; Guo, Hongjian; Liu, Feng; Manderick, Bernard

    2015-01-01

    Interaction Article Classification (IAC) is a specific text classification application in biological domain that tries to find out which articles describe Protein-Protein Interactions (PPIs) to help extract PPIs from biological literature more efficiently. However, the existing text representation and feature weighting schemes commonly used for text classification are not well suited for IAC. We capture and utilise biological domain knowledge, i.e. gene mentions also known as protein or gene names in the articles, to address the problem. We put forward a new gene mention order-based approach that highlights the important role of gene mentions to represent the texts. Furthermore, we also incorporate the information concerning gene mentions into a novel feature weighting scheme called Gene Mention-based Term Frequency (GMTF). By conducting experiments, we show that using the proposed representation and weighting schemes, our Interaction Article Classifier (IACer) performs better than other leading systems for the moment.

  9. Reconstruction of Protein-Protein Interaction Pathways by Mining Subject-Verb-Objects Intermediates

    CERN Document Server

    Ling, Maurice HT; Nicholas, Kevin R; Lin, Feng

    2007-01-01

    The exponential increase in publication rate of new articles is limiting access of researchers to relevant literature. This has prompted the use of text mining tools to extract key biological information. Previous studies have reported extensive modification of existing generic text processors to process biological text. However, this requirement for modification had not been examined. In this study, we have constructed Muscorian, using MontyLingua, a generic text processor. It uses a two-layered generalization-specialization paradigm previously proposed where text was generically processed to a suitable intermediate format before domain-specific data extraction techniques are applied at the specialization layer. Evaluation using a corpus and experts indicated 86-90% precision and approximately 30% recall in extracting protein-protein interactions, which was comparable to previous studies using either specialized biological text processing tools or modified existing tools. Our study had also demonstrated the ...

  10. Protein-protein interactions: a supra-structural phenomenon demanding trans-disciplinary biophysical approaches.

    Science.gov (United States)

    Byron, Olwyn; Vestergaard, Bente

    2015-12-01

    Responsive formation of protein:protein interaction (PPI) upon diverse stimuli is a fundament of cellular function. As a consequence, PPIs are complex, adaptive entities, and exist in structurally heterogeneous interplays defined by the energetic states of the free and complexed protomers. The biophysical and structural investigations of PPIs consequently demand hybrid approaches, implementing orthogonal methods and strategies for global data analysis. Currently, impressive developments in hardware and software within several methodologies define a new era for the biostructural community. Data can be obtained at increasing resolution, at relevant time-scales and under increasingly relevant experimental conditions, intricate data are interpreted reliably, and the questions posed and answered grow in complexity. With this review, highlights from the study of PPIs using a multitude of biophysical methods, are reported. The aim is to depict how the elucidation of the interplay of structures requires the interplay of methods.

  11. Role of -methyl-8-(alkoxy)quinolinium iodide in suppression of protein-protein interactions

    Indian Academy of Sciences (India)

    Bimlesh Ojha; Cirantan Kar; Gopal Das

    2013-03-01

    There is a great deal of interest in developing small molecule inhibitors of protein misfolding and aggregation due to a growing number of pathologic states known as amyloid disorders. In searching for alternative ways to reduce protein-protein interactions or to inhibit the amyloid formation, the inhibitory effects of cationic amphiphile viz. -methyl-8-(alkoxy)quinolinium iodide on aggregation behaviour of hen egg white lysozyme (HEWL) at alkaline pH has been studied. Even though the compounds did not protect native HEWL from conformational changes, they were effective in diminishing HEWL amyloid formation, delaying both nucleation and elongation phases. It is likely that strong binding in the HEWL compound complex, raises the activation energy barrier for protein misfolding and subsequent aggregation, thereby retarding the aggregation kinetics substantially.

  12. Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Baoman Wang

    2015-01-01

    Full Text Available Apoptosis is the process of programmed cell death (PCD that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

  13. A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction

    Directory of Open Access Journals (Sweden)

    Lei Hua

    2016-01-01

    Full Text Available The state-of-the-art methods for protein-protein interaction (PPI extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN and propose a shortest dependency path based CNN (sdpCNN model. The proposed method (1 only takes the sdp and word embedding as input and (2 could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task.

  14. A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction.

    Science.gov (United States)

    Hua, Lei; Quan, Chanqin

    2016-01-01

    The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task.

  15. Diversity-oriented synthetic strategy for developing a chemical modulator of protein-protein interaction

    Science.gov (United States)

    Kim, Jonghoon; Jung, Jinjoo; Koo, Jaeyoung; Cho, Wansang; Lee, Won Seok; Kim, Chanwoo; Park, Wonwoo; Park, Seung Bum

    2016-10-01

    Diversity-oriented synthesis (DOS) can provide a collection of diverse and complex drug-like small molecules, which is critical in the development of new chemical probes for biological research of undruggable targets. However, the design and synthesis of small-molecule libraries with improved biological relevance as well as maximized molecular diversity represent a key challenge. Herein, we employ functional group-pairing strategy for the DOS of a chemical library containing privileged substructures, pyrimidodiazepine or pyrimidine moieties, as chemical navigators towards unexplored bioactive chemical space. To validate the utility of this DOS library, we identify a new small-molecule inhibitor of leucyl-tRNA synthetase-RagD protein-protein interaction, which regulates the amino acid-dependent activation of mechanistic target of rapamycin complex 1 signalling pathway. This work highlights that privileged substructure-based DOS strategy can be a powerful research tool for the construction of drug-like compounds to address challenging biological targets.

  16. A protein-protein interaction map of the Trypanosoma brucei paraflagellar rod.

    Directory of Open Access Journals (Sweden)

    Sylvain Lacomble

    Full Text Available We have conducted a protein interaction study of components within a specific sub-compartment of a eukaryotic flagellum. The trypanosome flagellum contains a para-crystalline extra-axonemal structure termed the paraflagellar rod (PFR with around forty identified components. We have used a Gateway cloning approach coupled with yeast two-hybrid, RNAi and 2D DiGE to define a protein-protein interaction network taking place in this structure. We define two clusters of interactions; the first being characterised by two proteins with a shared domain which is not sufficient for maintaining the interaction. The other cohort is populated by eight proteins, a number of which possess a PFR domain and sub-populations of this network exhibit dependency relationships. Finally, we provide clues as to the structural organisation of the PFR at the molecular level. This multi-strand approach shows that protein interactome data can be generated for insoluble protein complexes.

  17. Prediction of Protein-Protein Interactions with Physicochemical Descriptors and Wavelet Transform via Random Forests.

    Science.gov (United States)

    Jia, Jianhua; Xiao, Xuan; Liu, Bingxiang

    2016-06-01

    Protein-protein interactions (PPIs) provide valuable insight into the inner workings of cells, and it is significant to study the network of PPIs. It is vitally important to develop an automated method as a high-throughput tool to timely predict PPIs. Based on the physicochemical descriptors, a protein was converted into several digital signals, and then wavelet transform was used to analyze them. With such a formulation frame to represent the samples of protein sequences, the random forests algorithm was adopted to conduct prediction. The results on a large-scale independent-test data set show that the proposed model can achieve a good performance with an accuracy value of about 0.86 and a geometric mean value of about 0.85. Therefore, it can be a usefully supplementary tool for PPI prediction. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI_RF.

  18. A Statistical Analysis of Protein-Protein Interaction with Knowledge-Based Potential at Residue Level

    Institute of Scientific and Technical Information of China (English)

    林巍; 孙飞; 饶子和

    2003-01-01

    Protein-protein recognition is an important step in biological processes, which still largely remains elusive.The inter-residue contact potential, CPij, describes the propensity of contact between two types of residue.In this study, several different CPij variants were examined with the objective of discriminating the binding potential of surface pairs.Using solvent mediated inter-molecule contact potential (SM-IMCPij), an evaluation model was deduced and tested.Using the evaluation model it was found that the SM-IMCPij gives a better performance than either residue mediated IMCPij(RM-IMCPij) or folding-residue contact potential (FCPij).The results suggest that the evaluation model provides a fast, effective, and discriminative method for the evaluation of proposed binding interfaces.

  19. Application of shotgun proteomics for discovery-driven protein-protein interaction.

    Science.gov (United States)

    Goto-Silva, Livia; Maliga, Zoltan; Slabicki, Mikolaj; Murillo, Jimmy Rodriguez; Junqueira, Magno

    2014-01-01

    Affinity purification of protein complexes and identification of co-purified proteins by mass spectrometry is a powerful method to discover novel protein-protein interactions. Application of this method to the study of biological systems often requires the ability to process a large number of samples. Hence, there is great need to generate proteomic workflows compatible with large-scale studies. The major goal of this protocol is to present a fast, reliable, and scalable method to characterize protein complexes by mass spectrometry to overcome the limitations of conventional geLC-MS/MS or MudPIT protocols. This method was successfully employed for the discovery and characterization of novel protein complexes in cultured yeast, mammalian cells, and mice.

  20. Small-molecule stabilization of protein-protein interactions: an underestimated concept in drug discovery?

    Science.gov (United States)

    Thiel, Philipp; Kaiser, Markus; Ottmann, Christian

    2012-02-27

    The modulation of protein-protein interactions (PPIs) has been recognized as one of the most challenging tasks in drug discovery. While their systematic development has long been considered as intractable, this view has changed over the last years, with the first drug candidates undergoing clinical studies. To date, the vast majority of PPI modulators are interaction inhibitors. However, in many biological contexts a prolonged lifespan of a PPI might be desirable, calling for the complementary approach of PPI stabilization. In fact, nature offers impressive examples of this concept and some PPI-stabilizing natural products have already found application as important drugs. Moreover, directed small-molecule stabilization has recently been demonstrated. Therefore, it is time to take a closer look at the constructive side of modulating PPIs. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Small-molecule modulators of 14-3-3 protein-protein interactions.

    Science.gov (United States)

    Ottmann, Christian

    2013-07-15

    14-3-3 Proteins are eukaryotic adapter proteins that regulate a plethora of physiological processes by binding to several hundred partner proteins. They play a role in biological activities as diverse as signal transduction, cell cycle regulation, apoptosis, host-pathogen interactions and metabolic control. As such, 14-3-3s are implicated in disease areas like cancer, neurodegeneration, diabetes, pulmonary disease, and obesity. Targeted modulation of 14-3-3 protein-protein interactions (PPIs) by small molecules is therefore an attractive concept for disease intervention. In recent years a number of examples of inhibitors and stabilizers of 14-3-3 PPIs have been reported promising a vivid future in chemical biology and drug development for this remarkable class of proteins. Copyright © 2012 Elsevier Ltd. All rights reserved.

  2. Dataset of integrin-linked kinase protein: Protein interactions in cardiomyocytes identified by mass spectrometry

    Directory of Open Access Journals (Sweden)

    Alexandra Traister

    2016-06-01

    Full Text Available Using hearts from mice overexpressing integrin linked kinase (ILK behind the cardiac specific promoter αMHC, we have performed immunoprecipitation and mass spectrometry to identify novel ILK protein:protein interactions that regulate cardiomyocyte activity and calcium flux. Integrin linked kinase complexes were captured from mouse heart lysates using a commercial antibody, with subsequent liquid chromatography tandem mass spectral analysis. Interacting partners were identified using the MASCOT server, and important interactions verified using reverse immunoprecipitation and mass spectrometry. All ILK interacting proteins were identified in a non-biased manner, and are stored in the ProteomeXchange Consortium via the PRIDE partner repository (reference ID PRIDE: http://www.ebi.ac.uk/pride/archive/projects/PXD001053. The functional role of identified ILK interactions in cardiomyocyte function and arrhythmia were subsequently confirmed in human iPSC-cardiomyocytes.

  3. Targeted in vivo inhibition of specific protein-protein interactions using recombinant antibodies.

    Directory of Open Access Journals (Sweden)

    Matej Zábrady

    Full Text Available With the growing availability of genomic sequence information, there is an increasing need for gene function analysis. Antibody-mediated "silencing" represents an intriguing alternative for the precise inhibition of a particular function of biomolecules. Here, we describe a method for selecting recombinant antibodies with a specific purpose in mind, which is to inhibit intrinsic protein-protein interactions in the cytosol of plant cells. Experimental procedures were designed for conveniently evaluating desired properties of recombinant antibodies in consecutive steps. Our selection method was successfully used to develop a recombinant antibody inhibiting the interaction of ARABIDOPSIS HISTIDINE PHOSPHOTRANSFER PROTEIN 3 with such of its upstream interaction partners as the receiver domain of CYTOKININ INDEPENDENT HISTIDINE KINASE 1. The specific down-regulation of the cytokinin signaling pathway in vivo demonstrates the validity of our approach. This selection method can serve as a prototype for developing unique recombinant antibodies able to interfere with virtually any biomolecule in the living cell.

  4. Protein-protein binding detection with nanoparticle photonic crystal enhanced microscopy (NP-PCEM).

    Science.gov (United States)

    Zhuo, Yue; Tian, Limei; Chen, Weili; Yu, Hojeong; Singamaneni, Srikanth; Cunningham, Brian T

    2014-01-01

    We demonstrate a novel microscopy-based biosensing approach that utilizes a photonic crystal (PC) surface to detect protein-protein binding with the functionalized nanoparticles as tags. This imaging approach utilizes the measurement of localized shifts in the resonant wavelength and resonant reflection magnitude from the PC biosensor in the presence of individual nanoparticles. Moreover, it substantially increases the sensitivity of the imaging approach through tunable localized surface plasmon resonant frequency of the nanoparticle matching with the resonance of the PC biosensor. Experimental demonstrations of photonic crystal enhanced microscopy (PCEM) imaging with single nanoparticle resolution are supported by Finite-Difference Time-Domain (FDTD) computer simulations. The ability to detect the surface adsorption of individual nanoparticles as tags offers a route to single molecule biosensing with photonic crystal biosensor in the future.

  5. ATTRACT and PTools: open source programs for protein-protein docking.

    Science.gov (United States)

    Schneider, Sebastian; Saladin, Adrien; Fiorucci, Sébastien; Prévost, Chantal; Zacharias, Martin

    2012-01-01

    The prediction of the structure of protein-protein complexes based on structures or structural models of isolated partners is of increasing importance for structural biology and bioinformatics. The ATTRACT program can be used to perform systematic docking searches based on docking energy minimization. It is part of the object-oriented PTools library written in Python and C++. The library contains various routines to manipulate protein structures, to prepare and perform docking searches as well as analyzing docking results. It also intended to facilitate further methodological developments in the area of macromolecular docking that can be easily integrated. Here, we describe the application of PTools to perform systematic docking searches and to analyze the results. In addition, the possibility to perform multi-component docking will also be presented.

  6. Information theory in systems biology. Part II: protein-protein interaction and signaling networks.

    Science.gov (United States)

    Mousavian, Zaynab; Díaz, José; Masoudi-Nejad, Ali

    2016-03-01

    By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Robustness of indispensable nodes in controlling protein-protein interaction network

    CERN Document Server

    Zhang, Xizhe; Yang, Yunyi

    2016-01-01

    Recently, the structural controllability theory has been introduced to analyze the Protein-Protein Interaction (PPI) network. The indispensable nodes, which their removal increase the number of driver nodes to control the network, are found essential in PPI network. However, the PPI network is far from complete and there may exist many false-positive or false-negative interactions, which promotes us to question: are these indispensable nodes robust to structural change? Here we systematically investigate the robustness of indispensable nodes of PPI network by removing and adding possible interactions. We found that the indispensable nodes are sensitive to the structural change and very few edges can change the type of many indispensable nodes. The finding may promote our understanding to the control principle of PPI network.

  8. Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

    Science.gov (United States)

    Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

    2017-01-01

    Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  9. Proteomic dissection of biological pathways/processes through profiling protein-protein interaction networks

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies. The precise determination of the specific composition of protein complexes, especially using scalable and high-throughput methods, represents a systematic approach toward revealing particular cellular biological functions. In this regard, the direct profiling protein-protein interactions (PPIs) represent an efficient way to dissect functional pathways for revealing novel protein functions. In this review, we illustrate the technological evolution for the large-scale and precise identification of PPIs toward higher physiologically relevant accuracy. These techniques aim at improving the efficiency of complex pull-down, the signal specificity and accuracy in distinguishing specific PPIs, and the accuracy of identifying physiological relevant PPIs. A newly developed streamline proteomic approach for mapping the binary relationship of PPIs in a protein complex is introduced.

  10. Quantitative analysis of protein-protein interactions by split firefly luciferase complementation in plant protoplasts.

    Science.gov (United States)

    Li, Jian-Feng; Zhang, Dandan

    2014-07-01

    This unit describes the split firefly luciferase complementation (SFLC) assay, a high-throughput quantitative method that can be used to investigate protein-protein interactions (PPIs) in plant mesophyll protoplasts. In SFLC, the two proteins to be tested for interaction are expressed as chimeric proteins, each fused to a different half of firefly luciferase. If the proteins interact, a functional luciferase can be transitorily reconstituted, and is detected using the cell-permeable substrate D-luciferin. An advantage of the SFLC assay is that dynamic changes in PPIs in a cell can be detected in a near real-time manner. Another advantage is the unusually high DNA co-transfection and protein expression efficiencies that can be achieved in plant protoplasts, thereby enhancing the throughput of the method.

  11. Regulation of dopamine transporter function by protein-protein interactions: new discoveries and methodological challenges

    DEFF Research Database (Denmark)

    Eriksen, Jacob; Jørgensen, Trine Nygaard; Gether, Ulrik

    2010-01-01

    The dopamine transporter (DAT) plays a key role in regulating dopaminergic signalling in the brain by mediating rapid clearance of dopamine from the synaptic clefts. The psychostimulatory actions of cocaine and amphetamine are primarily the result of a direct interaction of these compounds with DAT...... leading to attenuated dopamine clearance and for amphetamine even increased dopamine release. In the last decade, intensive efforts have been directed towards understanding the molecular and cellular mechanisms governing the activity and availability of DAT in the plasma membrane of the pre...... cells have also recently become available such as fluorescently tagged cocaine analogues and fluorescent substrates. Here we review the current knowledge about the role of protein-protein interactions in DAT regulation as well as we describe the most recent methodological developments that have been...

  12. Identification of Protein-Protein Interactions Involved in Pectin Biosynthesis in the golgi Apparatus

    DEFF Research Database (Denmark)

    Lund, Christian Have

    GALACTURONOSYLTRANSFERASE1 (GAUT1) and GAUT7 has beesn identified and is essential for pectin biosynthesis. Interestingly, GAUT1 has been shown to be proteolytic processed from its transmembrane anchor domain and its catalytic domain is retained by GAUT7, thus ensuring biosynthesis of HG in the Golgi apparatus. Many...... methods exist in identifying protein-protein interaction (PPI) but many of these are developed for other organisms than plants and are most applicable for PPI detection in other organelles than the Golgi apparatus where pectin biosynthesis occurs. In this work, different PPI detection methods are examined...... for their ability to detect PPI inside the Golgi lumen. The first method tested was the commercially available splitubiquitin system from Dualsystems Biotech AG. This was applied to test binary interactions between proteins involved in HG and Rhamnogalacturonan I (RG-I) biosynthesis (see Manuscript II...

  13. Prediction and systematic study of protein-protein interaction networks of Leptospira interrogans

    Institute of Scientific and Technical Information of China (English)

    SUN Jingchun; XU Jinlin; CAO Jianping; LIU Qi; GUO Xiaokui; SHI Tieliu; LI Yixue

    2006-01-01

    Leptospira interrogans serovar Lai is a pathogenic bacterium that causes a spirochetal zoonosis in humans and some animals. With its complete genome sequence available, it is possible to analyze protein-protein interactions from a whole- genome standpoint. Here we combine four recently developed computational approaches (gene fusion method, gene neighbor method, phylogenetic profiles method, and operon method) to predict protein-pro- tein interaction networks of Leptospira interrogans strain Lai. Through comprehensive analysis on in- teractions among proteins of motility and chemotaxis system, signal transduction, lipopolysaccaride bio- synthesis and a series of proteins related to adhesion and invasion, we provided information for further studying on its pathogenic mechanism. In addition, we also assigned 203 previously uncharacterized proteins with possible functions based on the known functions of its interacting partners. This work is helpful for further investigating L. interrogans strain Lai.

  14. Prediction of Protein-Protein Interaction Sites Based on Naive Bayes Classifier

    Directory of Open Access Journals (Sweden)

    Haijiang Geng

    2015-01-01

    Full Text Available Protein functions through interactions with other proteins and biomolecules and these interactions occur on the so-called interface residues of the protein sequences. Identifying interface residues makes us better understand the biological mechanism of protein interaction. Meanwhile, information about the interface residues contributes to the understanding of metabolic, signal transduction networks and indicates directions in drug designing. In recent years, researchers have focused on developing new computational methods for predicting protein interface residues. Here we creatively used a 181-dimension protein sequence feature vector as input to the Naive Bayes Classifier- (NBC- based method to predict interaction sites in protein-protein complexes interaction. The prediction of interaction sites in protein interactions is regarded as an amino acid residue binary classification problem by applying NBC with protein sequence features. Independent test results suggested that Naive Bayes Classifier-based method with the protein sequence features as input vectors performed well.

  15. From protein-protein interaction to therapy response: Molecular imaging of heat shock proteins

    Energy Technology Data Exchange (ETDEWEB)

    Niu Gang [Molecular Imaging Program at Stanford (MIPS), Department of Radiology and Bio-X Program, Stanford University School of Medicine, 1201 Welch Rd, P095, Stanford, CA 94305-5484 (United States); Chen Xiaoyuan [Molecular Imaging Program at Stanford (MIPS), Department of Radiology and Bio-X Program, Stanford University School of Medicine, 1201 Welch Rd, P095, Stanford, CA 94305-5484 (United States)], E-mail: shawchen@stanford.edu

    2009-05-15

    HSP70 promoter-driven gene therapy and inhibition of HSP90 activity with small molecule inhibitors are two shining points in a newly developed cohort of cancer treatment. For HSP70 promoters, high efficiency and heat inducibility within a localized region make it very attractive to clinical translation. The HSP90 inhibitors exhibit a broad spectrum of anticancer activities due to the downstream effects of HSP90 inhibition, which interfere with a wide range of signaling processes that are crucial for the malignant properties of cancer cells. In this review article, we summarize exciting applications of newly emerged molecular imaging techniques as they relate to HSP, including protein-protein interactions of HSP90 complexes, therapeutic response of tumors to HSP90 inhibitors, and HSP70 promoters-controlled gene therapy. In the HSPs context, molecular imaging is expected to play a vital role in promoting drug development and advancing individualized medicine.

  16. A second-generation protein-protein interaction network of Helicobacter pylori.

    Science.gov (United States)

    Häuser, Roman; Ceol, Arnaud; Rajagopala, Seesandra V; Mosca, Roberto; Siszler, Gabriella; Wermke, Nadja; Sikorski, Patricia; Schwarz, Frank; Schick, Matthias; Wuchty, Stefan; Aloy, Patrick; Uetz, Peter

    2014-05-01

    Helicobacter pylori infections cause gastric ulcers and play a major role in the development of gastric cancer. In 2001, the first protein interactome was published for this species, revealing over 1500 binary protein interactions resulting from 261 yeast two-hybrid screens. Here we roughly double the number of previously published interactions using an ORFeome-based, proteome-wide yeast two-hybrid screening strategy. We identified a total of 1515 protein-protein interactions, of which 1461 are new. The integration of all the interactions reported in H. pylori results in 3004 unique interactions that connect about 70% of its proteome. Excluding interactions of promiscuous proteins we derived from our new data a core network consisting of 908 interactions. We compared our data set to several other bacterial interactomes and experimentally benchmarked the conservation of interactions using 365 protein pairs (interologs) of E. coli of which one third turned out to be conserved in both species.

  17. Improving analytical methods for protein-protein interaction through implementation of chemically inducible dimerization

    DEFF Research Database (Denmark)

    Andersen, Tonni Grube; Nintemann, Sebastian; Marek, Magdalena

    2016-01-01

    into the widely used split ubiquitin-, bimolecular fluorescence complementation (BiFC)- and Forster resonance energy transfer (FRET)-based methods and investigated different protein-protein interactions in yeast and plants. We demonstrate the functionality of this concept by the analysis of weakly interacting......When investigating interactions between two proteins with complementary reporter tags in yeast two-hybrid or split GFP assays, it remains troublesome to discriminate true-from false-negative results and challenging to compare the level of interaction across experiments. This leads to decreased...... sensitivity and renders analysis of weak or transient interactions difficult to perform. In this work, we describe the development of reporters that can be chemically induced to dimerize independently of the investigated interactions and thus alleviate these issues. We incorporated our reporters...

  18. Influence of homology and node age on the growth of protein-protein interaction networks.

    Science.gov (United States)

    Bottinelli, Arianna; Bassetti, Bruno; Lagomarsino, Marco Cosentino; Gherardi, Marco

    2012-10-01

    Proteins participating in a protein-protein interaction network can be grouped into homology classes following their common ancestry. Proteins added to the network correspond to genes added to the classes, so the dynamics of the two objects are intrinsically linked. Here we first introduce a statistical model describing the joint growth of the network and the partitioning of nodes into classes, which is studied through a combined mean-field and simulation approach. We then employ this unified framework to address the specific issue of the age dependence of protein interactions through the definition of three different node wiring or divergence schemes. A comparison with empirical data indicates that an age-dependent divergence move is necessary in order to reproduce the basic topological observables together with the age correlation between interacting nodes visible in empirical data. We also discuss the possibility of nontrivial joint partition and topology observables.

  19. Quantitative Tagless Copurification: A Method to Validate and Identify Protein-Protein Interactions*

    Science.gov (United States)

    Shatsky, Maxim; Dong, Ming; Liu, Haichuan; Yang, Lee Lisheng; Choi, Megan; Singer, Mary E.; Geller, Jil T.; Fisher, Susan J.; Hall, Steven C.; Hazen, Terry C.; Brenner, Steven E.; Butland, Gareth; Jin, Jian; Witkowska, H. Ewa; Chandonia, John-Marc; Biggin, Mark D.

    2016-01-01

    Identifying protein-protein interactions (PPIs) at an acceptable false discovery rate (FDR) is challenging. Previously we identified several hundred PPIs from affinity purification - mass spectrometry (AP-MS) data for the bacteria Escherichia coli and Desulfovibrio vulgaris. These two interactomes have lower FDRs than any of the nine interactomes proposed previously for bacteria and are more enriched in PPIs validated by other data than the nine earlier interactomes. To more thoroughly determine the accuracy of ours or other interactomes and to discover further PPIs de novo, here we present a quantitative tagless method that employs iTRAQ MS to measure the copurification of endogenous proteins through orthogonal chromatography steps. 5273 fractions from a four-step fractionation of a D. vulgaris protein extract were assayed, resulting in the detection of 1242 proteins. Protein partners from our D. vulgaris and E. coli AP-MS interactomes copurify as frequently as pairs belonging to three benchmark data sets of well-characterized PPIs. In contrast, the protein pairs from the nine other bacterial interactomes copurify two- to 20-fold less often. We also identify 200 high confidence D. vulgaris PPIs based on tagless copurification and colocalization in the genome. These PPIs are as strongly validated by other data as our AP-MS interactomes and overlap with our AP-MS interactome for D.vulgaris within 3% of expectation, once FDRs and false negative rates are taken into account. Finally, we reanalyzed data from two quantitative tagless screens of human cell extracts. We estimate that the novel PPIs reported in these studies have an FDR of at least 85% and find that less than 7% of the novel PPIs identified in each screen overlap. Our results establish that a quantitative tagless method can be used to validate and identify PPIs, but that such data must be analyzed carefully to minimize the FDR. PMID:27099342

  20. Detection of protein-protein interactions in plants using bimolecular fluorescence complementation.

    Science.gov (United States)

    Bracha-Drori, Keren; Shichrur, Keren; Katz, Aviva; Oliva, Moran; Angelovici, Ruthie; Yalovsky, Shaul; Ohad, Nir

    2004-11-01

    Protein function is often mediated via formation of stable or transient complexes. Here we report the determination of protein-protein interactions in plants using bimolecular fluorescence complementation (BiFC). The yellow fluorescent protein (YFP) was split into two non-overlapping N-terminal (YN) and C-terminal (YC) fragments. Each fragment was cloned in-frame to a gene of interest, enabling expression of fusion proteins. To demonstrate the feasibility of BiFC in plants, two pairs of interacting proteins were utilized: (i) the alpha and beta subunits of the Arabidopsis protein farnesyltransferase (PFT), and (ii) the polycomb proteins, FERTILIZATION-INDEPENDENT ENDOSPERM (FIE) and MEDEA (MEA). Members of each protein pair were transiently co-expressed in leaf epidermal cells of Nicotiana benthamiana or Arabidopsis. Reconstitution of a fluorescing YFP chromophore occurred only when the inquest proteins interacted. No fluorescence was detected following co-expression of free non-fused YN and YC or non-interacting protein pairs. Yellow fluorescence was detected in the cytoplasm of cells that expressed PFT alpha and beta subunits, or in nuclei and cytoplasm of cells that expressed FIE and MEA. In vivo measurements of fluorescence spectra emitted from reconstituted YFPs were identical to that of a non-split YFP, confirming reconstitution of the chromophore. Expression of the inquest proteins was verified by immunoblot analysis using monoclonal antibodies directed against tags within the hybrid proteins. In addition, protein interactions were confirmed by immunoprecipitations. These results demonstrate that plant BiFC is a simple, reliable and relatively fast method for determining protein-protein interactions in plants.

  1. Piezo dispensed microarray of multivalent chelating thiols for dissecting complex protein-protein interactions.

    Science.gov (United States)

    Klenkar, Goran; Valiokas, Ramûnas; Lundström, Ingemar; Tinazli, Ali; Tampé, Robert; Piehler, Jacob; Liedberg, Bo

    2006-06-01

    The fabrication of a novel biochip, designed for dissection of multiprotein complex formation, is reported. An array of metal chelators has been produced by piezo dispensing of a bis-nitrilotriacetic acid (bis-NTA) thiol on evaporated gold thin films, prestructured with a microcontact printed grid of eicosanethiols. The bis-NTA thiol is mixed in various proportions with an inert, tri(ethylene glycol) hexadecane thiol, and the thickness and morphological homogeneity of the dispensed layers are characterized by imaging ellipsometry before and after back-filling with the same inert thiol and subsequent rinsing. It is found that the dispensed areas display a monotonic increase in thickness with increasing molar fraction of bis-NTA in the dispensing solution, and they are consistently a few Angströms thicker than those prepared at the same molar fraction by solution self-assembly under equilibrium-like conditions. The bulkiness of the bis-NTA tail group and the short period of time available for chemisorption and in-plane organization of the dispensed thiols are most likely responsible for the observed difference in thickness. Moreover, the functional properties of this biochip are demonstrated by studying multiple protein-protein interactions using imaging surface plasmon resonance. The subunits of the type I interferon receptor are immobilized as a composition array determined by the surface concentration of bis-NTA in the array elements. Ligand dissociation kinetics depends on the receptor surface concentration, which is ascribed to the formation of a ternary complex by simultaneous interaction of the ligand with the two receptor subunits. Thus, multiplexed monitoring of binding phenomena at various compositions (receptor densities) offers a powerful tool to dissect protein-protein interactions.

  2. Prediction of protein-protein interactions between viruses and human by an SVM model

    Directory of Open Access Journals (Sweden)

    Cui Guangyu

    2012-05-01

    Full Text Available Abstract Background Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species. Results We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV and hepatitis C virus (HCV, our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO annotations of proteins, we predicted new interactions between virus proteins and human proteins. Conclusions Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1 it enables a prediction model to achieve a better performance than other representations, (2 it generates feature vectors of fixed length regardless of the sequence length, and (3 the same representation is applicable to different types of proteins.

  3. Development of a multiplexed microfluidic proteomic reactor and its application for studying protein-protein interactions.

    Science.gov (United States)

    Tian, Ruijun; Hoa, Xuyen Dai; Lambert, Jean-Philippe; Pezacki, John Paul; Veres, Teodor; Figeys, Daniel

    2011-06-01

    Mass spectrometry-based proteomics techniques have been very successful for the identification and study of protein-protein interactions. Typically, immunopurification of protein complexes is conducted, followed by protein separation by gel electrophoresis and in-gel protein digestion, and finally, mass spectrometry is performed to identify the interacting partners. However, the manual processing of the samples is time-consuming and error-prone. Here, we developed a polymer-based microfluidic proteomic reactor aimed at the parallel analysis of minute amounts of protein samples obtained from immunoprecipitation. The design of the proteomic reactor allows for the simultaneous processing of multiple samples on the same devices. Each proteomic reactor on the device consists of SCX beads packed and restricted into a 1 cm microchannel by two integrated pillar frits. The device is fabricated using a combination of low-cost hard cyclic olefin copolymer thermoplastic and elastomeric thermoplastic materials (styrene/(ethylene/butylenes)/styrene) using rapid hot-embossing replication techniques with a polymer-based stamp. Three immunopurified protein samples are simultaneously captured, reduced, alkylated, and digested on the device within 2-3 h instead of the days required for the conventional protein-protein interaction studies. The limit of detection of the microfluidic proteomic reactor was shown to be lower than 2 ng of protein. Furthermore, the application of the microfluidic proteomic reactor was demonstrated for the simultaneous processing of the interactome of the histone variant Htz1 in wild-type yeast and in a swr1Δ yeast strain compared to an untagged control using a novel three-channel microfluidic proteomic reactor.

  4. Protein-protein interactions visualized by bimolecular fluorescence complementation in tobacco protoplasts and leaves.

    Science.gov (United States)

    Schweiger, Regina; Schwenkert, Serena

    2014-03-09

    Many proteins interact transiently with other proteins or are integrated into multi-protein complexes to perform their biological function. Bimolecular fluorescence complementation (BiFC) is an in vivo method to monitor such interactions in plant cells. In the presented protocol the investigated candidate proteins are fused to complementary halves of fluorescent proteins and the respective constructs are introduced into plant cells via agrobacterium-mediated transformation. Subsequently, the proteins are transiently expressed in tobacco leaves and the restored fluorescent signals can be detected with a confocal laser scanning microscope in the intact cells. This allows not only visualization of the interaction itself, but also the subcellular localization of the protein complexes can be determined. For this purpose, marker genes containing a fluorescent tag can be coexpressed along with the BiFC constructs, thus visualizing cellular structures such as the endoplasmic reticulum, mitochondria, the Golgi apparatus or the plasma membrane. The fluorescent signal can be monitored either directly in epidermal leaf cells or in single protoplasts, which can be easily isolated from the transformed tobacco leaves. BiFC is ideally suited to study protein-protein interactions in their natural surroundings within the living cell. However, it has to be considered that the expression has to be driven by strong promoters and that the interaction partners are modified due to fusion of the relatively large fluorescence tags, which might interfere with the interaction mechanism. Nevertheless, BiFC is an excellent complementary approach to other commonly applied methods investigating protein-protein interactions, such as coimmunoprecipitation, in vitro pull-down assays or yeast-two-hybrid experiments.

  5. Dynamics of protein-protein encounter: a Langevin equation approach with reaction patches.

    Science.gov (United States)

    Schluttig, Jakob; Alamanova, Denitsa; Helms, Volkhard; Schwarz, Ulrich S

    2008-10-21

    We study the formation of protein-protein encounter complexes with a Langevin equation approach that considers direct, steric, and thermal forces. As three model systems with distinctly different properties we consider the pairs barnase:barstar, cytochrome c-cytochrome c peroxidase, and p53:MDM2. In each case, proteins are modeled either as spherical particles, as dipolar spheres, or as collection of several small beads with one dipole. Spherical reaction patches are placed on the model proteins according to the known experimental structures of the protein complexes. In the computer simulations, concentration is varied by changing box size. Encounter is defined as overlap of the reaction patches and the corresponding first passage times are recorded together with the number of unsuccessful contacts before encounter. We find that encounter frequency scales linearly with protein concentration, thus proving that our microscopic model results in a well-defined macroscopic encounter rate. The number of unsuccessful contacts before encounter decreases with increasing encounter rate and ranges from 20 to 9000. For all three models, encounter rates are obtained within one order of magnitude of the experimentally measured association rates. Electrostatic steering enhances association up to 50-fold. If diffusional encounter is dominant (p53:MDM2) or similarly important as electrostatic steering (barnase:barstar), then encounter rate decreases with decreasing patch radius. More detailed modeling of protein shapes decreases encounter rates by 5%-95%. Our study shows how generic principles of protein-protein association are modulated by molecular features of the systems under consideration. Moreover it allows us to assess different coarse-graining strategies for the future modeling of the dynamics of large protein complexes.

  6. Analysis of Proteins, Protein Complexes, and Organellar Proteomes Using Sheathless Capillary Zone Electrophoresis - Native Mass Spectrometry

    Science.gov (United States)

    Belov, Arseniy M.; Viner, Rosa; Santos, Marcia R.; Horn, David M.; Bern, Marshall; Karger, Barry L.; Ivanov, Alexander R.

    2017-09-01

    Native mass spectrometry (MS) is a rapidly advancing field in the analysis of proteins, protein complexes, and macromolecular species of various types. The majority of native MS experiments reported to-date has been conducted using direct infusion of purified analytes into a mass spectrometer. In this study, capillary zone electrophoresis (CZE) was coupled online to Orbitrap mass spectrometers using a commercial sheathless interface to enable high-performance separation, identification, and structural characterization of limited amounts of purified proteins and protein complexes, the latter with preserved non-covalent associations under native conditions. The performance of both bare-fused silica and polyacrylamide-coated capillaries was assessed using mixtures of protein standards known to form non-covalent protein-protein and protein-ligand complexes. High-efficiency separation of native complexes is demonstrated using both capillary types, while the polyacrylamide neutral-coated capillary showed better reproducibility and higher efficiency for more complex samples. The platform was then evaluated for the determination of monoclonal antibody aggregation and for analysis of proteomes of limited complexity using a ribosomal isolate from E. coli. Native CZE-MS, using accurate single stage and tandem-MS measurements, enabled identification of proteoforms and non-covalent complexes at femtomole levels. This study demonstrates that native CZE-MS can serve as an orthogonal and complementary technique to conventional native MS methodologies with the advantages of low sample consumption, minimal sample processing and losses, and high throughput and sensitivity. This study presents a novel platform for analysis of ribosomes and other macromolecular complexes and organelles, with the potential for discovery of novel structural features defining cellular phenotypes (e.g., specialized ribosomes). [Figure not available: see fulltext.

  7. Intragenic suppressor of Osiaa23 revealed a conserved tryptophan residue crucial for protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Jun Ni

    Full Text Available The Auxin/Indole-3-Acetic Acid (Aux/IAA and Auxin Response Factor (ARF are two important families that play key roles in auxin signal transduction. Both of the families contain a similar carboxyl-terminal domain (Domain III/IV that facilitates interactions between these two families. In spite of the importance of protein-protein interactions among these transcription factors, the mechanisms involved in these interactions are largely unknown. In this study, we isolated six intragenic suppressors of an auxin insensitive mutant, Osiaa23. Among these suppressors, Osiaa23-R5 successfully rescued all the defects of the mutant. Sequence analysis revealed that an amino acid substitution occurred in the Tryptophan (W residue in Domain IV of Osiaa23. Yeast two-hybrid experiments showed that the mutation in Domain IV prevents the protein-protein interactions between Osiaa23 and OsARFs. Phylogenetic analysis revealed that the W residue is conserved in both OsIAAs and OsARFs. Next, we performed site-specific amino acid substitutions within Domain IV of OsARFs, and the conserved W in Domain IV was exchanged by Serine (S. The mutated OsARF(WSs can be released from the inhibition of Osiaa23 and maintain the transcriptional activities. Expression of OsARF(WSs in Osiaa23 mutant rescued different defects of the mutant. Our results suggest a previously unknown importance of Domain IV in both families and provide an indirect way to investigate functions of OsARFs.

  8. ABA Signaling in Guard Cells Entails a Dynamic Protein-Protein Interaction Relay from the PYL-RCAR Family Receptors to Ion Channels

    Institute of Scientific and Technical Information of China (English)

    Sung Chul Lee; Chae Woo Lim; Wenzhi Lan; Kai He; Sheng Luan

    2013-01-01

    Plant hormone abscisic acid (ABA) serves as an integrator of environmental stresses such as drought to trigger stomatal closure by regulating specific ion channels in guard cells.We previously reported that SLACl,an outward anion channel required for stomatal closure,was regulated via reversible protein phosphorylation events involving ABA signaling components,including protein phosphatase 2C members and a SnRK2-type kinase (OST1).In this study,we reconstituted the ABA signaling pathway as a protein-protein interaction relay from the PYL/RCAR-type receptors,to the PP2C-SnRK2 phosphatase-kinase pairs,to the ion channel SLACl.The ABA receptors interacted with and inhibited PP2C phosphatase activity against the SnRK2-type kinase,releasing active SnRK2 kinase to phosphorylate,and activate the SLACl channel,leading to reduced guard cell turgor and stomatal closure.Both yeast two-hybrid and bimolecular fluorescence complementation assays were used to verify the interactions among the components in the pathway.These biochemical assays demonstrated activity modifications of phosphatases and kinases by their interaction partners.The SLACl channel activity was used as an endpoint readout for the strength of the signaling pathway,depending on the presence of different combinations of signaling components.Further study using transgenic plants overexpressing one of the ABA receptors demonstrated that changing the relative level of interacting partners would change ABA sensitivity.

  9. Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

    DEFF Research Database (Denmark)

    Nandy, Subir Kumar; Jouhten, Paula; Nielsen, Jens

    2010-01-01

    proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives. RESULTS: Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient......-sensing and metabolic regulatory signal transduction pathways (STP) operating in Saccharomyces cerevisiae. The reconstructed STP network includes a full protein-protein interaction network including the key nodes Snf1, Tor1, Hog1 and Pka1. The network includes a total of 623 structural open reading frames (ORFs......) and for all the interactions between them (edges). The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner. CONCLUSIONS: The reported fully annotated interaction model serves as a platform for integrated systems biology studies...

  10. Biochemical Hypermedia: Galactose Metabolism.

    Directory of Open Access Journals (Sweden)

    J.K. Sugai

    2013-05-01

    Full Text Available Introduction: Animations of biochemical processes and virtual laboratory environments lead to true molecular simulations. The use of interactive software’s in education can improve cognitive capacity, better learning and, mainly, it makes information acquisition easier. Material and Methods: This work presents the development of a biochemical hypermedia to understanding of the galactose metabolism. It was developed with the help of concept maps, ISIS Draw, ADOBE Photoshop and FLASH MX Program. Results and Discussion: A step by step animation process shows the enzymatic reactions of galactose conversion to glucose-1-phosphate (to glycogen synthesis, glucose-6-phosphate (glycolysis intermediary, UDP-galactose (substrate to mucopolysaccharides synthesis and collagen’s glycosylation. There are navigation guide that allow scrolling the mouse over the names of the components of enzymatic reactions of via the metabolism of galactose. Thus, explanatory text box, chemical structures and animation of the actions of enzymes appear to navigator. Upon completion of the module, the user’s response to the proposed exercise can be checked immediately through text box with interactive content of the answer. Conclusion: This hypermedia was presented for undergraduate students (UFSC who revealed that it was extremely effective in promoting the understanding of the theme.

  11. Common and specific signatures of gene expression and protein-protein interactions in autoimmune diseases.

    Science.gov (United States)

    Tuller, T; Atar, S; Ruppin, E; Gurevich, M; Achiron, A

    2013-03-01

    The aim of this study is to understand intracellular regulatory mechanisms in peripheral blood mononuclear cells (PBMCs), which are either common to many autoimmune diseases or specific to some of them. We incorporated large-scale data such as protein-protein interactions, gene expression and demographical information of hundreds of patients and healthy subjects, related to six autoimmune diseases with available large-scale gene expression measurements: multiple sclerosis (MS), systemic lupus erythematosus (SLE), juvenile rheumatoid arthritis (JRA), Crohn's disease (CD), ulcerative colitis (UC) and type 1 diabetes (T1D). These data were analyzed concurrently by statistical and systems biology approaches tailored for this purpose. We found that chemokines such as CXCL1-3, 5, 6 and the interleukin (IL) IL8 tend to be differentially expressed in PBMCs of patients with the analyzed autoimmune diseases. In addition, the anti-apoptotic gene BCL3, interferon-γ (IFNG), and the vitamin D receptor (VDR) gene physically interact with significantly many genes that tend to be differentially expressed in PBMCs of patients with the analyzed autoimmune diseases. In general, similar cellular processes tend to be differentially expressed in PBMC in the analyzed autoimmune diseases. Specifically, the cellular processes related to cell proliferation (for example, epidermal growth factor, platelet-derived growth factor, nuclear factor-κB, Wnt/β-catenin signaling, stress-activated protein kinase c-Jun NH2-terminal kinase), inflammatory response (for example, interleukins IL2 and IL6, the cytokine granulocyte-macrophage colony-stimulating factor and the B-cell receptor), general signaling cascades (for example, mitogen-activated protein kinase, extracellular signal-regulated kinase, p38 and TRK) and apoptosis are activated in most of the analyzed autoimmune diseases. However, our results suggest that in each of the analyzed diseases, apoptosis and chemotaxis are activated via

  12. Theoretical studies of protein-protein and protein-DNA binding rates

    Science.gov (United States)

    Alsallaq, Ramzi A.

    Proteins are folded chains of amino acids. Some of the amino acids (e.g. Lys, Arg, His, Asp, and Glu) carry charges under physiological conditions. Proteins almost always function through binding to other proteins or ligands, for example barnase is a ribonuclease protein, found in the bacterium Bacillus amyloliquefaceus. Barnase degrades RNA by hydrolysis. For the bacterium to inhibit the potentially lethal action of Barnase within its own cell it co-produces another protein called barstar which binds quickly, and tightly, to barnase. The biological function of this binding is to block the active site of barnase. The speeds (rates) at which proteins associate are vital to many biological processes. They span a wide range (from less than 103 to 108 M-1s-1 ). Rates greater than ˜ 106 M -1s-1 are typically found to be manifestations of enhancements by long-range electrostatic interactions between the associating proteins. A different paradigm appears in the case of protein binding to DNA. The rate in this case is enhanced through attractive surface potential that effectively reduces the dimensionality of the available search space for the diffusing protein. This thesis presents computational and theoretical models on the rate of association of ligands/proteins to other proteins or DNA. For protein-protein association we present a general strategy for computing protein-protein rates of association. The main achievements of this strategy is the ability to obtain a stringent reaction criteria based on the landscape of short-range interactions between the associating proteins, and the ability to compute the effect of the electrostatic interactions on the rates of association accurately using the best known solvers for Poisson-Boltzmann equation presently available. For protein-DNA association we present a mathematical model for proteins targeting specific sites on a circular DNA topology. The main achievements are the realization that a linear DNA with reflecting ends

  13. Lanthanide-based imaging of protein-protein interactions in live cells.

    Science.gov (United States)

    Rajendran, Megha; Yapici, Engin; Miller, Lawrence W

    2014-02-17

    In order to deduce the molecular mechanisms of biological function, it is necessary to monitor changes in the subcellular location, activation, and interaction of proteins within living cells in real time. Förster resonance energy-transfer (FRET)-based biosensors that incorporate genetically encoded, fluorescent proteins permit high spatial resolution imaging of protein-protein interactions or protein conformational dynamics. However, a nonspecific fluorescence background often obscures small FRET signal changes, and intensity-based biosensor measurements require careful interpretation and several control experiments. These problems can be overcome by using lanthanide [Tb(III) or Eu(III)] complexes as donors and green fluorescent protein (GFP) or other conventional fluorophores as acceptors. Essential features of this approach are the long-lifetime (approximately milliseconds) luminescence of Tb(III) complexes and time-gated luminescence microscopy. This allows pulsed excitation, followed by a brief delay, which eliminates nonspecific fluorescence before the detection of Tb(III)-to-GFP emission. The challenges of intracellular delivery, selective protein labeling, and time-gated imaging of lanthanide luminescence are presented, and recent efforts to investigate the cellular uptake of lanthanide probes are reviewed. Data are presented showing that conjugation to arginine-rich, cell-penetrating peptides (CPPs) can be used as a general strategy for the cellular delivery of membrane-impermeable lanthanide complexes. A heterodimer of a luminescent Tb(III) complex, Lumi4, linked to trimethoprim and conjugated to nonaarginine via a reducible disulfide linker rapidly (∼10 min) translocates into the cytoplasm of Maden Darby canine kidney cells from the culture medium. With this reagent, the intracellular interaction between GFP fused to FK506 binding protein 12 (GFP-FKBP12) and the rapamycin binding domain of mTOR fused to Escherichia coli dihydrofolate reductase (FRB

  14. Construction of a protein-protein interaction network of Wilms' tumor and pathway prediction of molecular complexes.

    Science.gov (United States)

    Teng, W J; Zhou, C; Liu, L J; Cao, X J; Zhuang, J; Liu, G X; Sun, C G

    2016-05-23

    Wilms' tumor (WT), or nephroblastoma, is the most common malignant renal cancer that affects the pediatric population. Great progress has been achieved in the treatment of WT, but it cannot be cured at present. Nonetheless, a protein-protein interaction network of WT should provide some new ideas and methods. The purpose of this study was to analyze the protein-protein interaction network of WT. We screened the confirmed disease-related genes using the Online Mendelian Inheritance in Man database, created a protein-protein interaction network based on biological function in the Cytoscape software, and detected molecular complexes and relevant pathways that may be included in the network. The results showed that the protein-protein interaction network of WT contains 654 nodes, 1544 edges, and 5 molecular complexes. Among them, complex 1 is predicted to be related to the Jak-STAT signaling pathway, regulation of hematopoiesis by cytokines, cytokine-cytokine receptor interaction, cytokine and inflammatory responses, and hematopoietic cell lineage pathways. Molecular complex 4 shows a correlation of WT with colorectal cancer and the ErbB signaling pathway. The proposed method can provide the bioinformatic foundation for further elucidation of the mechanisms of WT development.

  15. Virtual screening and experimental validation reveal novel small-molecule inhibitors of 14-3-3 protein-protein interactions.

    Science.gov (United States)

    Thiel, Philipp; Röglin, Lars; Meissner, Nicole; Hennig, Sven; Kohlbacher, Oliver; Ottmann, Christian

    2013-10-04

    We report first non-covalent and exclusively extracellular inhibitors of 14-3-3 protein-protein interactions identified by virtual screening. Optimization by crystal structure analysis and in vitro binding assays yielded compounds capable of disrupting the interaction of 14-3-3σ with aminopeptidase N in a cellular assay.

  16. Utilizing Mechanistic Cross-Linking Technology to Study Protein-Protein Interactions: An Experiment Designed for an Undergraduate Biochemistry Lab

    Science.gov (United States)

    Finzel, Kara; Beld, Joris; Burkart, Michael D.; Charkoudian, Louise K.

    2017-01-01

    Over the past decade, mechanistic cross-linking probes have been used to study protein-protein interactions in natural product biosynthetic pathways. This approach is highly interdisciplinary, combining elements of protein biochemistry, organic chemistry, and computational docking. Herein, we described the development of an experiment to engage…

  17. An effective system for detecting protein-protein interaction based on in vivo cleavage by PPV NIa protease.

    Science.gov (United States)

    Zheng, Nuoyan; Huang, Xiahe; Yin, Bojiao; Wang, Dan; Xie, Qi

    2012-12-01

    Detection of protein-protein interaction can provide valuable information for investigating the biological function of proteins. The current methods that applied in protein-protein interaction, such as co-immunoprecipitation and pull down etc., often cause plenty of working time due to the burdensome cloning and purification procedures. Here we established a system that characterization of protein-protein interaction was accomplished by co-expression and simply purification of target proteins from one expression cassette within E. coli system. We modified pET vector into co-expression vector pInvivo which encoded PPV NIa protease, two cleavage site F and two multiple cloning sites that flanking cleavage sites. The target proteins (for example: protein A and protein B) were inserted at multiple cloning sites and translated into polyprotein in the order of MBP tag-protein A-site F-PPV NIa protease-site F-protein B-His(6) tag. PPV NIa protease carried out intracellular cleavage along expression, then led to the separation of polyprotein components, therefore, the interaction between protein A-protein B can be detected through one-step purification and analysis. Negative control for protein B was brought into this system for monitoring interaction specificity. We successfully employed this system to prove two cases of reported protien-protein interaction: RHA2a/ANAC and FTA/FTB. In conclusion, a convenient and efficient system has been successfully developed for detecting protein-protein interaction.

  18. Design and modular parallel synthesis of a MCR derived α-helix mimetic protein-protein interaction inhibitor scaffold

    NARCIS (Netherlands)

    Antuch, Walfrido; Menon, Sanjay; Chen, Quin-Zene; Lu, Yingchun; Sakamuri, Sukumar; Beck, Barbara; Schauer-Vukašinović, Vesna; Agarwal, Seema; Hess, Sibylle; Dömling, Alexander

    2006-01-01

    A terphenyl α-helix mimetic scaffold recognized to be capable of disrupting protein-protein interactions was structurally morphed into an easily amenable and versatile multicomponent reaction (MCR) backbone. The design, modular in-parallel library synthesis, initial cell based biological data, and p

  19. Binding specificity and in vivo targets of the EH domain, a novel protein-protein interaction module

    DEFF Research Database (Denmark)

    Salcini, A E; Confalonieri, S; Doria, M;

    1997-01-01

    EH is a recently identified protein-protein interaction domain found in the signal transducers Eps15 and Eps15R and several other proteins of yeast nematode. We show that EH domains from Eps15 and Eps15R bind in vitro to peptides containing an asparagine-proline-phenylalanine (NPF) motif. Direct...

  20. Interactome-Wide Prediction of Protein-Protein Binding Sites Reveals Effects of Protein Sequence Variation in Arabidopsis thaliana

    NARCIS (Netherlands)

    Valentim, F.L.; Neven, F.; Boyen, P.; Dijk, van A.D.J.

    2012-01-01

    The specificity of protein-protein interactions is encoded in those parts of the sequence that compose the binding interface. Therefore, understanding how changes in protein sequence influence interaction specificity, and possibly the phenotype, requires knowing the location of binding sites in thos

  1. Analysis of protein-protein interactions in the feline calicivirus replication complex.

    Science.gov (United States)

    Kaiser, William J; Chaudhry, Yasmin; Sosnovtsev, Stanislav V; Goodfellow, Ian G

    2006-02-01

    Caliciviruses are a major cause of gastroenteritis in humans and cause a wide variety of other diseases in animals. Here, the characterization of protein-protein interactions between the individual proteins of Feline calicivirus (FCV), a model system for other members of the family Caliciviridae, is reported. Using the yeast two-hybrid system combined with a number of other approaches, it is demonstrated that the p32 protein (the picornavirus 2B analogue) of FCV interacts with p39 (2C), p30 (3A) and p76 (3CD). The FCV protease/RNA polymerase (ProPol) p76 was found to form homo-oligomers, as well as to interact with VPg and ORF2, the region encoding the major capsid protein VP1. A weak interaction was also observed between p76 and the minor capsid protein encoded by ORF3 (VP2). ORF2 protein was found to interact with VPg, p76 and VP2. The potential roles of the interactions in calicivirus replication are discussed.

  2. Tree kernel-based protein-protein interaction extraction from biomedical literature.

    Science.gov (United States)

    Qian, Longhua; Zhou, Guodong

    2012-06-01

    There is a surge of research interest in protein-protein interaction (PPI) extraction from biomedical literature. While most of the state-of-the-art PPI extraction systems focus on dependency-based structured information, the rich structured information inherent in constituent parse trees has not been extensively explored for PPI extraction. In this paper, we propose a novel approach to tree kernel-based PPI extraction, where the tree representation generated from a constituent syntactic parser is further refined using the shortest dependency path between two proteins derived from a dependency parser. Specifically, all the constituent tree nodes associated with the nodes on the shortest dependency path are kept intact, while other nodes are removed safely to make the constituent tree concise and precise for PPI extraction. Compared with previously used constituent tree setups, our dependency-motivated constituent tree setup achieves the best results across five commonly used PPI corpora. Moreover, our tree kernel-based method outperforms other single kernel-based ones and performs comparably with some multiple kernel ones on the most commonly tested AIMed corpus.

  3. Inference of gene-phenotype associations via protein-protein interaction and orthology.

    Directory of Open Access Journals (Sweden)

    Panwen Wang

    Full Text Available One of the fundamental goals of genetics is to understand gene functions and their associated phenotypes. To achieve this goal, in this study we developed a computational algorithm that uses orthology and protein-protein interaction information to infer gene-phenotype associations for multiple species. Furthermore, we developed a web server that provides genome-wide phenotype inference for six species: fly, human, mouse, worm, yeast, and zebrafish. We evaluated our inference method by comparing the inferred results with known gene-phenotype associations. The high Area Under the Curve values suggest a significant performance of our method. By applying our method to two human representative diseases, Type 2 Diabetes and Breast Cancer, we demonstrated that our method is able to identify related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The web server can be used to infer functions and putative phenotypes of a gene along with the candidate genes of a phenotype, and thus aids in disease candidate gene discovery. Our web server is available at http://jjwanglab.org/PhenoPPIOrth.

  4. Dynamic protein-protein interaction subnetworks of lung cancer in cases with smoking history.

    Science.gov (United States)

    Yu, Wei; He, Li-Ran; Zhao, Yan-Chao; Chan, Man-Him; Zhang, Meng; He, Miao

    2013-02-01

    Smoking is the primary cause of lung cancer and is linked to 85% of lung cancer cases. However, how lung cancer develops in patients with smoking history remains unclear. Systems approaches that combine human protein-protein interaction (PPI) networks and gene expression data are superior to traditional methods. We performed these systems to determine the role that smoking plays in lung cancer development and used the support vector machine (SVM) model to predict PPIs. By defining expression variance (EV), we found 520 dynamic proteins (EV>0.4) using data from the Human Protein Reference Database and Gene Expression Omnibus Database, and built 7 dynamic PPI subnetworks of lung cancer in patients with smoking history. We also determined the primary functions of each subnetwork: signal transduction, apoptosis, and cell migration and adhesion for subnetwork A; cell-sustained angiogenesis for subnetwork B; apoptosis for subnetwork C; and, finally, signal transduction and cell replication and proliferation for subnetworks D-G. The probability distribution of the degree of dynamic protein and static protein differed, clearly showing that the dynamic proteins were not the core proteins which widely connected with their neighbor proteins. There were high correlations among the dynamic proteins, suggesting that the dynamic proteins tend to form specific dynamic modules. We also found that the dynamic proteins were only correlated with the expression of selected proteins but not all neighbor proteins when cancer occurred.

  5. SONAR Discovers RNA-Binding Proteins from Analysis of Large-Scale Protein-Protein Interactomes.

    Science.gov (United States)

    Brannan, Kristopher W; Jin, Wenhao; Huelga, Stephanie C; Banks, Charles A S; Gilmore, Joshua M; Florens, Laurence; Washburn, Michael P; Van Nostrand, Eric L; Pratt, Gabriel A; Schwinn, Marie K; Daniels, Danette L; Yeo, Gene W

    2016-10-20

    RNA metabolism is controlled by an expanding, yet incomplete, catalog of RNA-binding proteins (RBPs), many of which lack characterized RNA binding domains. Approaches to expand the RBP repertoire to discover non-canonical RBPs are currently needed. Here, HaloTag fusion pull down of 12 nuclear and cytoplasmic RBPs followed by quantitative mass spectrometry (MS) demonstrates that proteins interacting with multiple RBPs in an RNA-dependent manner are enriched for RBPs. This motivated SONAR, a computational approach that predicts RNA binding activity by analyzing large-scale affinity precipitation-MS protein-protein interactomes. Without relying on sequence or structure information, SONAR identifies 1,923 human, 489 fly, and 745 yeast RBPs, including over 100 human candidate RBPs that contain zinc finger domains. Enhanced CLIP confirms RNA binding activity and identifies transcriptome-wide RNA binding sites for SONAR-predicted RBPs, revealing unexpected RNA binding activity for disease-relevant proteins and DNA binding proteins.

  6. Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme

    Directory of Open Access Journals (Sweden)

    Moschopoulos Charalampos

    2011-06-01

    Full Text Available Abstract Background Recent technological advances applied to biology such as yeast-two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of protein interaction networks. These interaction networks represent a rich, yet noisy, source of data that could be used to extract meaningful information, such as protein complexes. Several interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. In this paper, we propose a novel weighting scheme and apply it to the S. cerevisiae interactome. Complex prediction rates are improved by up to 39%, depending on the clustering algorithm applied. Results We adopt a two step procedure. During the first step, by applying both novel and well established protein-protein interaction (PPI weighting methods, weights are introduced to the original interactome graph based on the confidence level that a given interaction is a true-positive one. The second step applies clustering using established algorithms in the field of graph theory, as well as two variations of Spectral clustering. The clustered interactome networks are also cross-validated against the confirmed protein complexes present in the MIPS database. Conclusions The results of our experimental work demonstrate that interactome graph weighting methods clearly improve the clustering results of several clustering algorithms. Moreover, our proposed weighting scheme outperforms other approaches of PPI graph weighting.

  7. The EED protein-protein interaction inhibitor A-395 inactivates the PRC2 complex.

    Science.gov (United States)

    He, Yupeng; Selvaraju, Sujatha; Curtin, Michael L; Jakob, Clarissa G; Zhu, Haizhong; Comess, Kenneth M; Shaw, Bailin; The, Juliana; Lima-Fernandes, Evelyne; Szewczyk, Magdalena M; Cheng, Dong; Klinge, Kelly L; Li, Huan-Qiu; Pliushchev, Marina; Algire, Mikkel A; Maag, David; Guo, Jun; Dietrich, Justin; Panchal, Sanjay C; Petros, Andrew M; Sweis, Ramzi F; Torrent, Maricel; Bigelow, Lance J; Senisterra, Guillermo; Li, Fengling; Kennedy, Steven; Wu, Qin; Osterling, Donald J; Lindley, David J; Gao, Wenqing; Galasinski, Scott; Barsyte-Lovejoy, Dalia; Vedadi, Masoud; Buchanan, Fritz G; Arrowsmith, Cheryl H; Chiang, Gary G; Sun, Chaohong; Pappano, William N

    2017-04-01

    Polycomb repressive complex 2 (PRC2) is a regulator of epigenetic states required for development and homeostasis. PRC2 trimethylates histone H3 at lysine 27 (H3K27me3), which leads to gene silencing, and is dysregulated in many cancers. The embryonic ectoderm development (EED) protein is an essential subunit of PRC2 that has both a scaffolding function and an H3K27me3-binding function. Here we report the identification of A-395, a potent antagonist of the H3K27me3 binding functions of EED. Structural studies demonstrate that A-395 binds to EED in the H3K27me3-binding pocket, thereby preventing allosteric activation of the catalytic activity of PRC2. Phenotypic effects observed in vitro and in vivo are similar to those of known PRC2 enzymatic inhibitors; however, A-395 retains potent activity against cell lines resistant to the catalytic inhibitors. A-395 represents a first-in-class antagonist of PRC2 protein-protein interactions (PPI) for use as a chemical probe to investigate the roles of EED-containing protein complexes.

  8. High throughput protein-protein interaction data: clues for the architecture of protein complexes

    Directory of Open Access Journals (Sweden)

    Pang Chi

    2008-11-01

    Full Text Available Abstract Background High-throughput techniques are becoming widely used to study protein-protein interactions and protein complexes on a proteome-wide scale. Here we have explored the potential of these techniques to accurately determine the constituent proteins of complexes and their architecture within the complex. Results Two-dimensional representations of the 19S and 20S proteasome, mediator, and SAGA complexes were generated and overlaid with high quality pairwise interaction data, core-module-attachment classifications from affinity purifications of complexes and predicted domain-domain interactions. Pairwise interaction data could accurately determine the members of each complex, but was unexpectedly poor at deciphering the topology of proteins in complexes. Core and module data from affinity purification studies were less useful for accurately defining the member proteins of these complexes. However, these data gave strong information on the spatial proximity of many proteins. Predicted domain-domain interactions provided some insight into the topology of proteins within complexes, but was affected by a lack of available structural data for the co-activator complexes and the presence of shared domains in paralogous proteins. Conclusion The constituent proteins of complexes are likely to be determined with accuracy by combining data from high-throughput techniques. The topology of some proteins in the complexes will be able to be clearly inferred. We finally suggest strategies that can be employed to use high throughput interaction data to define the membership and understand the architecture of proteins in novel complexes.

  9. Therapeutic design of peptide modulators of protein-protein interactions in membranes.

    Science.gov (United States)

    Stone, Tracy A; Deber, Charles M

    2017-04-01

    Membrane proteins play the central roles in a variety of cellular processes, ranging from nutrient uptake and signalling, to cell-cell communication. Their biological functions are directly related to how they fold and assemble; defects often lead to disease. Protein-protein interactions (PPIs) within the membrane are therefore of great interest as therapeutic targets. Here we review the progress in the application of membrane-insertable peptides for the disruption or stabilization of membrane-based PPIs. We describe the design and preparation of transmembrane peptide mimics; and of several categories of peptidomimetics used for study, including d-enantiomers, non-natural amino acids, peptoids, and β-peptides. Further aspects of the review describe modifications to membrane-insertable peptides, including lipidation and cyclization via hydrocarbon stapling. These approaches provide a pathway toward the development of metabolically stable, non-toxic, and efficacious peptide modulators of membrane-based PPIs. This article is part of a Special Issue entitled: Lipid order/lipid defects and lipid-control of protein activity edited by Dirk Schneider.

  10. C2 Domains as Protein-Protein Interaction Modules in the Ciliary Transition Zone

    Directory of Open Access Journals (Sweden)

    Kim Remans

    2014-07-01

    Full Text Available RPGR-interacting protein 1 (RPGRIP1 is mutated in the eye disease Leber congenital amaurosis (LCA and its structural homolog, RPGRIP1-like (RPGRIP1L, is mutated in many different ciliopathies. Both are multidomain proteins that are predicted to interact with retinitis pigmentosa G-protein regulator (RPGR. RPGR is mutated in X-linked retinitis pigmentosa and is located in photoreceptors and primary cilia. We solved the crystal structure of the complex between the RPGR-interacting domain (RID of RPGRIP1 and RPGR and demonstrate that RPGRIP1L binds to RPGR similarly. RPGRIP1 binding to RPGR affects the interaction with PDEδ, the cargo shuttling factor for prenylated ciliary proteins. RPGRIP1-RID is a C2 domain with a canonical β sandwich structure that does not bind Ca2+ and/or phospholipids and thus constitutes a unique type of protein-protein interaction module. Judging from the large number of C2 domains in most of the ciliary transition zone proteins identified thus far, the structure presented here seems to constitute a cilia-specific module that is present in multiprotein transition zone complexes.

  11. Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks.

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    Kyunghyun Park

    Full Text Available As pharmacodynamic drug-drug interactions (PD DDIs could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw.

  12. The polarity sub-network in the yeast network of protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Luca Paris

    2011-12-01

    Full Text Available Rare, but highly connected, hub proteins subdivide hierarchically global networks of interacting proteins into modular clusters. Most biological research, however, focuses on functionally defined sub-networks. Thus, it is important to know whether the sub-networks retain the same topology of the global networks, from which they derive. To address this issue, we have analyzed the protein-protein interaction sub-network that participates in the polarized growth of the budding yeast Saccharomyces cerevisiae and that is derived from the global network of this model organism. We have observed that, in contrast to global networks, the distribution of connectivity k (i.e., the number of interactions per protein does not follow a power law, but decays exponentially, which reflects the local absence of hub proteins. Nonetheless, far from being randomly organized, the polarity sub-network can be subdivided into functional modules. In addition, most non-hub connector proteins, besides ensuring communications among modules, are linked mutually and contribute to the formation of the polarisome, a structure that coordinates actin assembly with polarized growth. These findings imply that identifying critical proteins within sub-networks (e.g., for the aim of targeted therapy requires searching not only for hubs but also for key non-hub connectors, which might remain otherwise unnoticed due to their relatively low connectivity.

  13. Predicting protein-protein interactions from sequence using correlation coefficient and high-quality interaction dataset.

    Science.gov (United States)

    Shi, Ming-Guang; Xia, Jun-Feng; Li, Xue-Ling; Huang, De-Shuang

    2010-03-01

    Identifying protein-protein interactions (PPIs) is critical for understanding the cellular function of the proteins and the machinery of a proteome. Data of PPIs derived from high-throughput technologies are often incomplete and noisy. Therefore, it is important to develop computational methods and high-quality interaction dataset for predicting PPIs. A sequence-based method is proposed by combining correlation coefficient (CC) transformation and support vector machine (SVM). CC transformation not only adequately considers the neighboring effect of protein sequence but describes the level of CC between two protein sequences. A gold standard positives (interacting) dataset MIPS Core and a gold standard negatives (non-interacting) dataset GO-NEG of yeast Saccharomyces cerevisiae were mined to objectively evaluate the above method and attenuate the bias. The SVM model combined with CC transformation yielded the best performance with a high accuracy of 87.94% using gold standard positives and gold standard negatives datasets. The source code of MATLAB and the datasets are available on request under smgsmg@mail.ustc.edu.cn.

  14. Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering.

    Science.gov (United States)

    Yang, Lei; Tang, Xianglong

    2014-01-01

    Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

  15. Identifying protein complexes in protein-protein interaction networks by using clique seeds and graph entropy.

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    Chen, Bolin; Shi, Jinhong; Zhang, Shenggui; Wu, Fang-Xiang

    2013-01-01

    The identification of protein complexes plays a key role in understanding major cellular processes and biological functions. Various computational algorithms have been proposed to identify protein complexes from protein-protein interaction (PPI) networks. In this paper, we first introduce a new seed-selection strategy for seed-growth style algorithms. Cliques rather than individual vertices are employed as initial seeds. After that, a result-modification approach is proposed based on this seed-selection strategy. Predictions generated by higher order clique seeds are employed to modify results that are generated by lower order ones. The performance of this seed-selection strategy and the result-modification approach are tested by using the entropy-based algorithm, which is currently the best seed-growth style algorithm to detect protein complexes from PPI networks. In addition, we investigate four pairs of strategies for this algorithm in order to improve its accuracy. The numerical experiments are conducted on a Saccharomyces cerevisiae PPI network. The group of best predictions consists of 1711 clusters, with the average f-score at 0.68 after removing all similar and redundant clusters. We conclude that higher order clique seeds can generate predictions with higher accuracy and that our improved entropy-based algorithm outputs more reasonable predictions than the original one.

  16. Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.

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    Sriwastava, Brijesh K; Basu, Subhadip; Maulik, Ujjwal

    2015-01-01

    Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo.

  17. The role of exon shuffling in shaping protein-protein interaction networks

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    França Gustavo S

    2010-12-01

    Full Text Available Abstract Background Physical protein-protein interaction (PPI is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks. Results We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains, self-interacting (able to interact with another copy of themselves and abundant in the genomes presents a stronger signal for exon shuffling. Conclusions Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.

  18. Prediction of Protein-Protein Interaction By Metasample-Based Sparse Representation

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    Xiuquan Du

    2015-01-01

    Full Text Available Protein-protein interactions (PPIs play key roles in many cellular processes such as transcription regulation, cell metabolism, and endocrine function. Understanding these interactions takes a great promotion to the pathogenesis and treatment of various diseases. A large amount of data has been generated by experimental techniques; however, most of these data are usually incomplete or noisy, and the current biological experimental techniques are always very time-consuming and expensive. In this paper, we proposed a novel method (metasample-based sparse representation classification, MSRC for PPIs prediction. A group of metasamples are extracted from the original training samples and then use the l1-regularized least square method to express a new testing sample as the linear combination of these metasamples. PPIs prediction is achieved by using a discrimination function defined in the representation coefficients. The MSRC is applied to PPIs dataset; it achieves 84.9% sensitivity, and 94.55% specificity, which is slightly lower than support vector machine (SVM and much higher than naive Bayes (NB, neural networks (NN, and k-nearest neighbor (KNN. The result shows that the MSRC is efficient for PPIs prediction.

  19. Molecular tweezers modulate 14-3-3 protein-protein interactions.

    Science.gov (United States)

    Bier, David; Rose, Rolf; Bravo-Rodriguez, Kenny; Bartel, Maria; Ramirez-Anguita, Juan Manuel; Dutt, Som; Wilch, Constanze; Klärner, Frank-Gerrit; Sanchez-Garcia, Elsa; Schrader, Thomas; Ottmann, Christian

    2013-03-01

    Supramolecular chemistry has recently emerged as a promising way to modulate protein functions, but devising molecules that will interact with a protein in the desired manner is difficult as many competing interactions exist in a biological environment (with solvents, salts or different sites for the target biomolecule). We now show that lysine-specific molecular tweezers bind to a 14-3-3 adapter protein and modulate its interaction with partner proteins. The tweezers inhibit binding between the 14-3-3 protein and two partner proteins--a phosphorylated (C-Raf) protein and an unphosphorylated one (ExoS)--in a concentration-dependent manner. Protein crystallography shows that this effect arises from the binding of the tweezers to a single surface-exposed lysine (Lys214) of the 14-3-3 protein in the proximity of its central channel, which normally binds the partner proteins. A combination of structural analysis and computer simulations provides rules for the tweezers' binding preferences, thus allowing us to predict their influence on this type of protein-protein interactions.

  20. Stabilization of Protein-Protein Interactions in chemical biology and drug discovery.

    Science.gov (United States)

    Bier, David; Thiel, Philipp; Briels, Jeroen; Ottmann, Christian

    2015-10-01

    More than 300,000 Protein-Protein Interactions (PPIs) can be found in human cells. This number is significantly larger than the number of single proteins, which are the classical targets for pharmacological intervention. Hence, specific and potent modulation of PPIs by small, drug-like molecules would tremendously enlarge the "druggable genome" enabling novel ways of drug discovery for essentially every human disease. This strategy is especially promising in diseases with difficult targets like intrinsically disordered proteins or transcription factors, for example neurodegeneration or metabolic diseases. Whereas the potential of PPI modulation has been recognized in terms of the development of inhibitors that disrupt or prevent a binary protein complex, the opposite (or complementary) strategy to stabilize PPIs has not yet been realized in a systematic manner. This fact is rather surprising given the number of impressive natural product examples that confer their activity by stabilizing specific PPIs. In addition, in recent years more and more examples of synthetic molecules are being published that work as PPI stabilizers, despite the fact that in the majority they initially have not been designed as such. Here, we describe examples from both the natural products as well as the synthetic molecules advocating for a stronger consideration of the PPI stabilization approach in chemical biology and drug discovery. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. PPI finder: a mining tool for human protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Min He

    Full Text Available BACKGROUND: The exponential increase of published biomedical literature prompts the use of text mining tools to manage the information overload automatically. One of the most common applications is to mine protein-protein interactions (PPIs from PubMed abstracts. Currently, most tools in mining PPIs from literature are using co-occurrence-based approaches or rule-based approaches. Hybrid methods (frame-based approaches by combining these two methods may have better performance in predicting PPIs. However, the predicted PPIs from these methods are rarely evaluated by known PPI databases and co-occurred terms in Gene Ontology (GO database. METHODOLOGY/PRINCIPAL FINDINGS: We here developed a web-based tool, PPI Finder, to mine human PPIs from PubMed abstracts based on their co-occurrences and interaction words, followed by evidences in human PPI databases and shared terms in GO database. Only 28% of the co-occurred pairs in PubMed abstracts appeared in any of the commonly used human PPI databases (HPRD, BioGRID and BIND. On the other hand, of the known PPIs in HPRD, 69% showed co-occurrences in the literature, and 65% shared GO terms. CONCLUSIONS: PPI Finder provides a useful tool for biologists to uncover potential novel PPIs. It is freely accessible at http://liweilab.genetics.ac.cn/tm/.

  2. Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

    Directory of Open Access Journals (Sweden)

    Lei Yang

    2014-01-01

    Full Text Available Cliques (maximal complete subnets in protein-protein interaction (PPI network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

  3. Analysis of protein-protein interaction network in chronic obstructive pulmonary disease.

    Science.gov (United States)

    Yuan, Y P; Shi, Y H; Gu, W C

    2014-10-31

    Chronic obstructive pulmonary disease (COPD) is a growing cause of morbidity and mortality throughout the world. The purpose of our study was to uncover biomarkers and explore its pathogenic mechanisms at the molecular level. The gene expression profiles of COPD samples and normal controls were downloaded from Gene Expression Omnibus. Matlab was used for data preprocessing and SAM4.0 was applied to determine the differentially expressed genes (DEGs). Furthermore, a protein-protein interaction (PPI) network was constructed by mapping the DEGs into PPI data, and functional analysis of the network was conducted with BiNGO. A total of 348 DEGs and 765 interactive genes were identified. The hub genes were mainly involved in metabolic processes and ribosome biogenesis. Several genes related to COPD in the PPI network were found, including CAMK1D, ALB, KIT, and DDX3Y. In conclusion, CAMK1D, ALB, KIT, and DDX3Y were chosen as candidate genes, which have the potential to be biomarkers or candidate target molecules to apply in clinical diagnosis and treatment of COPD.

  4. Designing specific protein-protein interactions using computation, experimental library screening, or integrated methods.

    Science.gov (United States)

    Chen, T Scott; Keating, Amy E

    2012-07-01

    Given the importance of protein-protein interactions for nearly all biological processes, the design of protein affinity reagents for use in research, diagnosis or therapy is an important endeavor. Engineered proteins would ideally have high specificities for their intended targets, but achieving interaction specificity by design can be challenging. There are two major approaches to protein design or redesign. Most commonly, proteins and peptides are engineered using experimental library screening and/or in vitro evolution. An alternative approach involves using protein structure and computational modeling to rationally choose sequences predicted to have desirable properties. Computational design has successfully produced novel proteins with enhanced stability, desired interactions and enzymatic function. Here we review the strengths and limitations of experimental library screening and computational structure-based design, giving examples where these methods have been applied to designing protein interaction specificity. We highlight recent studies that demonstrate strategies for combining computational modeling with library screening. The computational methods provide focused libraries predicted to be enriched in sequences with the properties of interest. Such integrated approaches represent a promising way to increase the efficiency of protein design and to engineer complex functionality such as interaction specificity.

  5. Peptide inhibitors of the Keap1-Nrf2 protein-protein interaction.

    Science.gov (United States)

    Hancock, Rowena; Bertrand, Hélène C; Tsujita, Tadayuki; Naz, Shama; El-Bakry, Ayman; Laoruchupong, Jitnueng; Hayes, John D; Wells, Geoff

    2012-01-15

    Disruption of the interaction between the ubiquitination facilitator protein Keap1 and the cap'n'collar basic-region leucine-zipper transcription factor Nrf2 is a potential strategy to enhance expression of antioxidant and free radical detoxification gene products regulated by Nrf2. Agents that disrupt this protein-protein interaction may be useful pharmacological probes and future cancer-chemopreventive agents. We describe the structure-activity relationships for a series of peptides based upon regions of the Nrf2 Neh2 domain, of varying length and sequence, that interact with the Keap1 Kelch domain and disrupt the interaction with Nrf2. We have also investigated sequestosome-1 (p62) and prothymosin-α sequences that have been reported to interact with Keap1. To achieve this we have developed a high-throughput fluorescence polarization (FP) assay to screen inhibitors. In addition to screening synthetic peptides, we have used a phage display library approach to identify putative peptide ligands with non-native sequence motifs. Candidate peptides from the phage display library screening protocol were evaluated in the FP assay to quantify their binding activity. Hybrid peptides based upon the Nrf2 "ETGE" motif and the sequestosome-1 "Keap1-interaction region" have superior binding activity compared to either native peptide alone.

  6. Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

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    Thomas Wallach

    2013-03-01

    Full Text Available Essentially all biological processes depend on protein-protein interactions (PPIs. Timing of such interactions is crucial for regulatory function. Although circadian (~24-hour clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc. contributing to temporal organization of cellular physiology in an unprecedented manner.

  7. Large-Scale Protein-Protein Interactions Detection by Integrating Big Biosensing Data with Computational Model

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    Zhu-Hong You

    2014-01-01

    Full Text Available Protein-protein interactions are the basis of biological functions, and studying these interactions on a molecular level is of crucial importance for understanding the functionality of a living cell. During the past decade, biosensors have emerged as an important tool for the high-throughput identification of proteins and their interactions. However, the high-throughput experimental methods for identifying PPIs are both time-consuming and expensive. On the other hand, high-throughput PPI data are often associated with high false-positive and high false-negative rates. Targeting at these problems, we propose a method for PPI detection by integrating biosensor-based PPI data with a novel computational model. This method was developed based on the algorithm of extreme learning machine combined with a novel representation of protein sequence descriptor. When performed on the large-scale human protein interaction dataset, the proposed method achieved 84.8% prediction accuracy with 84.08% sensitivity at the specificity of 85.53%. We conducted more extensive experiments to compare the proposed method with the state-of-the-art techniques, support vector machine. The achieved results demonstrate that our approach is very promising for detecting new PPIs, and it can be a helpful supplement for biosensor-based PPI data detection.

  8. Functional organization and its implication in evolution of the human protein-protein interaction network

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    Zhao Yiqiang

    2012-04-01

    Full Text Available Abstract Background Based on the distinguishing properties of protein-protein interaction networks such as power-law degree distribution and modularity structure, several stochastic models for the evolution of these networks have been purposed, motivated by the idea that a validated model should reproduce similar topological properties of the empirical network. However, being able to capture topological properties does not necessarily mean it correctly reproduces how networks emerge and evolve. More importantly, there is already evidence suggesting functional organization and significance of these networks. The current stochastic models of evolution, however, grow the network without consideration for biological function and natural selection. Results To test whether protein interaction networks are functionally organized and their impacts on the evolution of these networks, we analyzed their evolution at both the topological and functional level. We find that the human network is shown to be functionally organized, and its function evolves with the topological properties of the network. Our analysis suggests that function most likely affects local modularity of the network. Consistently, we further found that the topological unit is also the functional unit of the network. Conclusion We have demonstrated functional organization of a protein interaction network. Given our observations, we suggest that its significance should not be overlooked when studying network evolution.

  9. Simulated evolution of protein-protein interaction networks with realistic topology.

    Science.gov (United States)

    Peterson, G Jack; Pressé, Steve; Peterson, Kristin S; Dill, Ken A

    2012-01-01

    We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein's neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution.

  10. Stabilization of collagen through bioconversion: An insight in protein-protein interaction.

    Science.gov (United States)

    Usharani, Nagarajan; Jayakumar, Gladstone Christopher; Kanth, Swarna Vinodh; Rao, Jonnalagadda Raghava

    2014-08-01

    Collagen is a natural protein, which is used as a vital biomaterial in tissue engineering. The major concern about native collagen is lack of its thermal stability and weak resistance to proteolytic degradation. In this scenario, the crosslinking compounds used for stabilization of collagen are mostly of chemical nature and exhibit toxicity. The enzyme mediated crosslinking of collagen provides a novel alternative, nontoxic method for stabilization. In this study, aldehyde forming enzyme (AFE) is used in the bioconversion of hydroxylmethyl groups of collagen to formyl groups that results in the formation of peptidyl aldehyde. The resulted peptidyl aldehyde interacts with bipolar ions of basic amino acid residues of collagen. Further interaction leads to the formation of conjugated double bonds (aldol condensation involving the aldehyde group of peptidyl aldehyde) within the collagen. The enzyme modified collagen matrices have shown an increase in the denaturation temperature, when compared with native collagen. Enzyme modified collagen membranes exhibit resistance toward collagenolytic activity. Moreover, they exhibited a nontoxic nature. The catalytic activity of AFE on collagen as a substrate establishes an efficient modification, which enhances the structural stability of collagen. This finds new avenues in the context of protein-protein stabilization and discovers paramount application in tissue engineering.

  11. Protein-protein-interaction Network Organization of the Hypusine Modification System*

    Science.gov (United States)

    Sievert, Henning; Venz, Simone; Platas-Barradas, Oscar; Dhople, Vishnu M.; Schaletzky, Martin; Nagel, Claus-Henning; Braig, Melanie; Preukschas, Michael; Pällmann, Nora; Bokemeyer, Carsten; Brümmendorf, Tim H.; Pörtner, Ralf; Walther, Reinhard; Duncan, Kent E.; Hauber, Joachim; Balabanov, Stefan

    2012-01-01

    Hypusine modification of eukaryotic initiation factor 5A (eIF-5A) represents a unique and highly specific post-translational modification with regulatory functions in cancer, diabetes, and infectious diseases. However, the specific cellular pathways that are influenced by the hypusine modification remain largely unknown. To globally characterize eIF-5A and hypusine-dependent pathways, we used an approach that combines large-scale bioreactor cell culture with tandem affinity purification and mass spectrometry: “bioreactor-TAP-MS/MS.” By applying this approach systematically to all four components of the hypusine modification system (eIF-5A1, eIF-5A2, DHS, and DOHH), we identified 248 interacting proteins as components of the cellular hypusine network, with diverse functions including regulation of translation, mRNA processing, DNA replication, and cell cycle regulation. Network analysis of this data set enabled us to provide a comprehensive overview of the protein-protein interaction landscape of the hypusine modification system. In addition, we validated the interaction of eIF-5A with some of the newly identified associated proteins in more detail. Our analysis has revealed numerous novel interactions, and thus provides a valuable resource for understanding how this crucial homeostatic signaling pathway affects different cellular functions. PMID:22888148

  12. Protein-protein-interaction network organization of the hypusine modification system.

    Science.gov (United States)

    Sievert, Henning; Venz, Simone; Platas-Barradas, Oscar; Dhople, Vishnu M; Schaletzky, Martin; Nagel, Claus-Henning; Braig, Melanie; Preukschas, Michael; Pällmann, Nora; Bokemeyer, Carsten; Brümmendorf, Tim H; Pörtner, Ralf; Walther, Reinhard; Duncan, Kent E; Hauber, Joachim; Balabanov, Stefan

    2012-11-01

    Hypusine modification of eukaryotic initiation factor 5A (eIF-5A) represents a unique and highly specific post-translational modification with regulatory functions in cancer, diabetes, and infectious diseases. However, the specific cellular pathways that are influenced by the hypusine modification remain largely unknown. To globally characterize eIF-5A and hypusine-dependent pathways, we used an approach that combines large-scale bioreactor cell culture with tandem affinity purification and mass spectrometry: "bioreactor-TAP-MS/MS." By applying this approach systematically to all four components of the hypusine modification system (eIF-5A1, eIF-5A2, DHS, and DOHH), we identified 248 interacting proteins as components of the cellular hypusine network, with diverse functions including regulation of translation, mRNA processing, DNA replication, and cell cycle regulation. Network analysis of this data set enabled us to provide a comprehensive overview of the protein-protein interaction landscape of the hypusine modification system. In addition, we validated the interaction of eIF-5A with some of the newly identified associated proteins in more detail. Our analysis has revealed numerous novel interactions, and thus provides a valuable resource for understanding how this crucial homeostatic signaling pathway affects different cellular functions.

  13. A least square method based model for identifying protein complexes in protein-protein interaction network.

    Science.gov (United States)

    Dai, Qiguo; Guo, Maozu; Guo, Yingjie; Liu, Xiaoyan; Liu, Yang; Teng, Zhixia

    2014-01-01

    Protein complex formed by a group of physical interacting proteins plays a crucial role in cell activities. Great effort has been made to computationally identify protein complexes from protein-protein interaction (PPI) network. However, the accuracy of the prediction is still far from being satisfactory, because the topological structures of protein complexes in the PPI network are too complicated. This paper proposes a novel optimization framework to detect complexes from PPI network, named PLSMC. The method is on the basis of the fact that if two proteins are in a common complex, they are likely to be interacting. PLSMC employs this relation to determine complexes by a penalized least squares method. PLSMC is applied to several public yeast PPI networks, and compared with several state-of-the-art methods. The results indicate that PLSMC outperforms other methods. In particular, complexes predicted by PLSMC can match known complexes with a higher accuracy than other methods. Furthermore, the predicted complexes have high functional homogeneity.

  14. Predictions of hot spot residues at protein-protein interfaces using support vector machines.

    Directory of Open Access Journals (Sweden)

    Stefano Lise

    Full Text Available Protein-protein interactions are critically dependent on just a few 'hot spot' residues at the interface. Hot spots make a dominant contribution to the free energy of binding and they can disrupt the interaction if mutated to alanine. Here, we present HSPred, a support vector machine(SVM-based method to predict hot spot residues, given the structure of a complex. HSPred represents an improvement over a previously described approach (Lise et al, BMC Bioinformatics 2009, 10:365. It achieves higher accuracy by treating separately predictions involving either an arginine or a glutamic acid residue. These are the amino acid types on which the original model did not perform well. We have therefore developed two additional SVM classifiers, specifically optimised for these cases. HSPred reaches an overall precision and recall respectively of 61% and 69%, which roughly corresponds to a 10% improvement. An implementation of the described method is available as a web server at http://bioinf.cs.ucl.ac.uk/hspred. It is free to non-commercial users.

  15. Classification of protein-protein interaction full-text documents using text and citation network features.

    Science.gov (United States)

    Kolchinsky, Artemy; Abi-Haidar, Alaa; Kaur, Jasleen; Hamed, Ahmed Abdeen; Rocha, Luis M

    2010-01-01

    We participated (as Team 9) in the Article Classification Task of the Biocreative II.5 Challenge: binary classification of full-text documents relevant for protein-protein interaction. We used two distinct classifiers for the online and offline challenges: 1) the lightweight Variable Trigonometric Threshold (VTT) linear classifier we successfully introduced in BioCreative 2 for binary classification of abstracts and 2) a novel Naive Bayes classifier using features from the citation network of the relevant literature. We supplemented the supplied training data with full-text documents from the MIPS database. The lightweight VTT classifier was very competitive in this new full-text scenario: it was a top-performing submission in this task, taking into account the rank product of the Area Under the interpolated precision and recall Curve, Accuracy, Balanced F-Score, and Matthew's Correlation Coefficient performance measures. The novel citation network classifier for the biomedical text mining domain, while not a top performing classifier in the challenge, performed above the central tendency of all submissions, and therefore indicates a promising new avenue to investigate further in bibliome informatics.

  16. Genome-wide protein-protein interactions and protein function exploration in cyanobacteria.

    Science.gov (United States)

    Lv, Qi; Ma, Weimin; Liu, Hui; Li, Jiang; Wang, Huan; Lu, Fang; Zhao, Chen; Shi, Tieliu

    2015-10-22

    Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and "interologs" in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria.

  17. PIE: an online prediction system for protein-protein interactions from text.

    Science.gov (United States)

    Kim, Sun; Shin, Soo-Yong; Lee, In-Hee; Kim, Soo-Jin; Sriram, Ram; Zhang, Byoung-Tak

    2008-07-01

    Protein-protein interaction (PPI) extraction has been an important research topic in bio-text mining area, since the PPI information is critical for understanding biological processes. However, there are very few open systems available on the Web and most of the systems focus on keyword searching based on predefined PPIs. PIE (Protein Interaction information Extraction system) is a configurable Web service to extract PPIs from literature, including user-provided papers as well as PubMed articles. After providing abstracts or papers, the prediction results are displayed in an easily readable form with essential, yet compact features. The PIE interface supports more features such as PDF file extraction, PubMed search tool and network communication, which are useful for biologists and bio-system developers. The PIE system utilizes natural language processing techniques and machine learning methodologies to predict PPI sentences, which results in high precision performance for Web users. PIE is freely available at http://bi.snu.ac.kr/pie/.

  18. Enabling large-scale design, synthesis and validation of small molecule protein-protein antagonists.

    Science.gov (United States)

    Koes, David; Khoury, Kareem; Huang, Yijun; Wang, Wei; Bista, Michal; Popowicz, Grzegorz M; Wolf, Siglinde; Holak, Tad A; Dömling, Alexander; Camacho, Carlos J

    2012-01-01

    Although there is no shortage of potential drug targets, there are only a handful known low-molecular-weight inhibitors of protein-protein interactions (PPIs). One problem is that current efforts are dominated by low-yield high-throughput screening, whose rigid framework is not suitable for the diverse chemotypes present in PPIs. Here, we developed a novel pharmacophore-based interactive screening technology that builds on the role anchor residues, or deeply buried hot spots, have in PPIs, and redesigns these entry points with anchor-biased virtual multicomponent reactions, delivering tens of millions of readily synthesizable novel compounds. Application of this approach to the MDM2/p53 cancer target led to high hit rates, resulting in a large and diverse set of confirmed inhibitors, and co-crystal structures validate the designed compounds. Our unique open-access technology promises to expand chemical space and the exploration of the human interactome by leveraging in-house small-scale assays and user-friendly chemistry to rationally design ligands for PPIs with known structure.

  19. Sequence-based prediction of protein protein interaction using a deep-learning algorithm.

    Science.gov (United States)

    Sun, Tanlin; Zhou, Bo; Lai, Luhua; Pei, Jianfeng

    2017-05-25

    Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.

  20. Mapping of protein-protein interactions within the DNA-dependent protein kinase complex.

    Science.gov (United States)

    Gell, D; Jackson, S P

    1999-01-01

    In mammalian cells, the Ku and DNA-dependent protein kinase catalytic subunit (DNA-PKcs) proteins are required for the correct and efficient repair of DNA double-strand breaks. Ku comprises two tightly-associated subunits of approximately 69 and approximately 83 kDa, which are termed Ku70 and Ku80 (or Ku86), respectively. Previously, a number of regions of both Ku subunits have been demonstrated to be involved in their interaction, but the molecular mechanism of this interaction remains unknown. We have identified a region in Ku70 (amino acid residues 449-578) and a region in Ku80 (residues 439-592) that participate in Ku subunit interaction. Sequence analysis reveals that these interaction regions share sequence homology and suggests that the Ku subunits are structurally related. On binding to a DNA double-strand break, Ku is able to interact with DNA-PKcs, but how this interaction is mediated has not been defined. We show that the extreme C-terminus of Ku80, specifically the final 12 amino acid residues, mediates a highly specific interaction with DNA-PKcs. Strikingly, these residues appear to be conserved only in Ku80 sequences from vertebrate organisms. These data suggest that Ku has evolved to become part of the DNA-PK holo-enzyme by acquisition of a protein-protein interaction motif at the C-terminus of Ku80. PMID:10446239

  1. Evolution versus "intelligent design": comparing the topology of protein-protein interaction networks to the Internet.

    Science.gov (United States)

    Yang, Q; Siganos, G; Faloutsos, M; Lonardi, S

    2006-01-01

    Recent research efforts have made available genome-wide, high-throughput protein-protein interaction (PPI) maps for several model organisms. This has enabled the systematic analysis of PPI networks, which has become one of the primary challenges for the system biology community. In this study, we attempt to understand better the topological structure of PPI networks by comparing them against man-made communication networks, and more specifically, the Internet. Our comparative study is based on a comprehensive set of graph metrics. Our results exhibit an interesting dichotomy. On the one hand, both networks share several macroscopic properties such as scale-free and small-world properties. On the other hand, the two networks exhibit significant topological differences, such as the cliqueishness of the highest degree nodes. We attribute these differences to the distinct design principles and constraints that both networks are assumed to satisfy. We speculate that the evolutionary constraints that favor the survivability and diversification are behind the building process of PPI networks, whereas the leading force in shaping the Internet topology is a decentralized optimization process geared towards efficient node communication.

  2. Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords

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    Shun Koyabu

    2015-01-01

    Full Text Available For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pair in each sentence has an interaction. Such a specific keyword that is directly related to interaction as “bind” or “interact” plays an important role for training classifiers. We call it a dominant keyword that affects the capability of the classifier. Although it is important to identify the dominant keywords, whether a keyword is dominant depends on the context in which it occurs. Therefore, we propose a method for predicting whether a keyword is dominant for each instance. In this method, a keyword that derives imbalanced classification results is tentatively assumed to be a dominant keyword initially. Then the classifiers are separately trained from the instance with and without the assumed dominant keywords. The validity of the assumed dominant keyword is evaluated based on the classification results of the generated classifiers. The assumption is updated by the evaluation result. Repeating this process increases the prediction accuracy of the dominant keyword. Our experimental results using five corpora show the effectiveness of our proposed method with dominant keyword prediction.

  3. PPISEARCHENGINE: gene ontology-based search for protein-protein interactions.

    Science.gov (United States)

    Park, Byungkyu; Cui, Guangyu; Lee, Hyunjin; Huang, De-Shuang; Han, Kyungsook

    2013-01-01

    This paper presents a new search engine called PPISearchEngine which finds protein-protein interactions (PPIs) using the gene ontology (GO) and the biological relations of proteins. For efficient retrieval of PPIs, each GO term is assigned a prime number and the relation between the terms is represented by the product of prime numbers. This representation is hidden from users but facilitates the search for the interactions of a query protein by unique prime factorisation of the number that represents the query protein. For a query protein, PPISearchEngine considers not only the GO term associated with the query protein but also the GO terms at the lower level than the GO term in the GO hierarchy, and finds all the interactions of the query protein which satisfy the search condition. In contrast, the standard keyword-matching or ID-matching search method cannot find the interactions of a protein unless the interactions involve a protein with explicit annotations. To the best of our knowledge, this search engine is the first method that can process queries like 'for protein p with GO [Formula: see text], find p's interaction partners with GO [Formula: see text]'. PPISearchEngine is freely available to academics at http://search.hpid.org/.

  4. Interplay between binding affinity and kinetics in protein-protein interactions.

    Science.gov (United States)

    Cao, Huaiqing; Huang, Yongqi; Liu, Zhirong

    2016-07-01

    To clarify the interplay between the binding affinity and kinetics of protein-protein interactions, and the possible role of intrinsically disordered proteins in such interactions, molecular simulations were carried out on 20 protein complexes. With bias potential and reweighting techniques, the free energy profiles were obtained under physiological affinities, which showed that the bound-state valley is deep with a barrier height of 12 - 33 RT. From the dependence of the affinity on interface interactions, the entropic contribution to the binding affinity is approximated to be proportional to the interface area. The extracted dissociation rates based on the Arrhenius law correlate reasonably well with the experimental values (Pearson correlation coefficient R = 0.79). For each protein complex, a linear free energy relationship between binding affinity and the dissociation rate was confirmed, but the distribution of the slopes for intrinsically disordered proteins showed no essential difference with that observed for ordered proteins. A comparison with protein folding was also performed. Proteins 2016; 84:920-933. © 2016 Wiley Periodicals, Inc.

  5. Protein-protein interaction network and subcellular localization of the Arabidopsis thaliana ESCRT machinery

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    Lynn eRichardson

    2011-06-01

    Full Text Available The Endosomal Sorting Complex Required for Transport (ESCRT consists of several multi-protein subcomplexes which assemble sequentially at the endosomal surface and function in multivesicular body (MVB biogenesis. While ESCRT has been relatively well characterized in yeasts and mammals, comparably little is known about ESCRT in plants. Here we explored the yeast two-hybrid protein interaction network and subcellular localization of the Arabidopsis thaliana ESCRT machinery. We show that Arabidopsis ESCRT interactome possess a number of protein-protein interactions that are either conserved in yeasts and mammals or distinct to plants. We show also that most of the Arabidopsis ESCRT proteins examined at least partially localize to MVBs in plant cells when ectopically expressed on their own or co-expressed with other interacting ESCRT proteins, and some also induce abnormal MVB phenotypes, consistent with their proposed functional roles in MVB biogenesis. Overall, our results help define the plant ESCRT machinery by highlighting both conserved and unique features when compared to ESCRT in other evolutionarily diverse organisms, providing a foundation for further exploration of ESCRT in plants.

  6. Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords.

    Science.gov (United States)

    Koyabu, Shun; Phan, Thi Thanh Thuy; Ohkawa, Takenao

    2015-01-01

    For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pair in each sentence has an interaction. Such a specific keyword that is directly related to interaction as "bind" or "interact" plays an important role for training classifiers. We call it a dominant keyword that affects the capability of the classifier. Although it is important to identify the dominant keywords, whether a keyword is dominant depends on the context in which it occurs. Therefore, we propose a method for predicting whether a keyword is dominant for each instance. In this method, a keyword that derives imbalanced classification results is tentatively assumed to be a dominant keyword initially. Then the classifiers are separately trained from the instance with and without the assumed dominant keywords. The validity of the assumed dominant keyword is evaluated based on the classification results of the generated classifiers. The assumption is updated by the evaluation result. Repeating this process increases the prediction accuracy of the dominant keyword. Our experimental results using five corpora show the effectiveness of our proposed method with dominant keyword prediction.

  7. A General System for Studying Protein-Protein Interactions in Gram-Negative Bacteria

    Energy Technology Data Exchange (ETDEWEB)

    Pelletier, Dale A [ORNL; Auberry, Deanna L [ORNL; Buchanan, Michelle V [ORNL; Cannon, Bill [Pacific Northwest National Laboratory (PNNL); Daly, Don S. [Pacific Northwest National Laboratory (PNNL); Doktycz, Mitchel John [ORNL; Foote, Linda J [ORNL; Hervey, IV, William Judson [ORNL; Hooker, Brian [Pacific Northwest National Laboratory (PNNL); Hurst, Gregory {Greg} B [ORNL; Kennel, Steve J [ORNL; Lankford, Patricia K [ORNL; Larimer, Frank W [ORNL; Lu, Tse-Yuan S [ORNL; McDonald, W Hayes [ORNL; McKeown, Catherine K [ORNL; Morrell-Falvey, Jennifer L [ORNL; Owens, Elizabeth T [ORNL; Schmoyer, Denise D [ORNL; Shah, Manesh B [ORNL; Wiley, Steven [Pacific Northwest National Laboratory (PNNL); Wang, Yisong [ORNL; Gilmore, Jason [Pacific Northwest National Laboratory (PNNL)

    2008-01-01

    Abstract One of the most promising methods for large-scale studies of protein interactions is isolation of an affinity-tagged protein with its in vivo interaction partners, followed by mass spectrometric identification of the copurified proteins. Previous studies have generated affinity-tagged proteins using genetic tools or cloning systems that are specific to a particular organism. To enable protein-protein interaction studies across a wider range of Gram-negative bacteria, we have developed a methodology based on expression of affinity-tagged bait proteins from a medium copy-number plasmid. This construct is based on a broad-host-range vector backbone (pBBR1MCS5). The vector has been modified to incorporate the Gateway DEST vector recombination region, to facilitate cloning and expression of fusion proteins bearing a variety of affinity, fluorescent, or other tags. We demonstrate this methodology by characterizing interactions among subunits of the DNA-dependent RNA polymerase complex in two metabolically versatile Gram-negative microbial species of environmental interest, Rhodopseudomonas palustris CGA010 and Shewanella oneidensis MR-1. Results compared favorably with those for both plasmid and chromosomally encoded affinity-tagged fusion proteins expressed in a model organism, Escherichia coli.

  8. Computational prediction of protein-protein interactions in Leishmania predicted proteomes.

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    Antonio M Rezende

    Full Text Available The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks

  9. The origins of the evolutionary signal used to predict protein-protein interactions

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    Swapna Lakshmipuram S

    2012-12-01

    Full Text Available Abstract Background The correlation of genetic distances between pairs of protein sequence alignments has been used to infer protein-protein interactions. It has been suggested that these correlations are based on the signal of co-evolution between interacting proteins. However, although mutations in different proteins associated with maintaining an interaction clearly occur (particularly in binding interfaces and neighbourhoods, many other factors contribute to correlated rates of sequence evolution. Proteins in the same genome are usually linked by shared evolutionary history and so it would be expected that there would be topological similarities in their phylogenetic trees, whether they are interacting or not. For this reason the underlying species tree is often corrected for. Moreover processes such as expression level, are known to effect evolutionary rates. However, it has been argued that the correlated rates of evolution used to predict protein interaction explicitly includes shared evolutionary history; here we test this hypothesis. Results In order to identify the evolutionary mechanisms giving rise to the correlations between interaction proteins, we use phylogenetic methods to distinguish similarities in tree topologies from similarities in genetic distances. We use a range of datasets of interacting and non-interacting proteins from Saccharomyces cerevisiae. We find that the signal of correlated evolution between interacting proteins is predominantly a result of shared evolutionary rates, rather than similarities in tree topology, independent of evolutionary divergence. Conclusions Since interacting proteins do not have tree topologies that are more similar than the control group of non-interacting proteins, it is likely that coevolution does not contribute much to, if any, of the observed correlations.

  10. Evaluation of liver cirrhosis and hepatocellular carcinoma using Protein-Protein Interaction Networks.

    Science.gov (United States)

    Ehsani Ardakani, Mohammad Javad; Safaei, Akram; Arefi Oskouie, Afsaneh; Haghparast, Hesam; Haghazali, Mehrdad; Mohaghegh Shalmani, Hamid; Peyvandi, Hassan; Naderi, Nosratollah; Zali, Mohammad Reza

    2016-12-01

    In the current study, we analysised only the articles that investigate serum proteome profile of cirrhosis patients or HCC patients versus healthy controls. Increased understanding of cancer biology has enabled identification of molecular events that lead to the discovery of numerous potential biomarkers in diseases. Protein-protein interaction networks is one of aspect that could elevate the understanding level of molecular events and protein connections that lead to the identification of genes and proteins associated with diseases. Gene expression data, including 63 gene or protein names for hepatocellular carcinoma and 29 gene or protein names for cirrhosis, were extracted from a number of previous investigations. The networks of related differentially expressed genes were explored using Cytoscape and the PPI analysis methods such as MCODE and ClueGO. Centrality and cluster screening identified hub genes, including APOE, TTR, CLU, and APOA1 in cirrhosis. CLU and APOE belong to the regulation of positive regulation of neurofibrillary tangle assembly. HP and APOE involved in cellular oxidant detoxification. C4B and C4BP belong to the complement activation, classical pathway and acute inflammation response pathway. Also, it was reported TTR, TFRC, VWF, CLU, A2M, APOA1, CKAP5, ZNF648, CASP8, and HSP27 as hubs in HCC. In HCC, these include A2M that are corresponding to platelet degranulation, humoral immune response, and negative regulation of immune effector process. CLU belong to the reverse cholesterol transport, platelet degranulation and human immune response. APOA1 corresponds to the reverse cholesterol transport, platelet degranulation and humoral immune response, as well as negative regulation of immune effector process pathway. In conclusion, this study suggests that there is a common molecular relationship between cirrhosis and hepatocellular cancer that may help with identification of target molecules for early treatment that is essential in cancer therapy.

  11. Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions.

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    Kuppuswamy, Usha; Ananthasubramanian, Seshan; Wang, Yanli; Balakrishnan, Narayanaswamy; Ganapathiraju, Madhavi K

    2014-04-03

    The number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown. We carried out Bayesian inference of Gene Ontology (GO) term annotations of genes by employing the directed acyclic graph structure of GO and the network of protein-protein interactions (PPIs). The approach is designed based on the fact that two proteins that interact biophysically would be in physical proximity of each other, would possess complementary molecular function, and play role in related biological processes. Predicted GO terms were ranked according to their relative association scores and the approach was evaluated quantitatively by plotting the precision versus recall values and F-scores (the harmonic mean of precision and recall) versus varying thresholds. Precisions of ~58% and ~ 40% for localization and functions respectively of proteins were determined at a threshold of ~30 (top 30 GO terms in the ranked list). Comparison with function prediction based on semantic similarity among nodes in an ontology and incorporation of those similarities in a k-nearest neighbor classifier confirmed that our results compared favorably. This approach was applied to predict the cellular component and molecular function GO terms of all human proteins that have interacting partners possessing at least one known GO annotation. The list of predictions is available at http://severus.dbmi.pitt.edu/engo/GOPRED.html. We present the algorithm, evaluations and the results of the computational predictions, especially for genes identified in

  12. Influence of degree correlations on network structure and stability in protein-protein interaction networks

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    Zimmer Ralf

    2007-08-01

    Full Text Available Abstract Background The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs. Results For each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks. Conclusion Considerable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree

  13. Development of a novel molecular sensor for imaging estrogen receptor-coactivator protein-protein interactions.

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    Madryn C Lake

    Full Text Available Anti-estrogens, in particular tissue selective anti-estrogens, have been the bedrock of adjuvant therapy for patients with estrogen receptor alpha (ERα positive breast cancer. Though current therapies have greatly enhanced patient prognosis, there continues to be an impetus for the development of improved anti-estrogens. ERα is a nuclear receptor transcription factor which activates gene expression through the recruitment of transcriptional coactivator proteins. The SRC family of coactivators, which includes AIB1, has been shown to be of particular importance for ERα mediated transcription. ERα-AIB1 interactions are indicative of gene expression and are inhibited by anti-estrogen treatment. We have exploited the interaction between ERα and AIB1 as a novel method for imaging ERα activity using a split luciferase molecular sensor. By producing a range of ERα ligand binding domain (ER-LBD and AIB1 nuclear receptor interacting domain (AIB-RID N- and C-terminal firefly luciferase fragment fusion proteins, constructs which exhibited more than a 10-fold increase in luciferase activity with E2 stimulation were identified. The specificity of the E2-stimulated luciferase activity to ERα-AIB1 interaction was validated through Y537S and L539/540A ER-LBD fusion protein mutants. The primed nature of the split luciferase assay allowed changes in ERα activity, with respect to the protein-protein interactions preceding transcription, to be assessed soon after drug treatment. The novel assay split luciferase detailed in this report enabled modulation of ERα activity to be sensitively imaged in vitro and in living subjects and potentially holds much promise for imaging the efficacy of novel ERα specific therapies.

  14. Adding protein context to the human protein-protein interaction network to reveal meaningful interactions.

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    Martin H Schaefer

    Full Text Available Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs, which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the

  15. A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature.

    Science.gov (United States)

    Tikk, Domonkos; Thomas, Philippe; Palaga, Peter; Hakenberg, Jörg; Leser, Ulf

    2010-07-01

    The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein-protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study shows that three

  16. Bioluminescent indicator for determining protein-protein interactions using intramolecular complementation of split click beetle luciferase.

    Science.gov (United States)

    Kim, Sung Bae; Otani, Yosuke; Umezawa, Yoshio; Tao, Hiroaki

    2007-07-01

    Click beetle luciferase (CBLuc) is insensitive to pH, temperature, and heavy metals, and emits a stable, highly tissue-transparent red light with luciferin in physiological circumstances. Thus, the luminescence signal is optimal for a bioanalytical index reporting the magnitude of a signal transduction of interest. Here, we validated a single-molecule-format complementation system of split CBLuc to study signal-controlled protein-protein (peptide) interactions. First, we generated 10 pairs of N- and C-terminal fragments of CBLuc to examine respectively whether a significant recovery of the activity occurs through the intramolecular complementation. The ligand binding domain of androgen receptor (AR LBD) was connected to a functional peptide sequence through a flexible linker. The fusion protein was then sandwiched between the dissected N- and C-terminal fragments of CBLuc. Androgen induces the association between AR LBD and a functional peptide and the subsequent complementation of N- and C-terminal fragments of split CBLuc inside the single-molecule-format probe, which restores the activities of CBLuc. The examination about the dissection sites of CBLuc revealed that the dissection positions next to the amino acids D412 and I439 admit a stable recovery of CBLuc activity through an intramolecular complementation. The ligand sensitivity and kinetics of the single molecular probe with split CBLuc were discussed in various cell lines and in different protein-peptide binding models. The probe is applicable to developing biotherapeutic agents on the AR signaling and for screening adverse chemicals that possibly influence the signal transduction of proteins in living cells or animals.

  17. The many faces of protein-protein interactions: A compendium of interface geometry.

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    Wan Kyu Kim

    2006-09-01

    Full Text Available A systematic classification of protein-protein interfaces is a valuable resource for understanding the principles of molecular recognition and for modelling protein complexes. Here, we present a classification of domain interfaces according to their geometry. Our new algorithm uses a hybrid approach of both sequential and structural features. The accuracy is evaluated on a hand-curated dataset of 416 interfaces. Our hybrid procedure achieves 83% precision and 95% recall, which improves the earlier sequence-based method by 5% on both terms. We classify virtually all domain interfaces of known structure, which results in nearly 6,000 distinct types of interfaces. In 40% of the cases, the interacting domain families associate in multiple orientations, suggesting that all the possible binding orientations need to be explored for modelling multidomain proteins and protein complexes. In general, hub proteins are shown to use distinct surface regions (multiple faces for interactions with different partners. Our classification provides a convenient framework to query genuine gene fusion, which conserves binding orientation in both fused and separate forms. The result suggests that the binding orientations are not conserved in at least one-third of the gene fusion cases detected by a conventional sequence similarity search. We show that any evolutionary analysis on interfaces can be skewed by multiple binding orientations and multiple interaction partners. The taxonomic distribution of interface types suggests that ancient interfaces common to the three major kingdoms of life are enriched by symmetric homodimers. The classification results are online at http://www.scoppi.org.

  18. Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions.

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    Chuanhua Xing

    2011-07-01

    Full Text Available Protein-protein interactions (PPIs are essential to most fundamental cellular processes. There has been increasing interest in reconstructing PPIs networks. However, several critical difficulties exist in obtaining reliable predictions. Noticeably, false positive rates can be as high as >80%. Error correction from each generating source can be both time-consuming and inefficient due to the difficulty of covering the errors from multiple levels of data processing procedures within a single test. We propose a novel Bayesian integration method, deemed nonparametric Bayes ensemble learning (NBEL, to lower the misclassification rate (both false positives and negatives through automatically up-weighting data sources that are most informative, while down-weighting less informative and biased sources. Extensive studies indicate that NBEL is significantly more robust than the classic naïve Bayes to unreliable, error-prone and contaminated data. On a large human data set our NBEL approach predicts many more PPIs than naïve Bayes. This suggests that previous studies may have large numbers of not only false positives but also false negatives. The validation on two human PPIs datasets having high quality supports our observations. Our experiments demonstrate that it is feasible to predict high-throughput PPIs computationally with substantially reduced false positives and false negatives. The ability of predicting large numbers of PPIs both reliably and automatically may inspire people to use computational approaches to correct data errors in general, and may speed up PPIs prediction with high quality. Such a reliable prediction may provide a solid platform to other studies such as protein functions prediction and roles of PPIs in disease susceptibility.

  19. Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions.

    Science.gov (United States)

    Xing, Chuanhua; Dunson, David B

    2011-07-01

    Protein-protein interactions (PPIs) are essential to most fundamental cellular processes. There has been increasing interest in reconstructing PPIs networks. However, several critical difficulties exist in obtaining reliable predictions. Noticeably, false positive rates can be as high as >80%. Error correction from each generating source can be both time-consuming and inefficient due to the difficulty of covering the errors from multiple levels of data processing procedures within a single test. We propose a novel Bayesian integration method, deemed nonparametric Bayes ensemble learning (NBEL), to lower the misclassification rate (both false positives and negatives) through automatically up-weighting data sources that are most informative, while down-weighting less informative and biased sources. Extensive studies indicate that NBEL is significantly more robust than the classic naïve Bayes to unreliable, error-prone and contaminated data. On a large human data set our NBEL approach predicts many more PPIs than naïve Bayes. This suggests that previous studies may have large numbers of not only false positives but also false negatives. The validation on two human PPIs datasets having high quality supports our observations. Our experiments demonstrate that it is feasible to predict high-throughput PPIs computationally with substantially reduced false positives and false negatives. The ability of predicting large numbers of PPIs both reliably and automatically may inspire people to use computational approaches to correct data errors in general, and may speed up PPIs prediction with high quality. Such a reliable prediction may provide a solid platform to other studies such as protein functions prediction and roles of PPIs in disease susceptibility.

  20. Designing coarse grained-and atom based-potentials for protein-protein docking

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    Tobi Dror

    2010-11-01

    Full Text Available Abstract Background Protein-protein docking is a challenging computational problem in functional genomics, particularly when one or both proteins undergo conformational change(s upon binding. The major challenge is to define a scoring function soft enough to tolerate these changes and specific enough to distinguish between near-native and "misdocked" conformations. Results Using a linear programming (LP technique, we developed two types of potentials: (i Side chain-based and (ii Heavy atom-based. To achieve this we considered a set of 161 transient complexes and generated a large set of putative docked structures (decoys, based on a shape complementarity criterion, for each complex. The demand on the potentials was to yield, for the native (correctly docked structure, a potential energy lower than those of any of the non-native (misdocked structures. We show that the heavy atom-based potentials were able to comply with this requirement but not the side chain-based one. Thus, despite the smaller number of parameters, the capability of heavy atom-based potentials to discriminate between native and "misdocked" conformations is improved relative to those of the side chain-based potentials. The performance of the atom-based potentials was evaluated by a jackknife test on a set of 50 complexes taken from the Zdock2.3 decoys set. Conclusions Our results show that, using the LP approach, we were able to train our potentials using a dataset of transient complexes only the newly developed potentials outperform three other known potentials in this test.

  1. A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature.

    Directory of Open Access Journals (Sweden)

    Domonkos Tikk

    Full Text Available The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein-protein interactions (PPIs reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study

  2. Bovine peptidoglycan recognition protein-S: antimicrobial activity, localization, secretion, and binding properties.

    Science.gov (United States)

    Tydell, C Chace; Yuan, Jun; Tran, Patti; Selsted, Michael E

    2006-01-15

    Peptidoglycan (PGN) recognition proteins (PGRPs) are pattern recognition molecules of innate immunity that are conserved from insects to humans. Various PGRPs are reported to have diverse functions: they bind bacterial molecules, digest PGN, and are essential to the Toll pathway in Drosophila. One family member, bovine PGN recognition protein-S (bPGRP-S), has been found to bind and kill microorganisms in a PGN-independent manner, raising questions about the identity of the bPGRP-S ligand. Addressing this, we have determined the binding and microbicidal properties of bPGRP-S in a range of solutions approximating physiologic conditions. In this study we show that bPGRP-S interacts with other bacterial components, including LPS and lipoteichoic acid, with higher affinities than for PCP, as determined by their abilities to inhibit bPGRP-S-mediated killing of bacteria. Where and how PGRPs act in vivo is not yet clear. Using Immunogold electron microscopy, PGRP-S was localized to the dense/large granules of naive neutrophils, which contain the oxygen-independent bactericidal proteins of these cells, and to the neutrophil phagolysosome. In addition, Immunogold staining and secretion studies demonstrate that neutrophils secrete PGRP-S when exposed to bacteria. Bovine PGRP-S can mediate direct lysis of heat-killed bacteria; however, PGRP-S-mediated killing of bacteria is independent of this activity. Evidence that bPGRP-S has multiple activities and affinity to several bacterial molecules challenges the assumption that the PGRP family of proteins recapitulates the evolution of TLRs. Mammalian PGRPs do not have a single antimicrobial activity against a narrow range of target organisms; rather, they are generalists in their affinity and activity.

  3. Structural principles within the human-virus protein-protein interaction network

    Science.gov (United States)

    Franzosa, Eric A.; Xia, Yu

    2011-01-01

    General properties of the antagonistic biomolecular interactions between viruses and their hosts (exogenous interactions) remain poorly understood, and may differ significantly from known principles governing the cooperative interactions within the host (endogenous interactions). Systems biology approaches have been applied to study the combined interaction networks of virus and human proteins, but such efforts have so far revealed only low-resolution patterns of host-virus interaction. Here, we layer curated and predicted 3D structural models of human-virus and human-human protein complexes on top of traditional interaction networks to reconstruct the human-virus structural interaction network. This approach reveals atomic resolution, mechanistic patterns of host-virus interaction, and facilitates systematic comparison with the host’s endogenous interactions. We find that exogenous interfaces tend to overlap with and mimic endogenous interfaces, thereby competing with endogenous binding partners. The endogenous interfaces mimicked by viral proteins tend to participate in multiple endogenous interactions which are transient and regulatory in nature. While interface overlap in the endogenous network results largely from gene duplication followed by divergent evolution, viral proteins frequently achieve interface mimicry without any sequence or structural similarity to an endogenous binding partner. Finally, while endogenous interfaces tend to evolve more slowly than the rest of the protein surface, exogenous interfaces—including many sites of endogenous-exogenous overlap—tend to evolve faster, consistent with an evolutionary “arms race” between host and pathogen. These significant biophysical, functional, and evolutionary differences between host-pathogen and within-host protein-protein interactions highlight the distinct consequences of antagonism versus cooperation in biological networks. PMID:21680884

  4. Integrated cellular network of transcription regulations and protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Chen Bor-Sen

    2010-03-01

    Full Text Available Abstract Background With the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway. Results In this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated. Conclusions We successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology.

  5. Analysis of intraviral protein-protein interactions of the SARS coronavirus ORFeome.

    Directory of Open Access Journals (Sweden)

    Albrecht von Brunn

    Full Text Available The severe acute respiratory syndrome coronavirus (SARS-CoV genome is predicted to encode 14 functional open reading frames, leading to the expression of up to 30 structural and non-structural protein products. The functions of a large number of viral ORFs are poorly understood or unknown. In order to gain more insight into functions and modes of action and interaction of the different proteins, we cloned the viral ORFeome and performed a genome-wide analysis for intraviral protein interactions and for intracellular localization. 900 pairwise interactions were tested by yeast-two-hybrid matrix analysis, and more than 65 positive non-redundant interactions, including six self interactions, were identified. About 38% of interactions were subsequently confirmed by CoIP in mammalian cells. Nsp2, nsp8 and ORF9b showed a wide range of interactions with other viral proteins. Nsp8 interacts with replicase proteins nsp2, nsp5, nsp6, nsp7, nsp8, nsp9, nsp12, nsp13 and nsp14, indicating a crucial role as a major player within the replication complex machinery. It was shown by others that nsp8 is essential for viral replication in vitro, whereas nsp2 is not. We show that also accessory protein ORF9b does not play a pivotal role for viral replication, as it can be deleted from the virus displaying normal plaque sizes and growth characteristics in Vero cells. However, it can be expected to be important for the virus-host interplay and for pathogenicity, due to its large number of interactions, by enhancing the global stability of the SARS proteome network, or play some unrealized role in regulating protein-protein interactions. The interactions identified provide valuable material for future studies.

  6. Structural Perspectives on the Evolutionary Expansion of Unique Protein-Protein Binding Sites.

    Science.gov (United States)

    Goncearenco, Alexander; Shaytan, Alexey K; Shoemaker, Benjamin A; Panchenko, Anna R

    2015-09-15

    Structures of protein complexes provide atomistic insights into protein interactions. Human proteins represent a quarter of all structures in the Protein Data Bank; however, available protein complexes cover less than 10% of the human proteome. Although it is theoretically possible to infer interactions in human proteins based on structures of homologous protein complexes, it is still unclear to what extent protein interactions and binding sites are conserved, and whether protein complexes from remotely related species can be used to infer interactions and binding sites. We considered biological units of protein complexes and clustered protein-protein binding sites into similarity groups based on their structure and sequence, which allowed us to identify unique binding sites. We showed that the growth rate of the number of unique binding sites in the Protein Data Bank was much slower than the growth rate of the number of structural complexes. Next, we investigated the evolutionary roots of unique binding sites and identified the major phyletic branches with the largest expansion in the number of novel binding sites. We found that many binding sites could be traced to the universal common ancestor of all cellular organisms, whereas relatively few binding sites emerged at the major evolutionary branching points. We analyzed the physicochemical properties of unique binding sites and found that the most ancient sites were the largest in size, involved many salt bridges, and were the most compact and least planar. In contrast, binding sites that appeared more recently in the evolution of eukaryotes were characterized by a larger fraction of polar and aromatic residues, and were less compact and more planar, possibly due to their more transient nature and roles in signaling processes.

  7. New GATEWAY vectors for High Throughput Analyses of Protein-Protein Interactions by Bimolecular Fluorescence Complementation

    Institute of Scientific and Technical Information of China (English)

    Christian Gehl; Rainer Waadt; J(o)rg Kudla; Ralf-R. Mendel; Robert Hansch

    2009-01-01

    Complex protein interaction networks constitute plant metabolic and signaling systems. Bimolecular fluores-cence complementation (BiFC) is a suitable technique to investigate the formation of protein complexes and the locali-zation of protein-protein interactions in planta. However, the generation of large plasmid collections to facilitate the exploration of complex interaction networks is often limited by the need for conventional cloning techniques. Here, we report the implementation of a GATEWAY vector system enabling large-scale combination and investigation of can-didate proteins in BiFC studies. We describe a set of 12 GATEWAY-compatible BiFC vectors that efficiently permit the com-bination of candidate protein pairs with every possible N-or C-terminal sub-fragment of S(CFP)3A or Venus, respectively, and enable the performance of multicolor BiFC (mcBiFC). We used proteins of the plant molybdenum metabolism, in that more than 20 potentially interacting proteins are assumed to form the cellular molybdenum network, as a case study to establish the functionality of the new vectors. Using these vectors, we report the formation of the molybdopterin synthase complex by interaction of Arabidopsis proteins Cnx6 and Cnx7 detected by BiFC as well as the simultaneous formation of Cn×6/Cn×6 and Cn×6/Cn×7 complexes revealed by mcBiFC. Consequently, these GATEWAY-based BiFC vector systems should significantly facilitate the large-scale investigation of complex regulatory networks in plant cells.

  8. Comparing human-Salmonella with plant-Salmonella protein-protein interaction predictions

    Directory of Open Access Journals (Sweden)

    Sylvia eSchleker

    2015-01-01

    Full Text Available Salmonellosis is the most frequent food-borne disease world-wide and can be transmitted to humans by a variety of routes, especially via animal and plant products. Salmonella bacteria are believed to use not only animal and human but also plant hosts despite their evolutionary distance. This raises the question if Salmonella employs similar mechanisms in infection of these diverse hosts. Given that most of our understanding comes from its interaction with human hosts, we investigate here to what degree knowledge of Salmonella-human interactions can be transferred to the Salmonella-plant system. Reviewed are recent publications on analysis and prediction of Salmonella-host interactomes. Putative protein-protein interactions (PPIs between Salmonella and its human and Arabidopsis hosts were retrieved utilizing purely interolog-based approaches in which predictions were inferred based on available sequence and domain information of known PPIs, and machine learning approaches that integrate a larger set of useful information from different sources. Transfer learning is an especially suitable machine learning technique to predict plant host targets from the knowledge of human host targets. A comparison of the prediction results with transcriptomic data shows a clear overlap between the host proteins predicted to be targeted by PPIs and their gene ontology enrichment in both host species and regulation of gene expression. In particular, the cellular processes Salmonella interferes with in plants and humans are catabolic processes. The details of how these processes are targeted, however, are quite different between the two organisms, as expected based on their evolutionary and habitat differences. Possible implications of this observation on evolution of host-pathogen communication are discussed.

  9. Protein-protein interface detection using the energy centrality relationship (ECR characteristic of proteins.

    Directory of Open Access Journals (Sweden)

    Sanjana Sudarshan

    Full Text Available Specific protein interactions are responsible for most biological functions. Distinguishing Functionally Linked Interfaces of Proteins (FLIPs, from Functionally uncorrelated Contacts (FunCs, is therefore important to characterizing these interactions. To achieve this goal, we have created a database of protein structures called FLIPdb, containing proteins belonging to various functional sub-categories. Here, we use geometric features coupled with Kortemme and Baker's computational alanine scanning method to calculate the energetic sensitivity of each amino acid at the interface to substitution, identify hotspots, and identify other factors that may contribute towards an interface being FLIP or FunC. Using Principal Component Analysis and K-means clustering on a training set of 160 interfaces, we could distinguish FLIPs from FunCs with an accuracy of 76%. When these methods were applied to two test sets of 18 and 170 interfaces, we achieved similar accuracies of 78% and 80%. We have identified that FLIP interfaces have a stronger central organizing tendency than FunCs, due, we suggest, to greater specificity. We also observe that certain functional sub-categories, such as enzymes, antibody-heavy-light, antibody-antigen, and enzyme-inhibitors form distinct sub-clusters. The antibody-antigen and enzyme-inhibitors interfaces have patterns of physical characteristics similar to those of FunCs, which is in agreement with the fact that the selection pressures of these interfaces is differently evolutionarily driven. As such, our ECR model also successfully describes the impact of evolution and natural selection on protein-protein interfaces. Finally, we indicate how our ECR method may be of use in reducing the false positive rate of docking calculations.

  10. An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology

    Directory of Open Access Journals (Sweden)

    Jain Shobhit

    2010-11-01

    Full Text Available Abstract Background Semantic similarity measures are useful to assess the physiological relevance of protein-protein interactions (PPIs. They quantify similarity between proteins based on their function using annotation systems like the Gene Ontology (GO. Proteins that interact in the cell are likely to be in similar locations or involved in similar biological processes compared to proteins that do not interact. Thus the more semantically similar the gene function annotations are among the interacting proteins, more likely the interaction is physiologically relevant. However, most semantic similarity measures used for PPI confidence assessment do not consider the unequal depth of term hierarchies in different classes of cellular location, molecular function, and biological process ontologies of GO and thus may over-or under-estimate similarity. Results We describe an improved algorithm, Topological Clustering Semantic Similarity (TCSS, to compute semantic similarity between GO terms annotated to proteins in interaction datasets. Our algorithm, considers unequal depth of biological knowledge representation in different branches of the GO graph. The central idea is to divide the GO graph into sub-graphs and score PPIs higher if participating proteins belong to the same sub-graph as compared to if they belong to different sub-graphs. Conclusions The TCSS algorithm performs better than other semantic similarity measurement techniques that we evaluated in terms of their performance on distinguishing true from false protein interactions, and correlation with gene expression and protein families. We show an average improvement of 4.6 times the F1 score over Resnik, the next best method, on our Saccharomyces cerevisiae PPI dataset and 2 times on our Homo sapiens PPI dataset using cellular component, biological process and molecular function GO annotations.

  11. Comparative interactomics for virus-human protein-protein interactions: DNA viruses versus RNA viruses.

    Science.gov (United States)

    Durmuş, Saliha; Ülgen, Kutlu Ö

    2017-01-01

    Viruses are obligatory intracellular pathogens and completely depend on their hosts for survival and reproduction. The strategies adopted by viruses to exploit host cell processes and to evade host immune systems during infections may differ largely with the type of the viral genetic material. An improved understanding of these viral infection mechanisms is only possible through a better understanding of the pathogen-host interactions (PHIs) that enable viruses to enter into the host cells and manipulate the cellular mechanisms to their own advantage. Experimentally-verified protein-protein interaction (PPI) data of pathogen-host systems only became available at large scale within the last decade. In this study, we comparatively analyzed the current PHI networks belonging to DNA and RNA viruses and their human host, to get insights into the infection strategies used by these viral groups. We investigated the functional properties of human proteins in the PHI networks, to observe and compare the attack strategies of DNA and RNA viruses. We observed that DNA viruses are able to attack both human cellular and metabolic processes simultaneously during infections. On the other hand, RNA viruses preferentially interact with human proteins functioning in specific cellular processes as well as in intracellular transport and localization within the cell. Observing virus-targeted human proteins, we propose heterogeneous nuclear ribonucleoproteins and transporter proteins as potential antiviral therapeutic targets. The observed common and specific infection mechanisms in terms of viral strategies to attack human proteins may provide crucial information for further design of broad and specific next-generation antiviral therapeutics.

  12. Ouroboros - Playing A Biochemical

    Directory of Open Access Journals (Sweden)

    D. T. Rodrigues

    2014-08-01

    Full Text Available Ouroboros: Playing A Biochemical RODRIGUES,D.T.1,2;GAYER, M.C.1,2; ESCOTO, D.F.1; DENARDIN, E.L.G.2, ROEHRS, R.1,2 1Interdisciplinary Research Group on Teaching Practice, Graduate Program in Biochemistry, Unipampa, RS, Brazil 2Laboratory of Physicochemical Studies and Natural Products, Post Graduate Program in Biochemistry, Unipampa, RS, Brazil Introduction: Currently, teachers seek different alternatives to enhance the teaching-learning process. Innovative teaching methodologies are increasingly common tools in educational routine. The use of games, electronic or conventional, is an effective tool to assist in learning and also to raise the social interaction between students. Objective: In this sense our work aims to evaluate the card game and "Ouroboros" board as a teaching and learning tool in biochemistry for a graduating class in Natural Sciences. Materials and methods: The class gathered 22 students of BSc in Natural Sciences. Each letter contained a question across the board that was drawn to a group to answer within the allotted time. The questions related concepts of metabolism, organic and inorganic chemical reactions, bioenergetics, etc.. Before the game application, students underwent a pre-test with four issues involving the content that was being developed. Soon after, the game was applied. Then again questions were asked. Data analysis was performed from the ratio of the number of correct pre-test and post-test answers. Results and discussion: In the pre-test 18.1% of the students knew all issues, 18.1% got 3 correct answers, 40.9% answered only 2 questions correctly and 22.7% did not hit any. In post-test 45.4% answered all the questions right, 31.8% got 3 questions and 22.7% got 2 correct answers. The results show a significant improvement of the students about the field of content taught through the game. Conclusion: Generally, traditional approaches of chemistry and biochemistry are abstract and complex. Thus, through games

  13. Insights into cellulase-lignin non-specific binding revealed by computational redesign of the surface of green fluorescent protein: Protein Redesign to Lower Protein-lignin Binding

    Energy Technology Data Exchange (ETDEWEB)

    Haarmeyer, Carolyn N. [Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing Michigan 48824; Smith, Matthew D. [Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing Michigan 48824; Chundawat, Shishir P. S. [Great Lakes Bioenergy Research Center (GLBRC), Michigan State University, East Lansing Michigan; Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway New Jersey; Sammond, Deanne [Biosciences Center, National Renewable Energy Laboratory, Golden Colorado; Whitehead, Timothy A. [Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing Michigan 48824; Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing Michigan 48824

    2016-11-07

    Biological-mediated conversion of pretreated lignocellulosic biomass to biofuels and biochemicals is a promising avenue towards energy sustainability. However, a critical impediment to the commercialization of cellulosic biofuel production is the high cost of cellulase enzymes needed to deconstruct biomass into fermentable sugars. One major factor driving cost is cellulase adsorption and inactivation in the presence of lignin, yet we currently have a poor understanding of the protein structure-function relationships driving this adsorption. In this work, we have systematically investigated the role of protein surface potential on lignin adsorption using a model monomeric fluorescent protein. We have designed and experimentally characterized 16 model protein variants spanning the physiological range of net charge (-24 to +16 total charges) and total charge density (0.28 to 0.40 charges per sequence length) typical for natural proteins. Protein designs were expressed, purified, and subjected to in silico and in vitro biophysical measurements to evaluate the relationship between protein surface potential and lignin adsorption properties. The designs were comparable to model fluorescent protein in terms of thermostability and heterologous expression yield, although the majority of the designs unexpectedly formed homodimers. Protein adsorption to lignin was studied at two different temperatures using Quartz Crystal Microbalance with Dissipation Monitoring and a subtractive mass balance assay. We found a weak correlation between protein net charge and protein-binding capacity to lignin. No other single characteristic, including apparent melting temperature and 2nd virial coefficient, showed correlation with lignin binding. Analysis of an unrelated cellulase dataset with mutations localized to a family I carbohydrate-binding module showed a similar correlation between net charge and lignin binding capacity. Overall, our study provides strategies to identify highly active

  14. Complete protein-protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling

    Science.gov (United States)

    Plattner, Nuria; Doerr, Stefan; de Fabritiis, Gianni; Noé, Frank

    2017-10-01

    Protein-protein association is fundamental to many life processes. However, a microscopic model describing the structures and kinetics during association and dissociation is lacking on account of the long lifetimes of associated states, which have prevented efficient sampling by direct molecular dynamics (MD) simulations. Here we demonstrate protein-protein association and dissociation in atomistic resolution for the ribonuclease barnase and its inhibitor barstar by combining adaptive high-throughput MD simulations and hidden Markov modelling. The model reveals experimentally consistent intermediate structures, energetics and kinetics on timescales from microseconds to hours. A variety of flexibly attached intermediates and misbound states funnel down to a transition state and a native basin consisting of the loosely bound near-native state and the tightly bound crystallographic state. These results offer a deeper level of insight into macromolecular recognition and our approach opens the door for understanding and manipulating a wide range of macromolecular association processes.

  15. Biochemical Engineering and Industrial Biotechnology.

    Science.gov (United States)

    Moo-Young, Murray

    1986-01-01

    Describes the biochemical engineering and industrial biotechnology programs of the University of Waterloo (Ontario, Canada). Provides descriptions of graduate courses, along with a sample of current research activities. Includes a discussion of the programs' mechanisms for technology transfer. (TW)

  16. Enzyme and biochemical producing fungi

    DEFF Research Database (Denmark)

    Lübeck, Peter Stephensen; Lübeck, Mette; Nilsson, Lena

    2010-01-01

    factories for sustainable production of important molecules. For developing fungi into efficient cell factories, the project includes identification of important factors that control the flux through the pathways using metabolic flux analysis and metabolic engineering of biochemical pathways....

  17. A Course in... Biochemical Engineering.

    Science.gov (United States)

    Ng, Terry K-L.; And Others

    1988-01-01

    Describes a chemical engineering course for senior undergraduates and first year graduate students in biochemical engineering. Discusses five experiments used in the course: aseptic techniques, dissolved oxygen measurement, oxygen uptake by yeast, continuous sterilization, and cultivation of microorganisms. (MVL)

  18. Biochemical Engineering Education through Videotapes.

    Science.gov (United States)

    Austin, G. D.; And Others

    1990-01-01

    The implementation and results of the use of videotapes in introductory biochemical engineering classes are discussed. The topics of the videotapes and the results of a questionnaire about this instructional method are presented. (CW)

  19. Mathematical Models of Biochemical Oscillations

    OpenAIRE

    Conrad, Emery David

    1999-01-01

    The goal of this paper is to explain the mathematics involved in modeling biochemical oscillations. We first discuss several important biochemical concepts fundamental to the construction of descriptive mathematical models. We review the basic theory of differential equations and stability analysis as it relates to two-variable models exhibiting oscillatory behavior. The importance of the Hopf Bifurcation will be discussed in detail for the central role it plays in limit cycle behavior and...

  20. Relative quantification of protein-protein interactions using a dual luciferase reporter pull-down assay system.

    Directory of Open Access Journals (Sweden)

    Shuaizheng Jia

    Full Text Available The identification and quantitative analysis of protein-protein interactions are essential to the functional characterization of proteins in the post-proteomics era. The methods currently available are generally time-consuming, technically complicated, insensitive and/or semi-quantitative. The lack of simple, sensitive approaches to precisely quantify protein-protein interactions still prevents our understanding of the functions of many proteins. Here, we develop a novel dual luciferase reporter pull-down assay by combining a biotinylated Firefly luciferase pull-down assay with a dual luciferase reporter assay. The biotinylated Firefly luciferase-tagged protein enables rapid and efficient isolation of a putative Renilla luciferase-tagged binding protein from a relatively small amount of sample. Both of these proteins can be quantitatively detected using the dual luciferase reporter assay system. Protein-protein interactions, including Fos-Jun located in the nucleus; MAVS-TRAF3 in cytoplasm; inducible IRF3 dimerization; viral protein-regulated interactions, such as MAVS-MAVS and MAVS-TRAF3; IRF3 dimerization; and protein interaction domain mapping, are studied using this novel assay system. Herein, we demonstrate that this dual luciferase reporter pull-down assay enables the quantification of the relative amounts of interacting proteins that bind to streptavidin-coupled beads for protein purification. This study provides a simple, rapid, sensitive, and efficient approach to identify and quantify relative protein-protein interactions. Importantly, the dual luciferase reporter pull-down method will facilitate the functional determination of proteins.

  1. Prediction of Protein-Protein Interactions by NanoLuc-Based Protein-Fragment Complementation Assay | Office of Cancer Genomics

    Science.gov (United States)

    The CTD2 Center at Emory has developed a new NanoLuc®-based protein-fragment complementation assay (NanoPCA) which allows the detection of novel protein-protein interactions (PPI). NanoPCA allows the study of PPI dynamics with reversible interactions.  Read the abstract. Experimental Approaches Read the detailed Experimetnal Approaches. 

  2. Analysis of protein-protein docking decoys using interaction fingerprints: application to the reconstruction of CaM-ligand complexes

    Directory of Open Access Journals (Sweden)

    Uchikoga Nobuyuki

    2010-05-01

    Full Text Available Abstract Background Protein-protein docking for proteins with large conformational changes was analyzed by using interaction fingerprints, one of the scales for measuring similarities among complex structures, utilized especially for searching near-native protein-ligand or protein-protein complex structures. Here, we have proposed a combined method for analyzing protein-protein docking by taking large conformational changes into consideration. This combined method consists of ensemble soft docking with multiple protein structures, refinement of complexes, and cluster analysis using interaction fingerprints and energy profiles. Results To test for the applicability of this combined method, various CaM-ligand complexes were reconstructed from the NMR structures of unbound CaM. For the purpose of reconstruction, we used three known CaM-ligands, namely, the CaM-binding peptides of cyclic nucleotide gateway (CNG, CaM kinase kinase (CaMKK and the plasma membrane Ca2+ ATPase pump (PMCA, and thirty-one structurally diverse CaM conformations. For each ligand, 62000 CaM-ligand complexes were generated in the docking step and the relationship between their energy profiles and structural similarities to the native complex were analyzed using interaction fingerprint and RMSD. Near-native clusters were obtained in the case of CNG and CaMKK. Conclusions The interaction fingerprint method discriminated near-native structures better than the RMSD method in cluster analysis. We showed that a combined method that includes the interaction fingerprint is very useful for protein-protein docking analysis of certain cases.

  3. Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening.

    Science.gov (United States)

    Zhang, Changsheng; Tang, Bo; Wang, Qian; Lai, Luhua

    2014-10-01

    Target structure-based virtual screening, which employs protein-small molecule docking to identify potential ligands, has been widely used in small-molecule drug discovery. In the present study, we used a protein-protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all-to-all protein-protein docking run on a large dataset was performed. The three-dimensional rigid docking program SDOCK was used to examine protein-protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z-score, and convergency of the low-score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all-to-all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor-α (TNFα), which is a well-known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top-ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein-protein docking for the discovery of novel binding proteins for specific protein targets.

  4. The Development and Characterization of Protein-Based Stationary Phases for Studying Drug-Protein and Protein-Protein Interactions

    OpenAIRE

    Sanghvi, Mitesh; Moaddel, Ruin; Wainer, Irving W.

    2011-01-01

    Protein-based liquid chromatography stationary phases are used in bioaffinity chromatography for studying drug-protein interactions, the determination of binding affinities, competitive and allosteric interactions, as well as for studying protein-protein interactions. This review addresses the development and characterization of protein-based stationary phase, and the application of these phases using frontal and zonal chromatography techniques. The approach will be illustrated using immobili...

  5. A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data

    Directory of Open Access Journals (Sweden)

    Li Min

    2012-03-01

    Full Text Available Abstract Background Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value. Results In this paper, we propose a new centrality measure, named PeC, based on the integration of protein-protein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed centrality measures: Degree Centrality (DC, Betweenness Centrality (BC, Closeness Centrality (CC, Subgraph Centrality (SC, Eigenvector Centrality (EC, Information Centrality (IC, Bottle Neck (BN, Density of Maximum Neighborhood Component (DMNC, Local Average Connectivity-based method (LAC, Sum of ECC (SoECC, Range-Limited Centrality (RL, L-index (LI, Leader Rank (LR, Normalized α-Centrality (NC, and Moduland-Centrality (MC. Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN is more than 50% when predicting no more than 500 proteins. Conclusions We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.

  6. Light-regulated stapled peptides to inhibit protein-protein interactions involved in clathrin-mediated endocytosis.

    Science.gov (United States)

    Nevola, Laura; Martín-Quirós, Andrés; Eckelt, Kay; Camarero, Núria; Tosi, Sébastien; Llobet, Artur; Giralt, Ernest; Gorostiza, Pau

    2013-07-22

    Control of membrane traffic: Photoswitchable inhibitors of protein-protein interactions were applied to photoregulate clathrin-mediated endocytosis (CME) in living cells. Traffic light (TL) peptides acting as "stop" and "go" signals for membrane traffic can be used to dissect the role of CME in receptor internalization and in cell growth, division, and differentiation. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. Integrating Structure to Protein-Protein Interaction Networks That Drive Metastasis to Brain and Lung in Breast Cancer

    OpenAIRE

    H Billur Engin; Emre Guney; Ozlem Keskin; Baldo Oliva; Attila Gursoy

    2013-01-01

    Integrating Structure to Protein-Protein Interaction Networks That Drive Metastasis to Brain and Lung in Breast Cancer H. Billur Engin1, Emre Guney2, Ozlem Keskin1, Baldo Oliva2, Attila Gursoy1* 1 Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey, 2 Structural Bioinformatics Group (GRIB), Universitat Pompeu Fabra Abstract Blocking specific protein interactions can lead to human diseases. Accordingly, protein i...

  8. Improving protein protein interaction prediction based on phylogenetic information using a least-squares support vector machine.

    Science.gov (United States)

    Craig, Roger A; Liao, Li

    2007-12-01

    Predicting protein-protein interactions has become a key step of reverse-engineering biological networks to better understand cellular functions. The experimental methods in determining protein-protein interactions are time-consuming and costly, which has motivated vigorous development of computational approaches for predicting protein-protein interactions. A set of recently developed bioinformatics methods utilizes coevolutionary information of the interacting partners (e.g., as exhibited in the form of correlations between distance matrices, where, for each protein, a matrix stores the pairwise distances between the protein and its orthologs in a group of reference genomes). We proposed a novel method to account for the intra-matrix correlations in improving predictive accuracy. The distance matrices for a pair of proteins are transformed and concatenated into a phylogenetic vector. A least-squares support vector machine is trained and tested on pairs of proteins, represented as phylogenetic vectors, whose interactions are known. The intra-matrix correlations are accounted for by introducing a weighted linear kernel, which determines the dot product of two phylogenetic vectors. The performance, measured as receiver operator characteristic (ROC) score in cross-validation experiments, shows significant improvement of our method (ROC score 0.928) over that obtained by Pearson correlations (0.659).

  9. Protein Charge and Mass Contribute to the Spatio-temporal Dynamics of Protein-Protein Interactions in a Minimal Proteome

    Science.gov (United States)

    Xu, Yu; Wang, Hong; Nussinov, Ruth; Ma, Buyong

    2013-01-01

    We constructed and simulated a ‘minimal proteome’ model using Langevin dynamics. It contains 206 essential protein types which were compiled from the literature. For comparison, we generated six proteomes with randomized concentrations. We found that the net charges and molecular weights of the proteins in the minimal genome are not random. The net charge of a protein decreases linearly with molecular weight, with small proteins being mostly positively charged and large proteins negatively charged. The protein copy numbers in the minimal genome have the tendency to maximize the number of protein-protein interactions in the network. Negatively charged proteins which tend to have larger sizes can provide large collision cross-section allowing them to interact with other proteins; on the other hand, the smaller positively charged proteins could have higher diffusion speed and are more likely to collide with other proteins. Proteomes with random charge/mass populations form less stable clusters than those with experimental protein copy numbers. Our study suggests that ‘proper’ populations of negatively and positively charged proteins are important for maintaining a protein-protein interaction network in a proteome. It is interesting to note that the minimal genome model based on the charge and mass of E. Coli may have a larger protein-protein interaction network than that based on the lower organism M. pneumoniae. PMID:23420643

  10. A cautionary note on the use of split-YFP/BiFC in plant protein-protein interaction studies.

    Science.gov (United States)

    Horstman, Anneke; Tonaco, Isabella Antonia Nougalli; Boutilier, Kim; Immink, Richard G H

    2014-05-30

    Since its introduction in plants 10 years ago, the bimolecular fluorescence complementation (BiFC) method, or split-YFP (yellow fluorescent protein), has gained popularity within the plant biology field as a method to study protein-protein interactions. BiFC is based on the restoration of fluorescence after the two non-fluorescent halves of a fluorescent protein are brought together by a protein-protein interaction event. The major drawback of BiFC is that the fluorescent protein halves are prone to self-assembly independent of a protein-protein interaction event. To circumvent this problem, several modifications of the technique have been suggested, but these modifications have not lead to improvements in plant BiFC protocols. Therefore, it remains crucial to include appropriate internal controls. Our literature survey of recent BiFC studies in plants shows that most studies use inappropriate controls, and a qualitative rather than quantitative read-out of fluorescence. Therefore, we provide a cautionary note and beginner's guideline for the setup of BiFC experiments, discussing each step of the protocol, including vector choice, plant expression systems, negative controls, and signal detection. In addition, we present our experience with BiFC with respect to self-assembly, peptide linkers, and incubation temperature. With this note, we aim to provide a guideline that will improve the quality of plant BiFC experiments.

  11. Protein-protein interactions: general trends in the relationship between binding affinity and interfacial buried surface area.

    Science.gov (United States)

    Chen, Jieming; Sawyer, Nicholas; Regan, Lynne

    2013-04-01

    Protein-protein interactions play key roles in many cellular processes and their affinities and specificities are finely tuned to the functions they perform. Here, we present a study on the relationship between binding affinity and the size and chemical nature of protein-protein interfaces. Our analysis focuses on heterodimers and includes curated structural and thermodynamic data for 113 complexes. We observe a direct correlation between binding affinity and the amount of surface area buried at the interface. For a given amount of surface area buried, the binding affinity spans four orders of magnitude in terms of the dissociation constant (Kd ). Across the entire dataset, we observe no obvious relationship between binding affinity and the chemical composition of the interface. We also calculate the free energy per unit surface area buried, or "surface energy density," of each heterodimer. For interfacial surface areas between 500 and 2000 Å(2) , the surface energy density decreases as the buried surface area increases. As the buried surface area increases beyond about 2000 Å(2) , the surface energy density levels off to a constant value. We believe that these analyses and data will be useful for researchers with an interest in understanding, designing or inhibiting protein-protein interfaces.

  12. Controllability of protein-protein interaction phosphorylation-based networks: Participation of the hub 14-3-3 protein family.

    Science.gov (United States)

    Uhart, Marina; Flores, Gabriel; Bustos, Diego M

    2016-05-19

    Posttranslational regulation of protein function is an ubiquitous mechanism in eukaryotic cells. Here, we analyzed biological properties of nodes and edges of a human protein-protein interaction phosphorylation-based network, especially of those nodes critical for the network controllability. We found that the minimal number of critical nodes needed to control the whole network is 29%, which is considerably lower compared to other real networks. These critical nodes are more regulated by posttranslational modifications and contain more binding domains to these modifications than other kinds of nodes in the network, suggesting an intra-group fast regulation. Also, when we analyzed the edges characteristics that connect critical and non-critical nodes, we found that the former are enriched in domain-to-eukaryotic linear motif interactions, whereas the later are enriched in domain-domain interactions. Our findings suggest a possible structure for protein-protein interaction networks with a densely interconnected and self-regulated central core, composed of critical nodes with a high participation in the controllability of the full network, and less regulated peripheral nodes. Our study offers a deeper understanding of complex network control and bridges the controllability theorems for complex networks and biological protein-protein interaction phosphorylation-based networked systems.

  13. A machine learning approach for ranking clusters of docked protein-protein complexes by pairwise cluster comparison.

    Science.gov (United States)

    Pfeiffenberger, Erik; Chaleil, Raphael A G; Moal, Iain H; Bates, Paul A

    2017-03-01

    Reliable identification of near-native poses of docked protein-protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein-protein interactions is challenging for traditional biophysical or knowledge based potentials and the identification of many false positive binding sites is not unusual. Often, ranking protocols are based on initial clustering of docked poses followed by the application of an energy function to rank each cluster according to its lowest energy member. Here, we present an approach of cluster ranking based not only on one molecular descriptor (e.g., an energy function) but also employing a large number of descriptors that are integrated in a machine learning model, whereby, an extremely randomized tree classifier based on 109 molecular descriptors is trained. The protocol is based on first locally enriching clusters with additional poses, the clusters are then characterized using features describing the distribution of molecular descriptors within the cluster, which are combined into a pairwise cluster comparison model to discriminate near-native from incorrect clusters. The results show that our approach is able to identify clusters containing near-native protein-protein complexes. In addition, we present an analysis of the descriptors with respect to their power to discriminate near native from incorrect clusters and how data transformations and recursive feature elimination can improve the ranking performance. Proteins 2017; 85:528-543. © 2016 Wiley Periodicals, Inc.

  14. Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

    Science.gov (United States)

    Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

    2015-11-01

    Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases.

  15. A Cautionary Note on the Use of Split-YFP/BiFC in Plant Protein-Protein Interaction Studies

    Directory of Open Access Journals (Sweden)

    Anneke Horstman

    2014-05-01

    Full Text Available Since its introduction in plants 10 years ago, the bimolecular fluorescence complementation (BiFC method, or split-YFP (yellow fluorescent protein, has gained popularity within the plant biology field as a method to study protein-protein interactions. BiFC is based on the restoration of fluorescence after the two non-fluorescent halves of a fluorescent protein are brought together by a protein-protein interaction event. The major drawback of BiFC is that the fluorescent protein halves are prone to self-assembly independent of a protein-protein interaction event. To circumvent this problem, several modifications of the technique have been suggested, but these modifications have not lead to improvements in plant BiFC protocols. Therefore, it remains crucial to include appropriate internal controls. Our literature survey of recent BiFC studies in plants shows that most studies use inappropriate controls, and a qualitative rather than quantitative read-out of fluorescence. Therefore, we provide a cautionary note and beginner’s guideline for the setup of BiFC experiments, discussing each step of the protocol, including vector choice, plant expression systems, negative controls, and signal detection. In addition, we present our experience with BiFC with respect to self-assembly, peptide linkers, and incubation temperature. With this note, we aim to provide a guideline that will improve the quality of plant BiFC experiments.

  16. Interactome-wide prediction of protein-protein binding sites reveals effects of protein sequence variation in Arabidopsis thaliana.

    Directory of Open Access Journals (Sweden)

    Felipe Leal Valentim

    Full Text Available The specificity of protein-protein interactions is encoded in those parts of the sequence that compose the binding interface. Therefore, understanding how changes in protein sequence influence interaction specificity, and possibly the phenotype, requires knowing the location of binding sites in those sequences. However, large-scale detection of protein interfaces remains a challenge. Here, we present a sequence- and interactome-based approach to mine interaction motifs from the recently published Arabidopsis thaliana interactome. The resultant proteome-wide predictions are available via www.ab.wur.nl/sliderbio and set the stage for further investigations of protein-protein binding sites. To assess our method, we first show that, by using a priori information calculated from protein sequences, such as evolutionary conservation and residue surface accessibility, we improve the performance of interface prediction compared to using only interactome data. Next, we present evidence for the functional importance of the predicted sites, which are under stronger selective pressure than the rest of protein sequence. We also observe a tendency for compensatory mutations in the binding sites of interacting proteins. Subsequently, we interrogated the interactome data to formulate testable hypotheses for the molecular mechanisms underlying effects of protein sequence mutations. Examples include proteins relevant for various developmental processes. Finally, we observed, by analysing pairs of paralogs, a correlation between functional divergence and sequence divergence in interaction sites. This analysis suggests that large-scale prediction of binding sites can cast light on evolutionary processes that shape protein-protein interaction networks.

  17. Protein-protein docking with F(2Dock 2.0 and GB-rerank.

    Directory of Open Access Journals (Sweden)

    Rezaul Chowdhury

    Full Text Available MOTIVATION: Computational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F(2 Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introducing improvements in the shape-complementarity and electrostatics affinity functions, a new knowledge-based interface propensity term with FFT formulation, a set of novel knowledge-based filters and finally a solvation energy (GBSA based reranking technique. Our algorithms are based on highly efficient data structures including the dynamic packing grids and octrees which significantly speed up the computations and also provide guaranteed bounds on approximation error. RESULTS: The improved affinity functions show superior performance compared to their traditional counterparts in finding correct docking poses at higher ranks. We found that the new filters and the GBSA based reranking individually and in combination significantly improve the accuracy of docking predictions with only minor increase in computation time. We compared F(2 Dock 2.0 with ZDock 3.0.2 and found improvements over it, specifically among 176 complexes in ZLab Benchmark 4.0, F(2 Dock 2.0 finds a near-native solution as the top prediction for 22 complexes; where ZDock 3.0.2 does so for 13 complexes. F(2 Dock 2.0 finds a near-native solution within the top 1000 predictions for 106 complexes as opposed to 104 complexes for ZDock 3.0.2. However, there are 17 and 15 complexes where F(2 Dock 2.0 finds a solution but ZDock 3.0.2 does not and vice versa; which indicates that the two docking protocols can also complement each other. AVAILABILITY: The docking protocol has been implemented as a server with a graphical client (TexMol which allows the user to manage multiple docking jobs, and visualize the docked poses and interfaces. Both the server and client are available for download. Server

  18. Prediction and characterization of protein-protein interaction networks in swine

    Directory of Open Access Journals (Sweden)

    Wang Fen

    2012-01-01

    Full Text Available Abstract Background Studying the large-scale protein-protein interaction (PPI network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. Results We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. Conclusion The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/.

  19. Protein-protein docking with F(2)Dock 2.0 and GB-rerank.

    Science.gov (United States)

    Chowdhury, Rezaul; Rasheed, Muhibur; Keidel, Donald; Moussalem, Maysam; Olson, Arthur; Sanner, Michel; Bajaj, Chandrajit

    2013-01-01

    Computational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F(2) Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introducing improvements in the shape-complementarity and electrostatics affinity functions, a new knowledge-based interface propensity term with FFT formulation, a set of novel knowledge-based filters and finally a solvation energy (GBSA) based reranking technique. Our algorithms are based on highly efficient data structures including the dynamic packing grids and octrees which significantly speed up the computations and also provide guaranteed bounds on approximation error. The improved affinity functions show superior performance compared to their traditional counterparts in finding correct docking poses at higher ranks. We found that the new filters and the GBSA based reranking individually and in combination significantly improve the accuracy of docking predictions with only minor increase in computation time. We compared F(2) Dock 2.0 with ZDock 3.0.2 and found improvements over it, specifically among 176 complexes in ZLab Benchmark 4.0, F(2) Dock 2.0 finds a near-native solution as the top prediction for 22 complexes; where ZDock 3.0.2 does so for 13 complexes. F(2) Dock 2.0 finds a near-native solution within the top 1000 predictions for 106 complexes as opposed to 104 complexes for ZDock 3.0.2. However, there are 17 and 15 complexes where F(2) Dock 2.0 finds a solution but ZDock 3.0.2 does not and vice versa; which indicates that the two docking protocols can also complement each other. The docking protocol has been implemented as a server with a graphical client (TexMol) which allows the user to manage multiple docking jobs, and visualize the docked poses and interfaces. Both the server and client are available for download. Server: http

  20. Protein-Protein Docking with F2Dock 2.0 and GB-Rerank

    Science.gov (United States)

    Chowdhury, Rezaul; Rasheed, Muhibur; Keidel, Donald; Moussalem, Maysam; Olson, Arthur; Sanner, Michel; Bajaj, Chandrajit

    2013-01-01

    Motivation Computational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F2 Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introducing improvements in the shape-complementarity and electrostatics affinity functions, a new knowledge-based interface propensity term with FFT formulation, a set of novel knowledge-based filters and finally a solvation energy (GBSA) based reranking technique. Our algorithms are based on highly efficient data structures including the dynamic packing grids and octrees which significantly speed up the computations and also provide guaranteed bounds on approximation error. Results The improved affinity functions show superior performance compared to their traditional counterparts in finding correct docking poses at higher ranks. We found that the new filters and the GBSA based reranking individually and in combination significantly improve the accuracy of docking predictions with only minor increase in computation time. We compared F2 Dock 2.0 with ZDock 3.0.2 and found improvements over it, specifically among 176 complexes in ZLab Benchmark 4.0, F2 Dock 2.0 finds a near-native solution as the top prediction for 22 complexes; where ZDock 3.0.2 does so for 13 complexes. F2 Dock 2.0 finds a near-native solution within the top 1000 predictions for 106 complexes as opposed to 104 complexes for ZDock 3.0.2. However, there are 17 and 15 complexes where F2 Dock 2.0 finds a solution but ZDock 3.0.2 does not and vice versa; which indicates that the two docking protocols can also complement each other. Availability The docking protocol has been implemented as a server with a graphical client (TexMol) which allows the user to manage multiple docking jobs, and visualize the docked poses and interfaces. Both the server and client are available for download. Server: http

  1. Molecular insights into the stabilization of protein-protein interactions with small molecule: The FKBP12-rapamycin-FRB case study

    Science.gov (United States)

    Chaurasia, Shilpi; Pieraccini, Stefano; De Gonda, Riccardo; Conti, Simone; Sironi, Maurizio

    2013-11-01

    Targetting protein-protein interactions is a challenging task in drug discovery process. Despite the challenges, several studies provided evidences for the development of small molecules modulating protein-protein interactions. Here we consider a typical case of protein-protein interaction stabilization: the complex between FKBP12 and FRB with rapamycin. We have analyzed the stability of the complex and characterized its interactions at the atomic level by performing free energy calculations and computational alanine scanning. It is shown that rapamycin stabilizes the complex by acting as a bridge between the two proteins; and the complex is stable only in the presence of rapamycin.

  2. BEST: Biochemical Engineering Simulation Technology

    Energy Technology Data Exchange (ETDEWEB)

    1996-01-01

    The idea of developing a process simulator that can describe biochemical engineering (a relatively new technology area) was formulated at the National Renewable Energy Laboratory (NREL) during the late 1980s. The initial plan was to build a consortium of industrial and U.S. Department of Energy (DOE) partners to enhance a commercial simulator with biochemical unit operations. DOE supported this effort; however, before the consortium was established, the process simulator industry changed considerably. Work on the first phase of implementing various fermentation reactors into the chemical process simulator, ASPEN/SP-BEST, is complete. This report will focus on those developments. Simulation Sciences, Inc. (SimSci) no longer supports ASPEN/SP, and Aspen Technology, Inc. (AspenTech) has developed an add-on to its ASPEN PLUS (also called BioProcess Simulator [BPS]). This report will also explain the similarities and differences between BEST and BPS. ASPEN, developed by the Massachusetts Institute of Technology for DOE in the late 1970s, is still the state-of-the-art chemical process simulator. It was selected as the only simulator with the potential to be easily expanded into the biochemical area. ASPEN/SP, commercially sold by SimSci, was selected for the BEST work. SimSci completed work on batch, fed-batch, and continuous fermentation reactors in 1993, just as it announced it would no longer commercially support the complete ASPEN/SP product. BEST was left without a basic support program. Luckily, during this same time frame, AspenTech was developing a biochemical simulator with its version of ASPEN (ASPEN PLUS), which incorporates most BEST concepts. The future of BEST will involve developing physical property data and models appropriate to biochemical systems that are necessary for good biochemical process design.

  3. The TEAD4-YAP/TAZ protein-protein interaction: expected similarities and unexpected differences.

    Science.gov (United States)

    Hau, Jean Christophe; Erdmann, Dirk; Mesrouze, Yannick; Furet, Pascal; Fontana, Patrizia; Zimmermann, Catherine; Schmelzle, Tobias; Hofmann, Francesco; Chène, Patrick

    2013-07-08

    The Hippo pathway controls cell homeostasis, and its deregulation can lead to human diseases. In this pathway, the YAP and TAZ transcriptional cofactors play a key role in stimulating gene transcription through their interaction with the TEAD transcriptional factors. Our study of YAP and TAZ peptides in biochemical and biophysical assays shows that both proteins have essentially the same affinity for TEAD. Molecular modeling and structural biology data suggest that they also bind to the same site on TEAD. However, this apparent similarity hides differences in the ways in which the two proteins interact with TEAD. The secondary structure elements of their TEAD binding site do not contribute equally to the overall affinity, and critical interactions with TEAD are made through different residues. This convergent optimization of the YAP/TAZ TEAD binding site suggests that the similarity in the affinities of binding of YAP to TEAD and of TAZ to TEAD is important for Hippo pathway functionality. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. Prediction of protein-protein interactions in dengue virus coat proteins guided by low resolution cryoEM structures

    Directory of Open Access Journals (Sweden)

    Srinivasan Narayanaswamy

    2010-06-01

    Full Text Available Abstract Background Dengue virus along with the other members of the flaviviridae family has reemerged as deadly human pathogens. Understanding the mechanistic details of these infections can be highly rewarding in developing effective antivirals. During maturation of the virus inside the host cell, the coat proteins E and M undergo conformational changes, altering the morphology of the viral coat. However, due to low resolution nature of the available 3-D structures of viral assemblies, the atomic details of these changes are still elusive. Results In the present analysis, starting from Cα positions of low resolution cryo electron microscopic structures the residue level details of protein-protein interaction interfaces of dengue virus coat proteins have been predicted. By comparing the preexisting structures of virus in different phases of life cycle, the changes taking place in these predicted protein-protein interaction interfaces were followed as a function of maturation process of the virus. Besides changing the current notion about the presence of only homodimers in the mature viral coat, the present analysis indicated presence of a proline-rich motif at the protein-protein interaction interface of the coat protein. Investigating the conservation status of these seemingly functionally crucial residues across other members of flaviviridae family enabled dissecting common mechanisms used for infections by these viruses. Conclusions Thus, using computational approach the present analysis has provided better insights into the preexisting low resolution structures of virus assemblies, the findings of which can be made use of in designing effective antivirals against these deadly human pathogens.

  5. Photolytic Cross-Linking to Probe Protein-Protein and Protein-Matrix Interactions in Lyophilized Powders.

    Science.gov (United States)

    Iyer, Lavanya K; Moorthy, Balakrishnan S; Topp, Elizabeth M

    2015-09-08

    Protein structure and local environment in lyophilized formulations were probed using high-resolution solid-state photolytic cross-linking with mass spectrometric analysis (ssPC-MS). In order to characterize structure and microenvironment, protein-protein, protein-excipient, and protein-water interactions in lyophilized powders were identified. Myoglobin (Mb) was derivatized in solution with the heterobifunctional probe succinimidyl 4,4'-azipentanoate (SDA) and the structural integrity of the labeled protein (Mb-SDA) confirmed using CD spectroscopy and liquid chromatography/mass spectrometry (LC-MS). Mb-SDA was then formulated with and without excipients (raffinose, guanidine hydrochloride (Gdn HCl)) and lyophilized. The freeze-dried powder was irradiated with ultraviolet light at 365 nm for 30 min to produce cross-linked adducts that were analyzed at the intact protein level and after trypsin digestion. SDA-labeling produced Mb carrying up to five labels, as detected by LC-MS. Following lyophilization and irradiation, cross-linked peptide-peptide, peptide-water, and peptide-raffinose adducts were detected. The exposure of Mb side chains to the matrix was quantified based on the number of different peptide-peptide, peptide-water, and peptide-excipient adducts detected. In the absence of excipients, peptide-peptide adducts involving the CD, DE, and EF loops and helix H were common. In the raffinose formulation, peptide-peptide adducts were more distributed throughout the molecule. The Gdn HCl formulation showed more protein-protein and protein-water adducts than the other formulations, consistent with protein unfolding and increased matrix interactions. The results demonstrate that ssPC-MS can be used to distinguish excipient effects and characterize the local protein environment in lyophilized formulations with high resolution.

  6. Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

    Science.gov (United States)

    Chen, Ching-Tai; Peng, Hung-Pin; Jian, Jhih-Wei; Tsai, Keng-Chang; Chang, Jeng-Yih; Yang, Ei-Wen; Chen, Jun-Bo; Ho, Shinn-Ying; Hsu, Wen-Lian; Yang, An-Suei

    2012-01-01

    Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with

  7. Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles.

    Science.gov (United States)

    Brender, Jeffrey R; Zhang, Yang

    2015-10-01

    The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies.

  8. A Library of Plasmodium vivax Recombinant Merozoite Proteins Reveals New Vaccine Candidates and Protein-Protein Interactions.

    Directory of Open Access Journals (Sweden)

    Jessica B Hostetler

    2015-12-01

    Full Text Available A vaccine targeting Plasmodium vivax will be an essential component of any comprehensive malaria elimination program, but major gaps in our understanding of P. vivax biology, including the protein-protein interactions that mediate merozoite invasion of reticulocytes, hinder the search for candidate antigens. Only one ligand-receptor interaction has been identified, that between P. vivax Duffy Binding Protein (PvDBP and the erythrocyte Duffy Antigen Receptor for Chemokines (DARC, and strain-specific immune responses to PvDBP make it a complex vaccine target. To broaden the repertoire of potential P. vivax merozoite-stage vaccine targets, we exploited a recent breakthrough in expressing full-length ectodomains of Plasmodium proteins in a functionally-active form in mammalian cells and initiated a large-scale study of P. vivax merozoite proteins that are potentially involved in reticulocyte binding and invasion.We selected 39 P. vivax proteins that are predicted to localize to the merozoite surface or invasive secretory organelles, some of which show homology to P. falciparum vaccine candidates. Of these, we were able to express 37 full-length protein ectodomains in a mammalian expression system, which has been previously used to express P. falciparum invasion ligands such as PfRH5. To establish whether the expressed proteins were correctly folded, we assessed whether they were recognized by antibodies from Cambodian patients with acute vivax malaria. IgG from these samples showed at least a two-fold change in reactivity over naïve controls in 27 of 34 antigens tested, and the majority showed heat-labile IgG immunoreactivity, suggesting the presence of conformation-sensitive epitopes and native tertiary protein structures. Using a method specifically designed to detect low-affinity, extracellular protein-protein interactions, we confirmed a predicted interaction between P. vivax 6-cysteine proteins P12 and P41, further suggesting that the proteins

  9. A case study of evolutionary computation of biochemical adaptation

    Science.gov (United States)

    François, Paul; Siggia, Eric D.

    2008-06-01

    Simulations of evolution have a long history, but their relation to biology is questioned because of the perceived contingency of evolution. Here we provide an example of a biological process, adaptation, where simulations are argued to approach closer to biology. Adaptation is a common feature of sensory systems, and a plausible component of other biochemical networks because it rescales upstream signals to facilitate downstream processing. We create random gene networks numerically, by linking genes with interactions that model transcription, phosphorylation and protein-protein association. We define a fitness function for adaptation in terms of two functional metrics, and show that any reasonable combination of them will yield the same adaptive networks after repeated rounds of mutation and selection. Convergence to these networks is driven by positive selection and thus fast. There is always a path in parameter space of continuously improving fitness that leads to perfect adaptation, implying that the actual mutation rates we use in the simulation do not bias the results. Our results imply a kinetic view of evolution, i.e., it favors gene networks that can be learned quickly from the random examples supplied by mutation. This formulation allows for deductive predictions of the networks realized in nature.

  10. Detecting reliable non interacting proteins (NIPs) significantly enhancing the computational prediction of protein-protein interactions using machine learning methods.

    Science.gov (United States)

    Srivastava, A; Mazzocco, G; Kel, A; Wyrwicz, L S; Plewczynski, D

    2016-03-01

    Protein-protein interactions (PPIs) play a vital role in most biological processes. Hence their comprehension can promote a better understanding of the mechanisms underlying living systems. However, besides the cost and the time limitation involved in the detection of experimentally validated PPIs, the noise in the data is still an important issue to overcome. In the last decade several in silico PPI prediction methods using both structural and genomic information were developed for this purpose. Here we introduce a unique validation approach aimed to collect reliable non interacting proteins (NIPs). Thereafter the most relevant protein/protein-pair related features were selected. Finally, the prepared dataset was used for PPI classification, leveraging the prediction capabilities of well-established machine learning methods. Our best classification procedure displayed specificity and sensitivity values of 96.33% and 98.02%, respectively, surpassing the prediction capabilities of other methods, including those trained on gold standard datasets. We showed that the PPI/NIP predictive performances can be considerably improved by focusing on data preparation.

  11. Toward a rigorous network of protein-protein interactions of the model sulfate reducer Desulfovibrio vulgaris Hildenborough

    Energy Technology Data Exchange (ETDEWEB)

    Chhabra, S.R.; Joachimiak, M.P.; Petzold, C.J.; Zane, G.M.; Price, M.N.; Gaucher, S.; Reveco, S.A.; Fok, V.; Johanson, A.R.; Batth, T.S.; Singer, M.; Chandonia, J.M.; Joyner, D.; Hazen, T.C.; Arkin, A.P.; Wall, J.D.; Singh, A.K.; Keasling, J.D.

    2011-05-01

    Protein–protein interactions offer an insight into cellular processes beyond what may be obtained by the quantitative functional genomics tools of proteomics and transcriptomics. The aforementioned tools have been extensively applied to study E. coli and other aerobes and more recently to study the stress response behavior of Desulfovibrio 5 vulgaris Hildenborough, a model anaerobe and sulfate reducer. In this paper we present the first attempt to identify protein-protein interactions in an obligate anaerobic bacterium. We used suicide vector-assisted chromosomal modification of 12 open reading frames encoded by this sulfate reducer to append an eight amino acid affinity tag to the carboxy-terminus of the chosen proteins. Three biological replicates of the 10 ‘pulled-down’ proteins were separated and analyzed using liquid chromatography-mass spectrometry. Replicate agreement ranged between 35% and 69%. An interaction network among 12 bait and 90 prey proteins was reconstructed based on 134 bait-prey interactions computationally identified to be of high confidence. We discuss the biological significance of several unique metabolic features of D. vulgaris revealed by this protein-protein interaction data 15 and protein modifications that were observed. These include the distinct role of the putative carbon monoxide-induced hydrogenase, unique electron transfer routes associated with different oxidoreductases, and the possible role of methylation in regulating sulfate reduction.

  12. Dissecting the specificity of protein-protein interaction in bacterial two-component signaling: orphans and crosstalks.

    Directory of Open Access Journals (Sweden)

    Andrea Procaccini

    Full Text Available Predictive understanding of the myriads of signal transduction pathways in a cell is an outstanding challenge of systems biology. Such pathways are primarily mediated by specific but transient protein-protein interactions, which are difficult to study experimentally. In this study, we dissect the specificity of protein-protein interactions governing two-component signaling (TCS systems ubiquitously used in bacteria. Exploiting the large number of sequenced bacterial genomes and an operon structure which packages many pairs of interacting TCS proteins together, we developed a computational approach to extract a molecular interaction code capturing the preferences of a small but critical number of directly interacting residue pairs. This code is found to reflect physical interaction mechanisms, with the strongest signal coming from charged amino acids. It is used to predict the specificity of TCS interaction: Our results compare favorably to most available experimental results, including the prediction of 7 (out of 8 known interaction partners of orphan signaling proteins in Caulobacter crescentus. Surveying among the available bacterial genomes, our results suggest 15∼25% of the TCS proteins could participate in out-of-operon "crosstalks". Additionally, we predict clusters of crosstalking candidates, expanding from the anecdotally known examples in model organisms. The tools and results presented here can be used to guide experimental studies towards a system-level understanding of two-component signaling.

  13. Prediction of protein-protein interactions using chaos game representation and wavelet transform via the random forest algorithm.

    Science.gov (United States)

    Jia, J H; Liu, Z; Chen, X; Xiao, X; Liu, B X

    2015-10-02

    Studying the network of protein-protein interactions (PPIs) will provide valuable insights into the inner workings of cells. It is vitally important to develop an automated, high-throughput tool that efficiently predicts protein-protein interactions. This study proposes a new model for PPI prediction based on the concept of chaos game representation and the wavelet transform, which means that a considerable amount of sequence-order effects can be incorporated into a set of discrete numbers. The advantage of using chaos game representation and the wavelet transform to formulate the protein sequence is that it can more effectively reflect its overall sequence-order characteristics than the conventional correlation factors. Using such a formulation frame to represent the protein sequences means that the random forest algorithm can be used to conduct the prediction. The results for a large-scale independent test dataset show that the proposed model can achieve an excellent performance with an accuracy value of about 0.86 and a geometry mean value of about 0.85. The model is therefore a useful supplementary tool for PPI predictions. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI.

  14. Reverse Nearest Neighbor Search on a Protein-Protein Interaction Network to Infer Protein-Disease Associations.

    Science.gov (United States)

    Suratanee, Apichat; Plaimas, Kitiporn

    2017-01-01

    The associations between proteins and diseases are crucial information for investigating pathological mechanisms. However, the number of known and reliable protein-disease associations is quite small. In this study, an analysis framework to infer associations between proteins and diseases was developed based on a large data set of a human protein-protein interaction network integrating an effective network search, namely, the reverse k-nearest neighbor (RkNN) search. The RkNN search was used to identify an impact of a protein on other proteins. Then, associations between proteins and diseases were inferred statistically. The method using the RkNN search yielded a much higher precision than a random selection, standard nearest neighbor search, or when applying the method to a random protein-protein interaction network. All protein-disease pair candidates were verified by a literature search. Supporting evidence for 596 pairs was identified. In addition, cluster analysis of these candidates revealed 10 promising groups of diseases to be further investigated experimentally. This method can be used to identify novel associations to better understand complex relationships between proteins and diseases.

  15. Small molecules, peptides and natural products: getting a grip on 14-3-3 protein-protein modulation.

    Science.gov (United States)

    Bartel, Maria; Schäfer, Anja; Stevers, Loes M; Ottmann, Christian

    2014-05-01

    One of the proteins that is found in a diverse range of eukaryotic protein-protein interactions is the adaptor protein 14-3-3. As 14-3-3 is a hub protein with very diverse interactions, it is a good model to study various protein-protein interactions. A wide range of classes of molecules, peptides, small molecules or natural products, has been used to modify the protein interactions, providing both stabilization or inhibition of the interactions of 14-3-3 with its binding partners. The first protein crystal structures were solved in 1995 and gave molecular insights for further research. The plant analog of 14-3-3 binds to a plant plasma membrane H(+)-ATPase and this protein complex is stabilized by the fungal phytotoxin fusicoccin A. The knowledge gained from the process in plants was transferred to and applied in human models to find stabilizers or inhibitors of 14-3-3 interaction in human cellular pathways.

  16. Insight into structural organization and protein-protein interaction of non structural 3 (NS3) proteins from dengue serotypes.

    Science.gov (United States)

    Parida, Pratap; Yadav, R N S; Sarma, Kishore

    2014-01-01

    Dengue infections produce a distinct character of virus-induced intracellular membrane alterations which are associated with the viral replication machinery. Currently, the NS3 protein is being targeted for antiviral therapy against dengue. NS3 protein of dengue virus interacts with nuclear receptor binding protein (NRBP) of human causing cell trafficking between the Endoplasmic Reticulum (ER) and Golgi, which interacts with Rac3, a member of the Rho-GTPase family. No crystal structure of the NRBP is available for any species, thus limiting the complete understanding of structure- function relationships of this protein. The present study deals with the molecular modeling of the viral protein (NS3 of DENV1-4), the host protein (NRBP) and their interactions through protein-protein docking study. Theoretical threedimensional structures of the NRBP and NS3 were modeled using the Modeller 9v8, and the evaluated models were docked using GRAMM-X to study the mode of protein-protein interaction (NRBP as receptor and NS3 as ligand). The docked docking complexes were further evaluated for interaction analysis by the RosettaDock Server. Suface and interface residues were observed along with hydrogen and hydrophobic interaction. The conserved residues forming hydrogen interaction of NRBP with DENV1-4 serotypes were found to be GLN 305, SER 363 and GLN 379.

  17. Expression of mRNA and protein-protein interaction of the antiviral endoribonuclease RNase L in mouse spleen.

    Science.gov (United States)

    Gupta, Ankush; Rath, Pramod C

    2014-08-01

    The interferon-inducible, 2',5'-oligoadenylate (2-5A)-dependent endoribonuclease, RNase L is a unique antiviral RNA-degrading enzyme involved in RNA-metabolism, translational regulation, stress-response besides its anticancer/tumor-suppressor and antibacterial functions. RNase L represents complex cellular RNA-regulations in mammalian cells but diverse functions of RNase L are not completely explained by its 2-5A-regulated endoribonuclease activity. We hypothesized that RNase L has housekeeping function(s) through interaction with cellular proteins. We investigated RNase L mRNA expression in mouse tissues by RT-PCR and its protein-protein interaction in spleen by GST-pulldown and immunoprecipitation assays followed by proteomic analysis. RNase L mRNA is constitutively and differentially expressed in nine different mouse tissues, its level is maximum in immunological tissues (spleen, thymus and lungs), moderate in reproductive tissues (testis and prostate) and low in metabolic tissues (kidney, brain, liver and heart). Cellular proteins from mouse spleen [fibronectin precursor, β-actin, troponin I, myosin heavy chain 9 (non-muscle), growth-arrest specific protein 11, clathrin light chain B, a putative uncharacterized protein (Ricken cDNA 8030451F13) isoform (CRA_d) and alanyl tRNA synthetase] were identified as cellular RNase L-interacting proteins. Thus our results suggest for more general cellular functions of RNase L through protein-protein interactions in the spleen for immune response in mammals.

  18. Complex structure of cytochrome c-cytochrome c oxidase reveals a novel protein-protein interaction mode.

    Science.gov (United States)

    Shimada, Satoru; Shinzawa-Itoh, Kyoko; Baba, Junpei; Aoe, Shimpei; Shimada, Atsuhiro; Yamashita, Eiki; Kang, Jiyoung; Tateno, Masaru; Yoshikawa, Shinya; Tsukihara, Tomitake

    2017-02-01

    Mitochondrial cytochrome c oxidase (CcO) transfers electrons from cytochrome c (Cyt.c) to O2 to generate H2O, a process coupled to proton pumping. To elucidate the mechanism of electron transfer, we determined the structure of the mammalian Cyt.c-CcO complex at 2.0-Å resolution and identified an electron transfer pathway from Cyt.c to CcO. The specific interaction between Cyt.c and CcO is stabilized by a few electrostatic interactions between side chains within a small contact surface area. Between the two proteins are three water layers with a long inter-molecular span, one of which lies between the other two layers without significant direct interaction with either protein. Cyt.c undergoes large structural fluctuations, using the interacting regions with CcO as a fulcrum. These features of the protein-protein interaction at the docking interface represent the first known example of a new class of protein-protein interaction, which we term "soft and specific". This interaction is likely to contribute to the rapid association/dissociation of the Cyt.c-CcO complex, which facilitates the sequential supply of four electrons for the O2 reduction reaction.

  19. Cell-free Protein Synthesis in an Autoinduction System for NMR Studies of Protein-Protein Interactions

    Energy Technology Data Exchange (ETDEWEB)

    Ozawa, Kiyoshi; Jergic, Slobodan; Crowther, Jeffrey A.; Thompson, Phillip R. [Australian National University, Research School of Chemistry (Australia); Wijffels, Gene [Queensland Bioscience Precinct, CSIRO Livestock Industries (Australia); Otting, Gottfried; Dixon, Nicholas A. [Australian National University, Research School of Chemistry (Australia)], E-mail: dixon@rsc.anu.edu.au

    2005-07-15

    Cell-free protein synthesis systems provide facile access to proteins in a nascent state that enables formation of soluble, native protein-protein complexes even if one of the protein components is prone to self-aggregation and precipitation. Combined with selective isotope-labeling, this allows the rapid analysis of protein-protein interactions with few {sup 15}N-HSQC spectra. The concept is demonstrated with binary and ternary complexes between the {chi}, {psi} and {gamma} subunits of Escherichia coli DNA polymerase III: nascent, selectively {sup 15}N-labeled {psi} produced in the presence of {chi} resulted in a soluble, correctly folded {chi}-{psi} complex, whereas {psi} alone precipitated irrespective of whether {gamma} was present or not. The {sup 15}N-HSQC spectra showed that the N-terminal segment of {psi} is mobile in the {chi}-{psi} complex, yet important for its binding to {gamma}. The sample preparation was greatly enhanced by an autoinduction strategy, where the T7 RNA polymerase needed for transcription of a gene in a T7-promoter vector was produced in situ.

  20. A review on protein-protein interaction network of APE1/Ref-1 and its associated biological functions.

    Science.gov (United States)

    Thakur, S; Dhiman, M; Tell, G; Mantha, A K

    2015-04-01

    Apurinic/apyrimidinic endonuclease 1 (APE1) is a classic example of functionally variable protein. Besides its well-known role in (i) DNA repair of oxidative base damage, APE1 also plays a critical role in (ii) redox regulation of transcription factors controlling gene expression for cell survival pathways, for which it is also known as redox effector factor 1 (Ref-1), and recent evidences advocates for (iii) coordinated control of other non-canonical protein-protein interaction(s) responsible for significant biological functions in mammalian cells. The diverse functions of APE1 can be ascribed to its ability to interact with different protein partners, owing to the attainment of unfolded domains during evolution. Association of dysregulation of APE1 with various human pathologies, such as cancer, cardiovascular diseases and neurodegeneration, is attributable to its multifunctional nature, and this makes APE1 a potential therapeutic target. This review covers the important aspects of APE1 in terms of its significant protein-protein interaction(s), and this knowledge is required to understand the onset and development of human pathologies and to design or improve the strategies to target such interactions for treatment and management of various human diseases.

  1. Enhancements to the Rosetta Energy Function Enable Improved Identification of Small Molecules that Inhibit Protein-Protein Interactions.

    Directory of Open Access Journals (Sweden)

    Andrea Bazzoli

    Full Text Available Protein-protein interactions are among today's most exciting and promising targets for therapeutic intervention. To date, identifying small-molecules that selectively disrupt these interactions has proven particularly challenging for virtual screening tools, since these have typically been optimized to perform well on more "traditional" drug discovery targets. Here, we test the performance of the Rosetta energy function for identifying compounds that inhibit protein interactions, when these active compounds have been hidden amongst pools of "decoys." Through this virtual screening benchmark, we gauge the effect of two recent enhancements to the functional form of the Rosetta energy function: the new "Talaris" update and the "pwSHO" solvation model. Finally, we conclude by developing and validating a new weight set that maximizes Rosetta's ability to pick out the active compounds in this test set. Looking collectively over the course of these enhancements, we find a marked improvement in Rosetta's ability to identify small-molecule inhibitors of protein-protein interactions.

  2. Investigating the importance of Delaunay-based definition of atomic interactions in scoring of protein-protein docking results.

    Science.gov (United States)

    Jafari, Rahim; Sadeghi, Mehdi; Mirzaie, Mehdi

    2016-05-01

    The approaches taken to represent and describe structural features of the macromolecules are of major importance when developing computational methods for studying and predicting their structures and interactions. This study attempts to explore the significance of Delaunay tessellation for the definition of atomic interactions by evaluating its impact on the performance of scoring protein-protein docking prediction. Two sets of knowledge-based scoring potentials are extracted from a training dataset of native protein-protein complexes. The potential of the first set is derived using atomic interactions extracted from Delaunay tessellated structures. The potential of the second set is calculated conventionally, that is, using atom pairs whose interactions were determined by their separation distances. The scoring potentials were tested against two different docking decoy sets and their performances were compared. The results show that, if properly optimized, the Delaunay-based scoring potentials can achieve higher success rate than the usual scoring potentials. These results and the results of a previous study on the use of Delaunay-based potentials in protein fold recognition, all point to the fact that Delaunay tessellation of protein structure can provide a more realistic definition of atomic interaction, and therefore, if appropriately utilized, may be able to improve the accuracy of pair potentials.

  3. PETs: A Stable and Accurate Predictor of Protein-Protein Interacting Sites Based on Extremely-Randomized Trees.

    Science.gov (United States)

    Xia, Bin; Zhang, Hong; Li, Qianmu; Li, Tao

    2015-12-01

    Protein-protein interaction (PPI) plays crucial roles in the performance of various biological processes. A variety of methods are dedicated to identify whether proteins have interaction residues, but it is often more crucial to recognize each amino acid. In practical applications, the stability of a prediction model is as important as its accuracy. However, random sampling, which is widely used in previous prediction models, often brings large difference between each training model. In this paper, a Predictor of protein-protein interaction sites based on Extremely-randomized Trees (PETs) is proposed to improve the prediction accuracy while maintaining the prediction stability. In PETs, a cluster-based sampling strategy is proposed to ensure the model stability: first, the training dataset is divided into subsets using specific features; second, the subsets are clustered using K-means; and finally the samples are selected from each cluster. Using the proposed sampling strategy, samples which have different types of significant features could be selected independently from different clusters. The evaluation shows that PETs is able to achieve better accuracy while maintaining a good stability. The source code and toolkit are available at https://github.com/BinXia/PETs.

  4. Protein-spanning water networks and implications for prediction of protein-protein interactions mediated through hydrophobic effects.

    Science.gov (United States)

    Cui, Di; Ou, Shuching; Patel, Sandeep

    2014-12-01

    Hydrophobic effects, often conflated with hydrophobic forces, are implicated as major determinants in biological association and self-assembly processes. Protein-protein interactions involved in signaling pathways in living systems are a prime example where hydrophobic effects have profound implications. In the context of protein-protein interactions, a priori knowledge of relevant binding interfaces (i.e., clusters of residues involved directly with binding interactions) is difficult. In the case of hydrophobically mediated interactions, use of hydropathy-based methods relying on single residue hydrophobicity properties are routinely and widely used to predict propensities for such residues to be present in hydrophobic interfaces. However, recent studies suggest that consideration of hydrophobicity for single residues on a protein surface require accounting of the local environment dictated by neighboring residues and local water. In this study, we use a method derived from percolation theory to evaluate spanning water networks in the first hydration shells of a series of small proteins. We use residue-based water density and single-linkage clustering methods to predict hydrophobic regions of proteins; these regions are putatively involved in binding interactions. We find that this simple method is able to predict with sufficient accuracy and coverage the binding interface residues of a series of proteins. The approach is competitive with automated servers. The results of this study highlight the importance of accounting of local environment in determining the hydrophobic nature of individual residues on protein surfaces.

  5. Assessment of the Protein-Protein Interactions in a Highly Concentrated Antibody Solution by Using Raman Spectroscopy.

    Science.gov (United States)

    Ota, Chikashi; Noguchi, Shintaro; Nagatoishi, Satoru; Tsumoto, Kouhei

    2016-04-01

    To investigate the protein-protein interactions of a highly concentrated antibody solution that could cause oligomerization or aggregation and to develop a better understanding of the optimization of drug formulations. In this study, we used Raman spectroscopy to investigate the structure and interactions of a highly concentrated antibody solution over a wide range of concentrations (10-200 mg/mL) with the aid of a multivariate analysis. Our analysis of the amide I band, I 856 /I 830 of Tyr, and the relative intensity at 1004 cm(-1) of the Phe and OH stretching region at around 3000 cm(-1) showed that across this wide range of concentrations, the secondary structure of the IgG molecules did not change; however, short-range attractive interactions around the Tyr and Phe residues occurred as the distance between the IgG molecules decreased with increasing concentration. Analysis of the OH stretching region at around 3000 cm(-1) showed that these short-range attractive interactions correlated with the amount of hydrated water around the IgG molecules. Our data show that Raman spectroscopy can provide valuable information of the protein-protein interactions based on conformational approaches to support conventional colloidal approaches, especially for analyses of highly concentrated solutions.

  6. Biochemical regulators in cardiac hypertrophy.

    Science.gov (United States)

    Kölbel, F; Schreiber, V

    1983-01-01

    In recent years research has shown that muscle is capable of reacting to mechanical stimuli by altering biochemical processes. Myocardium is probably the source of a biochemical factor, or factors which activate myocardial protein synthesis. In experimentally induced cardiac hypertrophy adaptive alterations have been shown to occur not only in the adrenal medulla but also in the adrenal cortex. Finally, detection of cross reactivity between digitalis glycosides and a number of steroid hormones has succeeded. We assume that such cross reactivity indicates the existence of an endogenic factor of steroid character, which is produced in the adrenal gland and functions as an endogenic cardiotonic agent. During experimental cardiac hypertrophy its synthesis is possibly increased. We propose the term "endocardin" or "endocardiotonin" for this agent.

  7. Biochemical Engineering. Part II: Process Design

    Science.gov (United States)

    Atkinson, B.

    1972-01-01

    Describes types of industrial techniques involving biochemical products, specifying the advantages and disadvantages of batch and continuous processes, and contrasting biochemical and chemical engineering. See SE 506 318 for Part I. (AL)

  8. Development of Biochemical Cyanide Antidotes.

    Science.gov (United States)

    1992-03-01

    The long accepted mechanism of action for thiosulfate is that it serves as a sulfane-sulfur donor, in the presence of the mitochondrial enzyme...capable of identifying biochemical cyanide antidotes, but would not be expected to detect sulfur donors or methemoglobin formers. Blockade of mitochondrial ...549-558. Yamamoto, H. (1989). Hyperammonemia, increased brain neutral and aromauic amino acid levels and encephalopathy induced by cyanide in mice

  9. Associative learning in biochemical networks

    OpenAIRE

    Ghandi, Nikhil; Ashkenasy, Gonen; Tannenbaum, Emmanuel

    2007-01-01

    We develop a simple, chemostat-based model illustrating how a process analogous to associative learning can occur in a biochemical network. Associative learning is a form of learning whereby a system "learns" to associate two stimuli with one another. In our model, two types of replicating molecules, denoted A and B, are present in some initial concentration in the chemostat. Molecules A and B are stimulated to replicate by some growth factors, denoted GA and GB, respectively. It is also assu...

  10. Biochemical markers of bone turnover

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Deog Yoon [College of Medicine, Kyunghee Univ., Seoul (Korea, Republic of)

    1999-08-01

    Biochemical markers of bone turnover has received increasing attention over the past few years, because of the need for sensitivity and specific tool in the clinical investigation of osteoporosis. Bone markers should be unique to bone, reflect changes of bone less, and should be correlated with radiocalcium kinetics, histomorphometry, or changes in bone mass. The markers also should be useful in monitoring treatment efficacy. Although no bone marker has been established to meet all these criteria, currently osteocalcin and pyridinium crosslinks are the most efficient markers to assess the level of bone turnover in the menopausal and senile osteoporosis. Recently, N-terminal telopeptide (NTX), C-terminal telopeptide (CTX) and bone specific alkaline phosphatase are considered as new valid markers of bone turnover. Recent data suggest that CTX and free deoxypyridinoline could predict the subsequent risk of hip fracture of elderly women. Treatment of postmenopausal women with estrogen, calcitonin and bisphosphonates demonstrated rapid decrease of the levels of bone markers that correlated with the long-term increase of bone mass. Factors such as circadian rhythms, diet, age, sex, bone mass and renal function affect the results of biochemical markers and should be appropriately adjusted whenever possible. Each biochemical markers of bone turnover may have its own specific advantages and limitations. Recent advances in research will provide more sensitive and specific assays.

  11. Biochemical adaptation to ocean acidification.

    Science.gov (United States)

    Stillman, Jonathon H; Paganini, Adam W

    2015-06-01

    The change in oceanic carbonate chemistry due to increased atmospheric PCO2  has caused pH to decline in marine surface waters, a phenomenon known as ocean acidification (OA). The effects of OA on organisms have been shown to be widespread among diverse taxa from a wide range of habitats. The majority of studies of organismal response to OA are in short-term exposures to future levels of PCO2 . From such studies, much information has been gathered on plastic responses organisms may make in the future that are beneficial or harmful to fitness. Relatively few studies have examined whether organisms can adapt to negative-fitness consequences of plastic responses to OA. We outline major approaches that have been used to study the adaptive potential for organisms to OA, which include comparative studies and experimental evolution. Organisms that inhabit a range of pH environments (e.g. pH gradients at volcanic CO2 seeps or in upwelling zones) have great potential for studies that identify adaptive shifts that have occurred through evolution. Comparative studies have advanced our understanding of adaptation to OA by linking whole-organism responses with cellular mechanisms. Such optimization of function provides a link between genetic variation and adaptive evolution in tuning optimal function of rate-limiting cellular processes in different pH conditions. For example, in experimental evolution studies of organisms with short generation times (e.g. phytoplankton), hundreds of generations of growth under future conditions has resulted in fixed differences in gene expression related to acid-base regulation. However, biochemical mechanisms for adaptive responses to OA have yet to be fully characterized, and are likely to be more complex than simply changes in gene expression or protein modification. Finally, we present a hypothesis regarding an unexplored area for biochemical adaptation to ocean acidification. In this hypothesis, proteins and membranes exposed to the

  12. Free Energy Landscape of Protein-Protein Encounter Resulting from Brownian Dynamics Simulations of Barnase:Barstar.

    Science.gov (United States)

    Spaar, Alexander; Helms, Volkhard

    2005-07-01

    Over the past years Brownian dynamics (BD) simulations have been proven to be a suitable tool for the analysis of protein-protein association. The computed rates and relative trends for protein mutants and different ionic strength are generally in good agreement with experimental results, e.g. see ref 1. By design, BD simulations correspond to an intensive sampling over energetically favorable states, rather than to a systematic sampling over all possible states which is feasible only at rather low resolution. On the example of barnase and barstar, a well characterized model system of electrostatically steered diffusional encounter, we report here the computation of the 6-dimensional free energy landscape for the encounter process of two proteins by a novel, careful analysis of the trajectories from BD simulations. The aim of these studies was the clarification of the encounter state. Along the trajectories, the individual positions and orientations of one protein (relative to the other) are recorded and stored in so-called occupancy maps. Since the number of simulated trajectories is sufficiently high, these occupancy maps can be interpreted as a probability distribution which allows the calculation of the entropy landscape by the use of a locally defined entropy function. Additionally, the configuration dependent electrostatic and desolvation energies are recorded in separate maps. The free energy landscape of protein-protein encounter is finally obtained by summing the energy and entropy contributions. In the free energy profile along the reaction path, which is defined as the path along the minima in the free energy landscape, a minimum shows up suggesting this to be used as the definition of the encounter state. This minimum describes a state of reduced diffusion velocity where the electrostatic attraction is compensated by the repulsion due to the unfavorable desolvation of the charged residues and the entropy loss due to the increasing restriction of the

  13. Vectors for multi-color bimolecular fluorescence complementation to investigate protein-protein interactions in living plant cells

    Directory of Open Access Journals (Sweden)

    Kuang Lin-Yun

    2008-10-01

    Full Text Available Abstract Background The investigation of protein-protein interactions is important for characterizing protein function. Bimolecular fluorescence complementation (BiFC has recently gained interest as a relatively easy and inexpensive method to visualize protein-protein interactions in living cells. BiFC uses "split YFP" tags on proteins to detect interactions: If the tagged proteins interact, they may bring the two split fluorophore components together such that they can fold and reconstitute fluorescence. The sites of interaction can be monitored using epifluorescence or confocal microscopy. However, "conventional" BiFC can investigate interactions only between two proteins at a time. There are instances when one may wish to offer a particular "bait" protein to several "prey" proteins simultaneously. Preferential interaction of the bait protein with one of the prey proteins, or different sites of interaction between the bait protein and multiple prey proteins, may thus be observed. Results We have constructed a series of gene expression vectors, based upon the pSAT series of vectors, to facilitate the practice of multi-color BiFC. The bait protein is tagged with the C-terminal portion of CFP (cCFP, and prey proteins are tagged with the N-terminal portions of either Venus (nVenus or Cerulean (nCerulean. Interaction of cCFP-tagged proteins with nVenus-tagged proteins generates yellow fluorescence, whereas interaction of cCFP-tagged proteins with nCerulean-tagged proteins generates blue fluorescence. Additional expression of mCherry indicates transfected cells and sub-cellular structures. Using this system, we have determined in both tobacco BY-2 protoplasts and in onion epidermal cells that Agrobacterium VirE2 protein interacts with the Arabidopsis nuclear transport adapter protein importin α-1 in the cytoplasm, whereas interaction of VirE2 with a different importin α isoform, importin α-4, occurs predominantly in the nucleus. Conclusion Multi

  14. Arabidopsis mRNA polyadenylation machinery: comprehensive analysis of protein-protein interactions and gene expression profiling

    Directory of Open Access Journals (Sweden)

    Mo Min

    2008-05-01

    Full Text Available Abstract Background The polyadenylation of mRNA is one of the critical processing steps during expression of almost all eukaryotic genes. It is tightly integrated with transcription, particularly its termination, as well as other RNA processing events, i.e. capping and splicing. The poly(A tail protects the mRNA from unregulated degradation, and it is required for nuclear export and translation initiation. In recent years, it has been demonstrated that the polyadenylation process is also involved in the regulation of gene expression. The polyadenylation process requires two components, the cis-elements on the mRNA and a group of protein factors that recognize the cis-elements and produce the poly(A tail. Here we report a comprehensive pairwise protein-protein interaction mapping and gene expression profiling of the mRNA polyadenylation protein machinery in Arabidopsis. Results By protein sequence homology search using human and yeast polyadenylation factors, we identified 28 proteins that may be components of Arabidopsis polyadenylation machinery. To elucidate the protein network and their functions, we first tested their protein-protein interaction profiles. Out of 320 pair-wise protein-protein interaction assays done using the yeast two-hybrid system, 56 (~17% showed positive interactions. 15 of these interactions were further tested, and all were confirmed by co-immunoprecipitation and/or in vitro co-purification. These interactions organize into three distinct hubs involving the Arabidopsis polyadenylation factors. These hubs are centered around AtCPSF100, AtCLPS, and AtFIPS. The first two are similar to complexes seen in mammals, while the third one stands out as unique to plants. When comparing the gene expression profiles extracted from publicly available microarray datasets, some of the polyadenylation related genes showed tissue-specific expression, suggestive of potential different polyadenylation complex configurations. Conclusion An

  15. Community-wide Evaluation of Methods for Predicting the Effect of Mutations on Protein-Protein Interactions

    Science.gov (United States)

    Moretti, Rocco; Fleishman, Sarel J.; Agius, Rudi; Torchala, Mieczyslaw; Bates, Paul A.; Kastritis, Panagiotis L.; Rodrigues, João P. G. L. M.; Trellet, Mikaël; Bonvin, Alexandre M. J. J.; Cui, Meng; Rooman, Marianne; Gillis, Dimitri; Dehouck, Yves; Moal, Iain; Romero-Durana, Miguel; Perez-Cano, Laura; Pallara, Chiara; Jimenez, Brian; Fernandez-Recio, Juan; Flores, Samuel; Pacella, Michael; Kilambi, Krishna Praneeth; Gray, Jeffrey J.; Popov, Petr; Grudinin, Sergei; Esquivel-Rodríguez, Juan; Kihara, Daisuke; Zhao, Nan; Korkin, Dmitry; Zhu, Xiaolei; Demerdash, Omar N. A.; Mitchell, Julie C.; Kanamori, Eiji; Tsuchiya, Yuko; Nakamura, Haruki; Lee, Hasup; Park, Hahnbeom; Seok, Chaok; Sarmiento, Jamica; Liang, Shide; Teraguchi, Shusuke; Standley, Daron M.; Shimoyama, Hiromitsu; Terashi, Genki; Takeda-Shitaka, Mayuko; Iwadate, Mitsuo; Umeyama, Hideaki; Beglov, Dmitri; Hall, David R.; Kozakov, Dima; Vajda, Sandor; Pierce, Brian G.; Hwang, Howook; Vreven, Thom; Weng, Zhiping; Huang, Yangyu; Li, Haotian; Yang, Xiufeng; Ji, Xiaofeng; Liu, Shiyong; Xiao, Yi; Zacharias, Martin; Qin, Sanbo; Zhou, Huan-Xiang; Huang, Sheng-You; Zou, Xiaoqin; Velankar, Sameer; Janin, Joël; Wodak, Shoshana J.; Baker, David

    2014-01-01

    Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side chain sampling and backbone relaxation, and evaluated packing, electrostatic and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of methodological improvement. PMID:23843247

  16. A generalized free-solvent model for the osmotic pressure of multi-component solutions containing protein-protein interactions.

    Science.gov (United States)

    McBride, Devin W; Rodgers, V G J

    2014-07-01

    The free-solvent model has been shown to have excellent predictability of the osmotic pressure for single and binary non-interactive proteins in aqueous solutions. Here the free-solvent model is extended to be more generalized by including the contributions of intra- and inter-protein interactions to the osmotic pressure of a solution in the form of homo- and hetero-multimers. The solute-solvent interactions are considered to be unique for each homo- and hetero-multimer in solution. The effect of the various generalized free-solvent model parameters on the osmotic pressure are examined for a single protein solution with a homo-dimer, a binary protein solution with no protein-protein interactions, and a binary protein solution with a hetero-dimer. Finally, the limitations associated with the generalized free-solvent model are discussed.

  17. Statistical analysis on protein-protein interface in crystals:Specific and non-specific interfaces are differentially distributed

    Institute of Scientific and Technical Information of China (English)

    FENG Dan; ZENG Zonghao

    2004-01-01

    The distribution of contact areas, or fractions of contacting, of protein-protein interfaces in crystals of pure polypeptides contains two components: a major exponential distribution and a minor flatter distribution. Suppose the two components belong to specific and non-specific contacts, respectively, then the probability of a contact with a given area, or fraction of contacting, can be estimated. By dividing the whole database into two sub-databases, one of them is known to contain more specific contacts than the other, this hypothesis is confirmed and it is also proved that the fraction of contacting is more effective than the contact area on discriminating specific and non-specific contacts in protein crystals.

  18. Electrostatic similarities between protein and small molecule ligands facilitate the design of protein-protein interaction inhibitors.

    Directory of Open Access Journals (Sweden)

    Arnout Voet

    Full Text Available One of the underlying principles in drug discovery is that a biologically active compound is complimentary in shape and molecular recognition features to its receptor. This principle infers that molecules binding to the same receptor may share some common features. Here, we have investigated whether the electrostatic similarity can be used for the discovery of small molecule protein-protein interaction inhibitors (SMPPIIs. We have developed a method that can be used to evaluate the similarity of electrostatic potentials between small molecules and known protein ligands. This method was implemented in a software called EleKit. Analyses of all available (at the time of research SMPPII structures indicate that SMPPIIs bear some similarities of electrostatic potential with the ligand proteins of the same receptor. This is especially true for the more polar SMPPIIs. Retrospective analysis of several successful SMPPIIs has shown the applicability of EleKit in the design of new SMPPIIs.

  19. Prioritization of candidate genes for cattle reproductive traits, based on protein-protein interactions, gene expression, and text-mining

    DEFF Research Database (Denmark)

    Hulsegge, Ina; Woelders, Henri; Smits, Mari

    2013-01-01

    and processes in brain areas and pituitary involved in reproductive traits in cattle using information derived from three different data sources: gene expression, protein-protein interactions, and literature. We identified 59, 89, 53, 23, and 71 genes in bovine amygdala, dorsal hypothalamus, hippocampus......, pituitary, and ventral hypothalamus, respectively, potentially involved in processes underlying estrus and estrous behavior. Functional annotation of the candidate genes points to a number of tissue-specific processes of which the "neurotransmitter/ion channel/synapse" process in the amygdala, "steroid...... hormone receptor activity/ion binding" in the pituitary, "extracellular region" in the ventral hypothalamus, and "positive regulation of transcription/metabolic process" in the dorsal hypothalamus are most prominent. The regulation of the functional processes in the various tissues operate at different...

  20. Application of Enhanced Sampling Monte Carlo Methods for High-Resolution Protein-Protein Docking in Rosetta.

    Directory of Open Access Journals (Sweden)

    Zhe Zhang

    Full Text Available The high-resolution refinement of docked protein-protein complexes can provide valuable structural and mechanistic insight into protein complex formation complementing experiment. Monte Carlo (MC based approaches are frequently applied to sample putative interaction geometries of proteins including also possible conformational changes of the binding partners. In order to explore efficiency improvements of the MC sampling, several enhanced sampling techniques, including temperature or Hamiltonian replica exchange and well-tempered ensemble approaches, have been combined with the MC method and were evaluated on 20 protein complexes using unbound partner structures. The well-tempered ensemble method combined with a 2-dimensional temperature and Hamiltonian replica exchange scheme (WTE-H-REMC was identified as the most efficient search strategy. Comparison with prolonged MC searches indicates that the WTE-H-REMC approach requires approximately 5 times fewer MC steps to identify near native docking geometries compared to conventional MC searches.

  1. Structure-Based Design and Synthesis of Potent Cyclic Peptides Inhibiting the YAP-TEAD Protein-Protein Interaction.

    Science.gov (United States)

    Zhang, Zhisen; Lin, Zhaohu; Zhou, Zheng; Shen, Hong C; Yan, S Frank; Mayweg, Alexander V; Xu, Zhiheng; Qin, Ning; Wong, Jason C; Zhang, Zhenshan; Rong, Yiping; Fry, David C; Hu, Taishan

    2014-09-11

    The YAP-TEAD protein-protein interaction (PPI) mediates the oncogenic function of YAP, and inhibitors of this PPI have potential usage in treatment of YAP-involved cancers. Here we report the design and synthesis of potent cyclic peptide inhibitors of the YAP-TEAD interaction. A truncation study of YAP interface 3 peptide identified YAP(84-100) as a weak peptide inhibitor (IC50 = 37 μM), and an alanine scan revealed a beneficial mutation, D94A. Subsequent replacement of a native cation-π interaction with an optimized disulfide bridge for conformational constraint and synergistic effect between macrocyclization and modification at positions 91 and 93 greatly boosted inhibitory activity. Peptide 17 was identified with an IC50 of 25 nM, and the binding affinity (K d = 15 nM) of this 17mer peptide to TEAD1 proved to be stronger than YAP(50-171) (K d = 40 nM).

  2. Direct protein-protein interactions and substrate channeling between cellular retinoic acid binding proteins and CYP26B1.

    Science.gov (United States)

    Nelson, Cara H; Peng, Chi-Chi; Lutz, Justin D; Yeung, Catherine K; Zelter, Alex; Isoherranen, Nina

    2016-08-01

    Cellular retinoic acid binding proteins (CRABPs) bind all-trans-retinoic acid (atRA) tightly. This study aimed to determine whether atRA is channeled directly to cytochrome P450 (CYP) CYP26B1 by CRABPs, and whether CRABPs interact directly with CYP26B1. atRA bound to CRABPs (holo-CRABP) was efficiently metabolized by CYP26B1. Isotope dilution experiments showed that delivery of atRA to CYP26B1 in solution was similar with or without CRABP. Holo-CRABPs had higher affinity for CYP26B1 than free atRA, but both apo-CRABPs inhibited the formation of 4-OH-RA by CYP26B1. Similar protein-protein interactions between soluble binding proteins and CYPs may be important for other lipophilic CYP substrates.

  3. Analysis of hepatocellular carcinoma and metastatic hepatic carcinoma via functional modules in a protein-protein interaction network

    Directory of Open Access Journals (Sweden)

    Jun Pan

    2014-01-01

    Full Text Available Introduction: This study aims to identify protein clusters with potential functional relevance in the pathogenesis of hepatocellular carcinoma (HCC and metastatic hepatic carcinoma using network analysis. Materials and Methods: We used human protein interaction data to build a protein-protein interaction network with Cytoscape and then derived functional clusters using MCODE. Combining the gene expression profiles, we calculated the functional scores for the clusters and selected statistically significant clusters. Meanwhile, Gene Ontology was used to assess the functionality of these clusters. Finally, a support vector machine was trained on the gold standard data sets. Results: The differentially expressed genes of HCC were mainly involved in metabolic and signaling processes. We acquired 13 significant modules from the gene expression profiles. The area under the curve value based on the differentially expressed modules were 98.31%, which outweighed the classification with DEGs. Conclusions: Differentially expressed modules are valuable to screen biomarkers combined with functional modules.

  4. Effect of reference genome selection on the performance of computational methods for genome-wide protein-protein interaction prediction.

    Directory of Open Access Journals (Sweden)

    Vijaykumar Yogesh Muley

    Full Text Available BACKGROUND: Recent progress in computational methods for predicting physical and functional protein-protein interactions has provided new insights into the complexity of biological processes. Most of these methods assume that functionally interacting proteins are likely to have a shared evolutionary history. This history can be traced out for the protein pairs of a query genome by correlating different evolutionary aspects of their homologs in multiple genomes known as the reference genomes. These methods include phylogenetic profiling, gene neighborhood and co-occurrence of the orthologous protein coding genes in the same cluster or operon. These are collectively known as genomic context methods. On the other hand a method called mirrortree is based on the similarity of phylogenetic trees between two interacting proteins. Comprehensive performance analyses of these methods have been frequently reported in literature. However, very few studies provide insight into the effect of reference genome selection on detection of meaningful protein interactions. METHODS: We analyzed the performance of four methods and their variants to understand the effect of reference genome selection on prediction efficacy. We used six sets of reference genomes, sampled in accordance with phylogenetic diversity and relationship between organisms from 565 bacteria. We used Escherichia coli as a model organism and the gold standard datasets of interacting proteins reported in DIP, EcoCyc and KEGG databases to compare the performance of the prediction methods. CONCLUSIONS: Higher performance for predicting protein-protein interactions was achievable even with 100-150 bacterial genomes out of 565 genomes. Inclusion of archaeal genomes in the reference genome set improves performance. We find that in order to obtain a good performance, it is better to sample few genomes of related genera of prokaryotes from the large number of available genomes. Moreover, such a sampling

  5. Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson's disease.

    Directory of Open Access Journals (Sweden)

    Hindol Rakshit

    Full Text Available BACKGROUND: Parkinson's Disease (PD is one of the most prevailing neurodegenerative diseases. Improving diagnoses and treatments of this disease is essential, as currently there exists no cure for this disease. Microarray and proteomics data have revealed abnormal expression of several genes and proteins responsible for PD. Nevertheless, few studies have been reported involving PD-specific protein-protein interactions. RESULTS: Microarray based gene expression data and protein-protein interaction (PPI databases were combined to construct the PPI networks of differentially expressed (DE genes in post mortem brain tissue samples of patients with Parkinson's disease. Samples were collected from the substantia nigra and the frontal cerebral cortex. From the microarray data, two sets of DE genes were selected by 2-tailed t-tests and Significance Analysis of Microarrays (SAM, run separately to construct two Query-Query PPI (QQPPI networks. Several topological properties of these networks were studied. Nodes with High Connectivity (hubs and High Betweenness Low Connectivity (bottlenecks were identified to be the most significant nodes of the networks. Three and four-cliques were identified in the QQPPI networks. These cliques contain most of the topologically significant nodes of the networks which form core functional modules consisting of tightly knitted sub-networks. Hitherto unreported 37 PD disease markers were identified based on their topological significance in the networks. Of these 37 markers, eight were significantly involved in the core functional modules and showed significant change in co-expression levels. Four (ARRB2, STX1A, TFRC and MARCKS out of the 37 markers were found to be associated with several neurotransmitters including dopamine. CONCLUSION: This study represents a novel investigation of the PPI networks for PD, a complex disease. 37 proteins identified in our study can be considered as PD network biomarkers. These network

  6. An optimised small-molecule stabiliser of the 14-3-3-PMA2 protein-protein interaction.

    Science.gov (United States)

    Richter, Anja; Rose, Rolf; Hedberg, Christian; Waldmann, Herbert; Ottmann, Christian

    2012-05-21

    Modulation of protein-protein interactions (PPIs) is a highly demanding, but also a very promising approach in chemical biology and targeted drug discovery. In contrast to inhibiting PPIs with small, chemically tractable molecules, stabilisation of these interactions can only be achieved with complex natural products, like rapamycin, FK506, taxol, forskolin, brefeldin and fusicoccin. Fusicoccin stabilises the activatory complex of the plant H(+)-ATPase PMA2 and 14-3-3 proteins. Recently, we have shown that the stabilising effect of fusicoccin could be mimicked by a trisubstituted pyrrolinone (pyrrolidone1, 1). Here, we report the synthesis, functional activity and crystal structure of derivatives of 1 that stabilise the 14-3-3-PMA2 complex. With a limited compound collection three modifications that are important for activity enhancement could be determined: 1) conversion of the pyrrolinone scaffold into a pyrazole, 2) introduction of a tetrazole moiety to the phenyl ring that contacts PMA2, and 3) addition of a bromine to the phenyl ring that exclusively contacts the 14-3-3 protein. The crystal structure of a pyrazole derivative of 1 in complex with 14-3-3 and PMA2 revealed that the more rigid core of this molecule positions the stabiliser deeper into the rim of the interface, enlarging especially the contact surface to PMA2. Combination of the aforementioned features gave rise to a molecule (37) that displays a threefold increase in stabilising the 14-3-3-PMA2 complex over 1. Compound 37 and the other active derivatives show no effect on two other important 14-3-3 protein-protein interactions, that is, with CRaf and p53. This is the first study that describes the successful optimisation of a PPI stabiliser identified by screening. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. Dictionary and Gene Ontology Based Similarity for Named Entity Relationship Protein-protein Interaction Prediction from Biotext Corpus

    Directory of Open Access Journals (Sweden)

    Smt K. Prabavathy

    2014-12-01

    Full Text Available Protein-protein interactions functions as a significant key role in several biological systems. These involves in complex formation and many pathways which are used to perform biological processes. By accurate identification of the set of interacting proteins can get rid of new light on the functional role of various proteins in the complex surroundings of the cell. The ability to construct biologically consequential gene networks and identification of the exact relationship in the gene network is critical for present-day systems biology. In earlier research, the power of presented gene modules to shed light on the functioning of complex biological systems is studied. Most of modules in these networks have shown small link with meaningful biological function, because these methods doesn’t exactly calculate the semantic relationship between the entities. In order to overcome these problems and improve the PPI results in the biotext corpus a new method is proposed in this research. The proposed method which directly incorporates Gene Ontology (GO annotation in construction of gene modules and Dictionary-based text is proposed to extract biotext information. Dictionary-Based Text and Gene Ontology (DBTGO approach that integrates with various gene-gene pairwise similarity values, protein-protein interaction relationship obtained from gene expression, in order to gain better biotext information retrieval result. A result analysis has been carried out on Biotext Project at UC Berkley. Testing the DBTGO algorithm indicates that it is able to improve PPI relationship identification result with all previously suggested methods in terms of the precision, recall, F measure and Normalized Discounted Cumulative Gain (NDCG. The proposed DBTGO algorithm can facilitate comprehensive and in-depth analysis of high throughput experimental data at the gene network level.

  8. Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network.

    Science.gov (United States)

    Yang, Huiying; Ke, Yuehua; Wang, Jian; Tan, Yafang; Myeni, Sebenzile K; Li, Dong; Shi, Qinghai; Yan, Yanfeng; Chen, Hui; Guo, Zhaobiao; Yuan, Yanzhi; Yang, Xiaoming; Yang, Ruifu; Du, Zongmin

    2011-11-01

    A Yersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66 Y. pestis bait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted by Y. pestis were significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted by Y. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted by Y. pestis are highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance of Y. pestis to phagocytosis. Interference with TLR and MAPK signaling pathways by Y. pestis reflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting with Y. pestis (16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibit in vitro actin assembly mediated by VASP.

  9. GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction.

    Science.gov (United States)

    Browne, Fiona; Wang, Haiying; Zheng, Huiru; Azuaje, Francisco

    2009-01-26

    Information about protein interaction networks is fundamental to understanding protein function and cellular processes. Interaction patterns among proteins can suggest new drug targets and aid in the design of new therapeutic interventions. Efforts have been made to map interactions on a proteomic-wide scale using both experimental and computational techniques. Reference datasets that contain known interacting proteins (positive cases) and non-interacting proteins (negative cases) are essential to support computational prediction and validation of protein-protein interactions. Information on known interacting and non interacting proteins are usually stored within databases. Extraction of these data can be both complex and time consuming. Although, the automatic construction of reference datasets for classification is a useful resource for researchers no public resource currently exists to perform this task. GRIP (Gold Reference dataset constructor from Information on Protein complexes) is a web-based system that provides researchers with the functionality to create reference datasets for protein-protein interaction prediction in Saccharomyces cerevisiae. Both positive and negative cases for a reference dataset can be extracted, organised and downloaded by the user. GRIP also provides an upload facility whereby users can submit proteins to determine protein complex membership. A search facility is provided where a user can search for protein complex information in Saccharomyces cerevisiae. GRIP is developed to retrieve information on protein complex, cellular localisation, and physical and genetic interactions in Saccharomyces cerevisiae. Manual construction of reference datasets can be a time consuming process requiring programming knowledge. GRIP simplifies and speeds up this process by allowing users to automatically construct reference datasets. GRIP is free to access at http://rosalind.infj.ulst.ac.uk/GRIP/.

  10. GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction

    Directory of Open Access Journals (Sweden)

    Zheng Huiru

    2009-01-01

    Full Text Available Abstract Background Information about protein interaction networks is fundamental to understanding protein function and cellular processes. Interaction patterns among proteins can suggest new drug targets and aid in the design of new therapeutic interventions. Efforts have been made to map interactions on a proteomic-wide scale using both experimental and computational techniques. Reference datasets that contain known interacting proteins (positive cases and non-interacting proteins (negative cases are essential to support computational prediction and validation of protein-protein interactions. Information on known interacting and non interacting proteins are usually stored within databases. Extraction of these data can be both complex and time consuming. Although, the automatic construction of reference datasets for classification is a useful resource for researchers no public resource currently exists to perform this task. Results GRIP (Gold Reference dataset constructor from Information on Protein complexes is a web-based system that provides researchers with the functionality to create reference datasets for protein-protein interaction prediction in Saccharomyces cerevisiae. Both positive and negative cases for a reference dataset can be extracted, organised and downloaded by the user. GRIP also provides an upload facility whereby users can submit proteins to determine protein complex membership. A search facility is provided where a user can search for protein complex information in Saccharomyces cerevisiae. Conclusion GRIP is developed to retrieve information on protein complex, cellular localisation, and physical and genetic interactions in Saccharomyces cerevisiae. Manual construction of reference datasets can be a time consuming process requiring programming knowledge. GRIP simplifies and speeds up this process by allowing users to automatically construct reference datasets. GRIP is free to access at http://rosalind.infj.ulst.ac.uk/GRIP/.

  11. Biochemical structure of Calendula officinalis.

    Science.gov (United States)

    Korakhashvili, A; Kacharava, T; Kiknavelidze, N

    2007-01-01

    Calendula officinalis is a well known medicinal herb. It is common knowledge that its medicinal properties are conditioned on biologically active complex substances of Carotin (Provitamin A), Stearin, Triterpiniod, Plavonoid, Kumarin, macro and micro compound elements. Because of constant need in raw material of Calendula officinalis, features of its ontogenetic development agro-biological qualities in various eco regions of Georgia were investigated. The data of biologically active compounds, biochemical structure and the maintenance both in flowers and in others parts of plant is presented; the pharmacological activity and importance in medicine was reviewed.

  12. Enzyme and biochemical producing fungi

    DEFF Research Database (Denmark)

    Lübeck, Peter Stephensen; Lübeck, Mette; Nilsson, Lena

    2010-01-01

    We are developing a biorefinery concept for biological production of chemicals, drugs, feed and fuels using plant biomass as raw material in well-defined cell-factories. Among the important goals is the discovery of new biocatalysts for production of enzymes, biochemicals and fuels and already our...... screening of a large collection of fungal strains isolated from natural habitats have resulted in identification of strains with high production of hydrolytic enzymes and excretion of organic acids. Our research focuses on creating a fungal platform based on synthetic biology for developing new cell...

  13. Biochemical testing of thyroid function.

    Science.gov (United States)

    Klee, G G; Hay, I D

    1997-12-01

    Various published guidelines recommending serum thyrotropin (TSH)-first thyroid testing are outlined. The entities called "subclinical hypothyroidism" and "subclinical hyperthyroidism" are defined on the basis of abnormal TSH concentrations and normal values of other biochemical thyroid tests. The controversies about follow-up and treatment of these disorders are discussed. The laboratory experience of Mayo Clinic Rochester in using TSH-first thyroid testing and the subsequent implementation of a thyroid test ordering cascade are presented. Finally, recommendations are given for further optimizing laboratory testing for thyroid disorders.

  14. Vector Encoding in Biochemical Networks

    Science.gov (United States)

    Potter, Garrett; Sun, Bo

    Encoding of environmental cues via biochemical signaling pathways is of vital importance in the transmission of information for cells in a network. The current literature assumes a single cell state is used to encode information, however, recent research suggests the optimal strategy utilizes a vector of cell states sampled at various time points. To elucidate the optimal sampling strategy for vector encoding, we take an information theoretic approach and determine the mutual information of the calcium signaling dynamics obtained from fibroblast cells perturbed with different concentrations of ATP. Specifically, we analyze the sampling strategies under the cases of fixed and non-fixed vector dimension as well as the efficiency of these strategies. Our results show that sampling with greater frequency is optimal in the case of non-fixed vector dimension but that, in general, a lower sampling frequency is best from both a fixed vector dimension and efficiency standpoint. Further, we find the use of a simple modified Ornstein-Uhlenbeck process as a model qualitatively captures many of our experimental results suggesting that sampling in biochemical networks is based on a few basic components.

  15. Biochemical aspects of Huntington's chorea.

    Science.gov (United States)

    Caraceni, T; Calderini, G; Consolazione, A; Riva, E; Algeri, S; Girotti, F; Spreafico, R; Branciforti, A; Dall'olio, A; Morselli, P L

    1977-01-01

    Fifteen patients affected by Huntington's chorea were divided into two groups, 'slow' and 'fast', according to IQ scores on the Wechsler-Bellevue scale, and scores on some motor performance tests. A possible correlation was looked for between some biochemical data (cerebrospinal fluid (CSF), homovanillic acid (HVA), and 5-hydroxyindolacetic acid (5HIAA) levels, plasma dopamine-beta-hydroxylase (DBH), dopamine (DA) uptake by platelets), and clinical data (duration of illness, severity of symptoms, age of patients, IQ scores, 'slow' and 'fast' groups). The CSF, HVA, and 5HIAA levels were found to be significantly lowered in comparison with normal controls. DBH activity and DA uptake by platelets did not differ significantly from normal subjects. Treatment with haloperidol in all patients and with dipropylacetic acid in three patients did not appear to modify the CSF, HVA, and 5HIAA concentrations, the plasma DBH activity, or the DA uptake. There were no significant differences in the CSF, HVA, and 5HIAA contents between the two groups of patients, and there was no correlation between biochemical data and clinical features. PMID:143508

  16. Biochemical abnormalities in Pearson syndrome.

    Science.gov (United States)

    Crippa, Beatrice Letizia; Leon, Eyby; Calhoun, Amy; Lowichik, Amy; Pasquali, Marzia; Longo, Nicola

    2015-03-01

    Pearson marrow-pancreas syndrome is a multisystem mitochondrial disorder characterized by bone marrow failure and pancreatic insufficiency. Children who survive the severe bone marrow dysfunction in childhood develop Kearns-Sayre syndrome later in life. Here we report on four new cases with this condition and define their biochemical abnormalities. Three out of four patients presented with failure to thrive, with most of them having normal development and head size. All patients had evidence of bone marrow involvement that spontaneously improved in three out of four patients. Unique findings in our patients were acute pancreatitis (one out of four), renal Fanconi syndrome (present in all patients, but symptomatic only in one), and an unusual organic aciduria with 3-hydroxyisobutyric aciduria in one patient. Biochemical analysis indicated low levels of plasma citrulline and arginine, despite low-normal ammonia levels. Regression analysis indicated a significant correlation between each intermediate of the urea cycle and the next, except between ornithine and citrulline. This suggested that the reaction catalyzed by ornithine transcarbamylase (that converts ornithine to citrulline) might not be very efficient in patients with Pearson syndrome. In view of low-normal ammonia levels, we hypothesize that ammonia and carbamylphosphate could be diverted from the urea cycle to the synthesis of nucleotides in patients with Pearson syndrome and possibly other mitochondrial disorders.

  17. Biochemical Abnormalities in Batten's Syndrome

    DEFF Research Database (Denmark)

    Clausen, Jytte Lene; Nielsen, Gunnar Gissel; Jensen, Gunde Egeskov

    1978-01-01

    The present data indicate that a group of ten patients with Batten's syndrome showed reduced activity of erythrocyte glutathione (GSH) peroxidase (Px) (glutathione: H2O2 oxidoreductase, EC 1.1.1.9.) using H2O2 as peroxide donor. Assay of erythrocyte GSHPx using H2O2, cumene hydroperoxide and t......-butyl hydroperoxide as donors also makes it possible biochemically to divide Batten's syndrome into two types: (1) one type with decreased values when H2O2 and cumene hydroperoxide are used, and (2) one type with increased values when t-butyl hydroperoxide is used. Furthermore an increased content of palmitic, oleic...... and of eicosatrienoic acid but decreased linoleic acid content was found in serum from patients with Batten syndrome. An inverse relationship between erythrocyte GSHPx and serum eicosatrienoic acid was found in the patients. Finally normal selenium levels were found in erythrocytes, but decreased values were traced...

  18. Biochemical nature of Russell Bodies.

    Science.gov (United States)

    Mossuto, Maria Francesca; Ami, Diletta; Anelli, Tiziana; Fagioli, Claudio; Doglia, Silvia Maria; Sitia, Roberto

    2015-07-30

    Professional secretory cells produce and release abundant proteins. Particularly in case of mutations and/or insufficient chaperoning, these can aggregate and become toxic within or amongst cells. Immunoglobulins (Ig) are no exception. In the extracellular space, certain Ig-L chains form fibrils causing systemic amyloidosis. On the other hand, Ig variants lacking the first constant domain condense in dilated cisternae of the early secretory compartment, called Russell Bodies (RB), frequently observed in plasma cell dyscrasias, autoimmune diseases and chronic infections. RB biogenesis can be recapitulated in lymphoid and non-lymphoid cells by expressing mutant Ig-μ, providing powerful models to investigate the pathophysiology of endoplasmic reticulum storage disorders. Here we analyze the aggregation propensity and the biochemical features of the intra- and extra-cellular Ig deposits in human cells, revealing β-aggregated features for RB.

  19. Nonlinear Biochemical Signal Processing via Noise Propagation

    OpenAIRE

    Kim, Kyung Hyuk; Qian, Hong; Sauro, Herbert M.

    2013-01-01

    Single-cell studies often show significant phenotypic variability due to the stochastic nature of intra-cellular biochemical reactions. When the numbers of molecules, e.g., transcription factors and regulatory enzymes, are in low abundance, fluctuations in biochemical activities become significant and such "noise" can propagate through regulatory cascades in terms of biochemical reaction networks. Here we develop an intuitive, yet fully quantitative method for analyzing how noise affects cell...

  20. The Epc-N domain: a predicted protein-protein interaction domain found in select chromatin associated proteins

    Directory of Open Access Journals (Sweden)

    Perry Jason

    2006-01-01

    Full Text Available Abstract Background An underlying tenet of the epigenetic code hypothesis is the existence of protein domains that can recognize various chromatin structures. To date, two major candidates have emerged: (i the bromodomain, which can recognize certain acetylation marks and (ii the chromodomain, which can recognize certain methylation marks. Results The Epc-N (Enhancer of Polycomb-N-terminus domain is formally defined herein. This domain is conserved across eukaryotes and is predicted to form a right-handed orthogonal four-helix bundle with extended strands at both termini. The types of amino acid residues that define the Epc-N domain suggest a role in mediating protein-protein interactions, possibly specifically in the context of chromatin binding, and the types of proteins in which it is found (known components of histone acetyltransferase complexes strongly suggest a role in epigenetic structure formation and/or recognition. There appear to be two major Epc-N protein families that can be divided into four unique protein subfamilies. Two of these subfamilies (I and II may be related to one another in that subfamily I can be viewed as a plant-specific expansion of subfamily II. The other two subfamilies (III and IV appear to be related to one another by duplication events in a primordial fungal-metazoan-mycetozoan ancestor. Subfamilies III and IV are further defined by the presence of an evolutionarily conserved five-center-zinc-binding motif in the loop connecting the second and third helices of the four-helix bundle. This motif appears to consist of a PHD followed by a mononuclear Zn knuckle, followed by a PHD-like derivative, and will thus be referred to as the PZPM. All non-Epc-N proteins studied thus far that contain the PZPM have been implicated in histone methylation and/or gene silencing. In addition, an unusual phyletic distribution of Epc-N-containing proteins is observed. Conclusion The data suggest that the Epc-N domain is a protein-protein

  1. Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.

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    Nan Zhao

    2014-05-01

    Full Text Available Single nucleotide polymorphisms (SNPs are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs have been found near or inside the protein-protein interaction (PPI interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor. Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1 a 2-class problem (strengthening/weakening PPI mutations, (2 another 2-class problem (mutations that disrupt/preserve a PPI, and (3 a 3-class classification (detrimental/neutral/beneficial mutation effects. In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the

  2. Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network

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    Lv Jie

    2011-10-01

    Full Text Available Abstract Background As an important epigenetic modification, DNA methylation plays a crucial role in the development of mammals and in the occurrence of complex diseases. Genes that interact directly or indirectly may have the same or similar functions in the biological processes in which they are involved and together contribute to the related disease phenotypes. The complicated relations between genes can be clearly represented using network theory. A protein-protein interaction (PPI network offers a platform from which to systematically identify disease-related genes from the relations between genes with similar functions. Results We constructed a weighted human PPI network (WHPN using DNA methylation correlations based on human protein-protein interactions. WHPN represents the relationships of DNA methylation levels in gene pairs for four cancer types. A cancer-associated subnetwork (CASN was obtained from WHPN by selecting genes associated with seed genes which were known to be methylated in the four cancers. We found that CASN had a more densely connected network community than WHPN, indicating that the genes in CASN were much closer to seed genes. We prioritized 154 potential cancer-related genes with aberrant methylation in CASN by neighborhood-weighting decision rule. A function enrichment analysis for GO and KEGG indicated that the optimized genes were mainly involved in the biological processes of regulating cell apoptosis and programmed cell death. An analysis of expression profiling data revealed that many of the optimized genes were expressed differentially in the four cancers. By examining the PubMed co-citations, we found 43 optimized genes were related with cancers and aberrant methylation, and 10 genes were validated to be methylated aberrantly in cancers. Of 154 optimized genes, 27 were as diagnostic markers and 20 as prognostic markers previously identified in literature for cancers and other complex diseases by searching Pub

  3. Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

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    Pontil Massimiliano

    2009-10-01

    Full Text Available Abstract Background Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (ΔΔG measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots" at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition. Results We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which ΔΔG ≥ 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%. Conclusion We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been

  4. Predicting Human Protein Subcellular Locations by the Ensemble of Multiple Predictors via Protein-Protein Interaction Network with Edge Clustering Coefficients

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    Du, Pufeng; Wang, Lusheng

    2014-01-01

    One of the fundamental tasks in biology is to identify the functions of all proteins to reveal the primary machinery of a cell. Knowledge of the subcellular locations of proteins will provide key hints to reveal their functions and to understand the intricate pathways that regulate biological processes at the cellular level. Protein subcellular location prediction has been extensively studied in the past two decades. A lot of methods have been developed based on protein primary sequences as well as protein-protein interaction network. In this paper, we propose to use the protein-protein interaction network as an infrastructure to integrate existing sequence based predictors. When predicting the subcellular locations of a given protein, not only the protein itself, but also all its interacting partners were considered. Unlike existing methods, our method requires neither the comprehensive knowledge of the protein-protein interaction network nor the experimentally annotated subcellular locations of most proteins in the protein-protein interaction network. Besides, our method can be used as a framework to integrate multiple predictors. Our method achieved 56% on human proteome in absolute-true rate, which is higher than the state-of-the-art methods. PMID:24466278

  5. The PPI3D web server for searching, analyzing and modeling protein-protein interactions in the context of 3D structures.

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    Dapkūnas, Justas; Timinskas, Albertas; Olechnovič, Kliment; Margelevičius, Mindaugas; Dičiūnas, Rytis; Venclovas, Česlovas

    2016-12-22

    The PPI3D web server is focused on searching and analyzing the structural data on protein-protein interactions. Reducing the data redundancy by clustering and analyzing the properties of interaction interfaces using Voronoi tessellation makes this software a highly effective tool for addressing different questions related to protein interactions.

  6. No simple dependence between protein evolution rate and the number of protein-protein interactions: only the most prolific interactors tend to evolve slowly

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    Koonin Eugene V

    2003-01-01

    Full Text Available Abstract Background It has been suggested that rates of protein evolution are influenced, to a great extent, by the proportion of amino acid residues that are directly involved in protein function. In agreement with this hypothesis, recent work has shown a negative correlation between evolutionary rates and the number of protein-protein interactions. However, the extent to which the number of protein-protein interactions influences evolutionary rates remains unclear. Here, we address this question at several different levels of evolutionary relatedness. Results Manually curated data on the number of protein-protein interactions among Saccharomyces cerevisiae proteins was examined for possible correlation with evolutionary rates between S. cerevisiae and Schizosaccharomyces pombe orthologs. Only a very weak negative correlation between the number of interactions and evolutionary rate of a protein was observed. Furthermore, no relationship was found between a more general measure of the evolutionary conservation of S. cerevisiae proteins, based on the taxonomic distribution of their homologs, and the number of protein-protein interactions. However, when the proteins from yeast were assorted into discrete bins according to the number of interactions, it turned out that 6.5% of the proteins with the greatest number of interactions evolved, on average, significantly slower than the rest of the proteins. Comparisons were also performed using protein-protein interaction data obtained with high-throughput analysis of Helicobacter pylori proteins. No convincing relationship between the number of protein-protein interactions and evolutionary rates was detected, either for comparisons of orthologs from two completely sequenced H. pylori strains or for comparisons of H. pylori and Campylobacter jejuni orthologs, even when the proteins were classified into bins by the number of interactions. Conclusion The currently available comparative-genomic data do not

  7. InterEvDock: a docking server to predict the structure of protein-protein interactions using evolutionary information.

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    Yu, Jinchao; Vavrusa, Marek; Andreani, Jessica; Rey, Julien; Tufféry, Pierre; Guerois, Raphaël

    2016-07-01

    The structural modeling of protein-protein interactions is key in understanding how cell machineries cross-talk with each other. Molecular docking simulations provide efficient means to explore how two unbound protein structures interact. InterEvDock is a server for protein docking based on a free rigid-body docking strategy. A systematic rigid-body docking search is performed using the FRODOCK program and the resulting models are re-scored with InterEvScore and SOAP-PP statistical potentials. The InterEvScore potential was specifically designed to integrate co-evolutionary information in the docking process. InterEvDock server is thus particularly well suited in case homologous sequences are available for both binding partners. The server returns 10 structures of the most likely consensus models together with 10 predicted residues most likely involved in the interface. In 91% of all complexes tested in the benchmark, at least one residue out of the 10 predicted is involved in the interface, providing useful guidelines for mutagenesis. InterEvDock is able to identify a correct model among the top10 models for 49% of the rigid-body cases with evolutionary information, making it a unique and efficient tool to explore structural interactomes under an evolutionary perspective. The InterEvDock web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock/.

  8. Molecular interactions between multihaem cytochromes: probing the protein-protein interactions between pentahaem cytochromes of a nitrite reductase complex.

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    Lockwood, Colin; Butt, Julea N; Clarke, Thomas A; Richardson, David J

    2011-01-01

    The cytochrome c nitrite reductase NrfA is a 53 kDa pentahaem enzyme that crystallizes as a decahaem homodimer. NrfA catalyses the reduction of NO2- to NH4+ through a six electron reduction pathway that is of major physiological significance to the anaerobic metabolism of enteric and sulfate reducing bacteria. NrfA receives electrons from the 21 kDa pentahaem NrfB donor protein. This requires that redox complexes form between the NrfA and NrfB pentahaem cytochromes. The formation of these complexes can be monitored using a range of methodologies for studying protein-protein interactions, including dynamic light scattering, gel filtration, analytical ultracentrifugation and visible spectroscopy. These methods have been used to show that oxidized NrfA exists in dynamic monomer-dimer equilibrium with a Kd (dissociation constant) of 4 μM. Significantly, the monomeric and dimeric forms of NrfA are equally active for either the six electron reduction of NO2- or HSO3-. When mixed together, NrfA and NrfB exist in equilibrium with NrfAB, which is described by a Kd of 50 nM. Thus, since NrfA and NrfB are present in micromolar concentrations in the periplasmic compartment, it is likely that NrfB remains tightly associated with its NrfA redox partner under physiological conditions.

  9. Struct2Net: a web service to predict protein-protein interactions using a structure-based approach.

    Science.gov (United States)

    Singh, Rohit; Park, Daniel; Xu, Jinbo; Hosur, Raghavendra; Berger, Bonnie

    2010-07-01

    Struct2Net is a web server for predicting interactions between arbitrary protein pairs using a structure-based approach. Prediction of protein-protein interactions (PPIs) is a central area of interest and successful prediction would provide leads for experiments and drug design; however, the experimental coverage of the PPI interactome remains inadequate. We believe that Struct2Net is the first community-wide resource to provide structure-based PPI predictions that go beyond homology modeling. Also, most web-resources for predicting PPIs currently rely on functional genomic data (e.g. GO annotation, gene expression, cellular localization, etc.). Our structure-based approach is independent of such methods and only requires the sequence information of the proteins being queried. The web service allows multiple querying options, aimed at maximizing flexibility. For the most commonly studied organisms (fly, human and yeast), predictions have been pre-computed and can be retrieved almost instantaneously. For proteins from other species, users have the option of getting a quick-but-approximate result (using orthology over pre-computed results) or having a full-blown computation performed. The web service is freely available at http://struct2net.csail.mit.edu.

  10. Detecting Protein-Protein Interactions with a Novel Matrix-Based Protein Sequence Representation and Support Vector Machines

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    Zhu-Hong You

    2015-01-01

    Full Text Available Proteins and their interactions lie at the heart of most underlying biological processes. Consequently, correct detection of protein-protein interactions (PPIs is of fundamental importance to understand the molecular mechanisms in biological systems. Although the convenience brought by high-throughput experiment in technological advances makes it possible to detect a large amount of PPIs, the data generated through these methods is unreliable and may not be completely inclusive of all possible PPIs. Targeting at this problem, this study develops a novel computational approach to effectively detect the protein interactions. This approach is proposed based on a novel matrix-based representation of protein sequence combined with the algorithm of support vector machine (SVM, which fully considers the sequence order and dipeptide information of the protein primary sequence. When performed on yeast PPIs datasets, the proposed method can reach 90.06% prediction accuracy with 94.37% specificity at the sensitivity of 85.74%, indicating that this predictor is a useful tool to predict PPIs. Achieved results also demonstrate that our approach can be a helpful supplement for the interactions that have been detected experimentally.

  11. Identification of genes involved in radioresistance of nasopharyngeal carcinoma by integrating gene ontology and protein-protein interaction networks.

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    Guo, Ya; Zhu, Xiao-Dong; Qu, Song; Li, Ling; Su, Fang; Li, Ye; Huang, Shi-Ting; Li, Dan-Rong

    2012-01-01

    Radioresistance remains one of the important factors in relapse and metastasis of nasopharyngeal carcinoma. Thus, it is imperative to identify genes involved in radioresistance and explore the underlying biological processes in the development of radioresistance. In this study, we used cDNA microarrays to select differential genes between radioresistant CNE-2R and parental CNE-2 cell lines. One hundred and eighty-three significantly differentially expressed genes (pgenes were upregulated and 45 genes were downregulated in CNE-2R. We further employed publicly available bioinformatics related software, such as GOEAST and STRING to examine the relationship among differentially expressed genes. The results show that these genes were involved in type I interferon-mediated signaling pathway biological processes; the nodes tended to have high connectivity with the EGFR pathway, IFN-related pathways, NF-κB. The node STAT1 has high connectivity with other nodes in the protein-protein interaction (PPI) networks. Finally, the reliability of microarray data was validated for selected genes by semi-quantitative RT-PCR and Western blotting. The results were consistent with the microarray data. Our study suggests that microarrays combined with gene ontology and protein interaction networks have great value in the identification of genes of radioresistance in nasopharyngeal carcinoma; genes involved in several biological processes and protein interaction networks may be relevant to NPC radioresistance; in particular, the verified genes CCL5, STAT1-α, STAT2 and GSTP1 may become potential biomarkers for predicting NPC response to radiotherapy.

  12. Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry.

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    Kumar, Ravindra; Samal, Sabindra K; Routray, Samapika; Dash, Rupesh; Dixit, Anshuman

    2017-05-30

    In the recent years, bioinformatics methods have been reported with a high degree of success for candidate gene identification. In this milieu, we have used an integrated bioinformatics approach assimilating information from gene ontologies (GO), protein-protein interaction (PPI) and network analysis to predict candidate genes related to oral squamous cell carcinoma (OSCC). A total of 40973 PPIs were considered for 4704 cancer-related genes to construct human cancer gene network (HCGN). The importance of each node was measured in HCGN by ten different centrality measures. We have shown that the top ranking genes are related to a significantly higher number of diseases as compared to other genes in HCGN. A total of 39 candidate oral cancer target genes were predicted by combining top ranked genes and the genes corresponding to significantly enriched oral cancer related GO terms. Initial verification using literature and available experimental data indicated that 29 genes were related with OSCC. A detailed pathway analysis led us to propose a role for the selected candidate genes in the invasion and metastasis in OSCC. We further validated our predictions using immunohistochemistry (IHC) and found that the gene FLNA was upregulated while the genes ARRB1 and HTT were downregulated in the OSCC tissue samples.

  13. A brief history of the search for the protein(s) involved in the acute regulation of steroidogenesis.

    Science.gov (United States)

    Stocco, Douglas M; Zhao, Amy H; Tu, Lan N; Morohaku, Kanako; Selvaraj, Vimal

    2017-02-05

    The synthesis of steroid hormones occurs in specific cells and tissues in the body in response to trophic hormones and other signals. In order to synthesize steroids de novo, cholesterol, the precursor of all steroid hormones, must be mobilized from cellular stores to the inner mitochondrial membrane (IMM) to be converted into the first steroid formed, pregnenolone. This delivery of cholesterol to the IMM is the rate-limiting step in this process, and has long been known to require the rapid synthesis of a new protein(s) in response to stimulation. Although several possibilities for this protein have arisen over the past few decades, most of the recent attention to fill this role has centered on the candidacies of the proteins the Translocator Protein (TSPO) and the Steroidogenic Acute Regulatory Protein (StAR). In this review, the process of regulating steroidogenesis is briefly described, the characteristics of the candidate proteins and the data supporting their candidacies summarized, and some recent findings that propose a serious challenge for the role of TSPO in this process are discussed. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  14. A semisynthetic fusicoccane stabilizes a protein-protein interaction and enhances the expression of K+ channels at the cell surface.

    Science.gov (United States)

    Anders, Carolin; Higuchi, Yusuke; Koschinsky, Kristin; Bartel, Maria; Schumacher, Benjamin; Thiel, Philipp; Nitta, Hajime; Preisig-Müller, Regina; Schlichthörl, Günter; Renigunta, Vijay; Ohkanda, Junko; Daut, Jürgen; Kato, Nobuo; Ottmann, Christian

    2013-04-18

    Small-molecule stabilization of protein-protein interactions is an emerging field in chemical biology. We show how fusicoccanes, originally identified as fungal toxins acting on plants, promote the interaction of 14-3-3 proteins with the human potassium channel TASK-3 and present a semisynthetic fusicoccane derivative (FC-THF) that targets the 14-3-3 recognition motif (mode 3) in TASK-3. In the presence of FC-THF, the binding of 14-3-3 proteins to TASK-3 was increased 19-fold and protein crystallography provided the atomic details of the effects of FC-THF on this interaction. We also tested the functional effects of FC-THF on TASK channels heterologously expressed in Xenopus oocytes. Incubation with 10 μM FC-THF was found to promote the transport of TASK channels to the cell membrane, leading to a significantly higher density of channels at the surface membrane and increased potassium current. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. In Vivo Analysis of Protein-Protein Interactions with Bioluminescence Resonance Energy Transfer (BRET): Progress and Prospects.

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    Sun, Sihuai; Yang, Xiaobing; Wang, Yao; Shen, Xihui

    2016-10-11

    Proteins are the elementary machinery of life, and their functions are carried out mostly by molecular interactions. Among those interactions, protein-protein interactions (PPIs) are the most important as they participate in or mediate all essential biological processes. However, many common methods for PPI investigations are slightly unreliable and suffer from various limitations, especially in the studies of dynamic PPIs. To solve this problem, a method called Bioluminescence Resonance Energy Transfer (BRET) was developed about seventeen years ago. Since then, BRET has evolved into a whole class of methods that can be used to survey virtually any kinds of PPIs. Compared to many traditional methods, BRET is highly sensitive, reliable, easy to perform, and relatively inexpensive. However, most importantly, it can be done in vivo and allows the real-time monitoring of dynamic PPIs with the easily detectable light signal, which is extremely valuable for the PPI functional research. This review will take a comprehensive look at this powerful technique, including its principles, comparisons with other methods, experimental approaches, classifications, applications, early developments, recent progress, and prospects.

  16. Protein-protein interaction network construction for cancer using a new L1/2-penalized Net-SVM model.

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    Chai, H; Huang, H H; Jiang, H K; Liang, Y; Xia, L Y

    2016-07-25

    Identifying biomarker genes and characterizing interaction pathways with high-dimensional and low-sample size microarray data is a major challenge in computational biology. In this field, the construction of protein-protein interaction (PPI) networks using disease-related selected genes has garnered much attention. Support vector machines (SVMs) are commonly used to classify patients, and a number of useful tools such as lasso, elastic net, SCAD, or other regularization methods can be combined with SVM models to select genes that are related to a disease. In the current study, we propose a new Net-SVM model that is different from other SVM models as it is combined with L1/2-norm regularization, which has good performance with high-dimensional and low-sample size microarray data for cancer classification, gene selection, and PPI network construction. Both simulation studies and real data experiments demonstrated that our proposed method outperformed other regularization methods such as lasso, SCAD, and elastic net. In conclusion, our model may help to select fewer but more relevant genes, and can be used to construct simple and informative PPI networks that are highly relevant to cancer.

  17. Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning.

    Science.gov (United States)

    Du, Tianchuan; Liao, Li; Wu, Cathy H; Sun, Bilin

    2016-11-01

    Protein-protein interactions play essential roles in many biological processes. Acquiring knowledge of the residue-residue contact information of two interacting proteins is not only helpful in annotating functions for proteins, but also critical for structure-based drug design. The prediction of the protein residue-residue contact matrix of the interfacial regions is challenging. In this work, we introduced deep learning techniques (specifically, stacked autoencoders) to build deep neural network models to tackled the residue-residue contact prediction problem. In tandem with interaction profile Hidden Markov Models, which was used first to extract Fisher score features from protein sequences, stacked autoencoders were deployed to extract and learn hidden abstract features. The deep learning model showed significant improvement over the traditional machine learning model, Support Vector Machines (SVM), with the overall accuracy increased by 15% from 65.40% to 80.82%. We showed that the stacked autoencoders could extract novel features, which can be utilized by deep neural networks and other classifiers to enhance learning, out of the Fisher score features. It is further shown that deep neural networks have significant advantages over SVM in making use of the newly extracted features.

  18. The essential and downstream common proteins of amyotrophic lateral sclerosis: A protein-protein interaction network analysis.

    Science.gov (United States)

    Mao, Yimin; Kuo, Su-Wei; Chen, Le; Heckman, C J; Jiang, M C

    2017-01-01

    Amyotrophic Lateral Sclerosis (ALS) is a devastative neurodegenerative disease characterized by selective loss of motoneurons. While several breakthroughs have been made in identifying ALS genetic defects, the detailed molecular mechanisms are still unclear. These genetic defects involve in numerous biological processes, which converge to a common destiny: motoneuron degeneration. In addition, the common comorbid Frontotemporal Dementia (FTD) further complicates the investigation of ALS etiology. In this study, we aimed to explore the protein-protein interaction network built on known ALS-causative genes to identify essential proteins and common downstream proteins between classical ALS and ALS+FTD (classical ALS + ALS/FTD) groups. The results suggest that classical ALS and ALS+FTD share similar essential protein set (VCP, FUS, TDP-43 and hnRNPA1) but have distinctive functional enrichment profiles. Thus, disruptions to these essential proteins might cause motoneuron susceptible to cellular stresses and eventually vulnerable to proteinopathies. Moreover, we identified a common downstream protein, ubiquitin-C, extensively interconnected with ALS-causative proteins (22 out of 24) which was not linked to ALS previously. Our in silico approach provides the computational background for identifying ALS therapeutic targets, and points out the potential downstream common ground of ALS-causative mutations.

  19. Characterizing genes with distinct methylation patterns in the context of protein-protein interaction network: application to human brain tissues.

    Science.gov (United States)

    Li, Yongsheng; Xu, Juan; Chen, Hong; Zhao, Zheng; Li, Shengli; Bai, Jing; Wu, Aiwei; Jiang, Chunjie; Wang, Yuan; Su, Bin; Li, Xia

    2013-01-01

    DNA methylation is an essential epigenetic mechanism involved in transcriptional control. However, how genes with different methylation patterns are assembled in the protein-protein interaction network (PPIN) remains a mystery. In the present study, we systematically dissected the characterization of genes with different methylation patterns in the PPIN. A negative association was detected between the methylation levels in the brain tissues and topological centralities. By focusing on two classes of genes with considerably different methylation levels in the brain tissues, namely the low methylated genes (LMGs) and high methylated genes (HMGs), we found that their organizing principles in the PPIN are distinct. The LMGs tend to be the center of the PPIN, and attacking them causes a more deleterious effect on the network integrity. Furthermore, the LMGs express their functions in a modular pattern and substantial differences in functions are observed between the two types of genes. The LMGs are enriched in the basic biological functions, such as binding activity and regulation of transcription. More importantly, cancer genes, especially recessive cancer genes, essential genes, and aging-related genes were all found more often in the LMGs. Additionally, our analysis presented that the intra-classes communications are enhanced, but inter-classes communications are repressed. Finally, a functional complementation was revealed between methylation and miRNA regulation in the human genome. We have elucidated the assembling principles of genes with different methylation levels in the context of the PPIN, providing key insights into the complex epigenetic regulation mechanisms.

  20. DynaFace: Discrimination between Obligatory and Non-obligatory Protein-Protein Interactions Based on the Complex's Dynamics.

    Directory of Open Access Journals (Sweden)

    Seren Soner

    2015-10-01

    Full Text Available Protein-protein interfaces have been evolutionarily-designed to enable transduction between the interacting proteins. Thus, we hypothesize that analysis of the dynamics of the complex can reveal details about the nature of the interaction, and in particular whether it is obligatory, i.e., persists throughout the entire lifetime of the proteins, or not. Indeed, normal mode analysis, using the Gaussian network model, shows that for the most part obligatory and non-obligatory complexes differ in their decomposition into dynamic domains, i.e., the mobile elements of the protein complex. The dynamic domains of obligatory complexes often mix segments from the interacting chains, and the hinges between them do not overlap with the interface between the chains. In contrast, in non-obligatory complexes the interface often hinges between dynamic domains, held together through few anchor residues on one side of the interface that interact with their counterpart grooves in the other end. In automatic analysis, 117 of 139 obligatory (84.2% and 203 of 246 non-obligatory (82.5% complexes are correctly classified by our method: DynaFace. We further use DynaFace to predict obligatory and non-obligatory interactions among a set of 300 putative protein complexes. DynaFace is available at: http://safir.prc.boun.edu.tr/dynaface.

  1. QCM Biosensor Based on Polydopamine Surface for Real-Time Analysis of the Binding Kinetics of Protein-Protein Interactions

    Directory of Open Access Journals (Sweden)

    Chunli Wu

    2017-10-01

    Full Text Available A quartz crystal microbalance (QCM biosensor based on polydopamine (PDA surface was developed for real-time analysis of the binding kinetics of protein-protein interactions. The biosensor was fabricated by simply immersing the gold sensor chip into an aqueous dopamine solution at pH 8.5 leading to a spontaneous deposition of PDA film onto the sensor chip surface, which was followed by incubation with the protein to immobilize it onto the PDA-coated sensor chip surface via Michael addition and/or Schiff base reactions. In this paper, the interaction between monoclonal anti-myoglobin 7005 antibody (IgG1 and its antigen human cardiac myoglobin was used as a model system for real-time analysis of biomolecule interactions on the biosensor surface. The kinetic parameters of the interaction between anti-myoglobin 7005 and myoglobin were studied on the biosensor surface, which were consistent with the results obtained via amine coupling. The biosensor based on PDA surface has excellent regenerability, reproducibility, and specificity. Compared with the most frequently/typically used amine coupling method for immobilization of proteins on carboxylated substrates, the modification methodology presented in this paper is simple, mild and is not subjected to the limitations of the isoelectric point (pI of the protein. In addition, the PDA biosensor chip can be easily reused, which makes QCM biosensor analysis more efficient and cost effective.

  2. Multi-agent-based bio-network for systems biology: protein-protein interaction network as an example.

    Science.gov (United States)

    Ren, Li-Hong; Ding, Yong-Sheng; Shen, Yi-Zhen; Zhang, Xiang-Feng

    2008-10-01

    Recently, a collective effort from multiple research areas has been made to understand biological systems at the system level. This research requires the ability to simulate particular biological systems as cells, organs, organisms, and communities. In this paper, a novel bio-network simulation platform is proposed for system biology studies by combining agent approaches. We consider a biological system as a set of active computational components interacting with each other and with an external environment. Then, we propose a bio-network platform for simulating the behaviors of biological systems and modelling them in terms of bio-entities and society-entities. As a demonstration, we discuss how a protein-protein interaction (PPI) network can be seen as a society of autonomous interactive components. From interactions among small PPI networks, a large PPI network can emerge that has a remarkable ability to accomplish a complex function or task. We also simulate the evolution of the PPI networks by using the bio-operators of the bio-entities. Based on the proposed approach, various simulators with different functions can be embedded in the simulation platform, and further research can be done from design to development, including complexity validation of the biological system.

  3. Multi-Component Protein - Protein Docking Based Protocol with External Scoring for Modeling Dimers of G Protein-Coupled Receptors.

    Science.gov (United States)

    Kaczor, Agnieszka A; Guixà-González, Ramon; Carrió, Pau; Poso, Antti; Dove, Stefan; Pastor, Manuel; Selent, Jana

    2015-04-01

    In order to apply structure-based drug design techniques to GPCR complexes, it is essential to model their 3D structure. For this purpose, a multi-component protocol was derived based on protein-protein docking which generates populations of dimers compatible with membrane integration, considering all reasonable interfaces. At the next stage, we applied a scoring procedure based on up to eleven different parameters including shape or electrostatics complementarity. Two methods of consensus scoring were performed: (i) average scores of 100 best scored dimers with respect to each interface, and (ii) frequencies of interfaces among 100 best scored dimers. In general, our multi-component protocol gives correct indications for dimer interfaces that have been observed in X-ray crystal structures of GPCR dimers (opsin dimer, chemokine CXCR4 and CCR5 dimers, κ opioid receptor dimer, β1 adrenergic receptor dimer and smoothened receptor dimer) but also suggests alternative dimerization interfaces. Interestingly, at times these alternative interfaces are scored higher than the experimentally observed ones suggesting them to be also relevant in the life cycle of studied GPCR dimers. Further results indicate that GPCR dimer and higher-order oligomer formation may involve transmembrane helices (TMs) TM1-TM2-TM7, TM3-TM4-TM5 or TM4-TM5-TM6 but not TM1-TM2-TM3 or TM2-TM3-TM4 which is in general agreement with available experimental and computational data.

  4. PPI-IRO: A two-stage method for protein-protein interaction extraction based on interaction relation ontology

    KAUST Repository

    Li, Chuanxi

    2014-01-01

    Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identifi cation of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifi es and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At fi rst, IRO is applied in a binary classifi er to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the signifi cant performance of IRO on relation sentences classifi cation and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and Bioinfer, respectively, which are superior to most existing extraction methods. Copyright © 2014 Inderscience Enterprises Ltd.

  5. Large-scale protein-protein interaction analysis in Arabidopsis mesophyll protoplasts by split firefly luciferase complementation.

    Directory of Open Access Journals (Sweden)

    Jian-Feng Li

    Full Text Available Protein-protein interactions (PPIs constitute the regulatory network that coordinates diverse cellular functions. There are growing needs in plant research for creating protein interaction maps behind complex cellular processes and at a systems biology level. However, only a few approaches have been successfully used for large-scale surveys of PPIs in plants, each having advantages and disadvantages. Here we present split firefly luciferase complementation (SFLC as a highly sensitive and noninvasive technique for in planta PPI investigation. In this assay, the separate halves of a firefly luciferase can come into close proximity and transiently restore its catalytic activity only when their fusion partners, namely the two proteins of interest, interact with each other. This assay was conferred with quantitativeness and high throughput potential when the Arabidopsis mesophyll protoplast system and a microplate luminometer were employed for protein expression and luciferase measurement, respectively. Using the SFLC assay, we could monitor the dynamics of rapamycin-induced and ascomycin-disrupted interaction between Arabidopsis FRB and human FKBP proteins in a near real-time manner. As a proof of concept for large-scale PPI survey, we further applied the SFLC assay to testing 132 binary PPIs among 8 auxin response factors (ARFs and 12 Aux/IAA proteins from Arabidopsis. Our results demonstrated that the SFLC assay is ideal for in vivo quantitative PPI analysis in plant cells and is particularly powerful for large-scale binary PPI screens.

  6. Prioritization of potential candidate disease genes by topological similarity of protein-protein interaction network and phenotype data.

    Science.gov (United States)

    Luo, Jiawei; Liang, Shiyu

    2015-02-01

    Identifying candidate disease genes is important to improve medical care. However, this task is challenging in the post-genomic era. Several computational approaches have been proposed to prioritize potential candidate genes relying on protein-protein interaction (PPI) networks. However, the experimental PPI network is usually liable to contain a number of spurious interactions. In this paper, we construct a reliable heterogeneous network by fusing multiple networks, a PPI network reconstructed by topological similarity, a phenotype similarity network and known associations between diseases and genes. We then devise a random walk-based algorithm on the reliable heterogeneous network called RWRHN to prioritize potential candidate genes for inherited diseases. The results of leave-one-out cross-validation experiments show that the RWRHN algorithm has better performance than the RWRH and CIPHER methods in inferring disease genes. Furthermore, RWRHN is used to predict novel causal genes for 16 diseases, including breast cancer, diabetes mellitus type 2, and prostate cancer, as well as to detect disease-related protein complexes. The top predictions are supported by literature evidence.

  7. Structural basis for the potent inhibition of the HIV integrase-LEDGF/p75 protein-protein interaction.

    Science.gov (United States)

    Ribone, Sergio R; Quevedo, Mario A

    2017-08-01

    Integrase (IN) constitutes one of the key enzymes involved in the lifecycle of the Human Immunodeficiency Virus (HIV), the etiological agent of AIDS. The biological role of IN strongly depends on the recognition and binding of cellular cofactors belonging to the infected host cell. Thus, the inhibition of the protein-protein interaction (PPI) between IN and cellular cofactors has been envisioned as a promising therapeutic target. In the present work we explore a structure-activity relationship for a set of 14 compounds reported as inhibitors of the PPI between IN and the lens epithelium-derived growth factor (LEDGF/p75). Our results demonstrate that the possibility to adopt the bioactive conformation capable of interacting with the hotspots IN-LEDGF/p75 hotspots residues constitutes a critical feature to obtain a potent inhibition. A ligand efficiency (|Lig-Eff|) quantitative descriptor combining both interaction energetics and conformational requirements was developed and correlated with the reported biological activity. Our results contribute to the rational development of IN-LEDGF/p75 interaction inhibitors providing a solid quantitative structure-activity relationship aimed for the screening of new IN-LEDGF/p75 interaction inhibitors. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

    Science.gov (United States)

    Li, Jin; Wang, Limei; Guo, Maozu; Zhang, Ruijie; Dai, Qiguo; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Xuan, Ping; Zhang, Mingming

    2015-01-01

    In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

  9. BRET: NanoLuc-Based Bioluminescence Resonance Energy Transfer Platform to Monitor Protein-Protein Interactions in Live Cells.

    Science.gov (United States)

    Mo, Xiu-Lei; Fu, Haian

    2016-01-01

    Bioluminescence resonance energy transfer (BRET) is a prominent biophysical technology for monitoring molecular interactions, and has been widely used to study protein-protein interactions (PPI) in live cells. This technology requires proteins of interest to be associated with an energy donor (i.e., luciferase) and an acceptor (e.g., fluorescent protein) molecule. Upon interaction of the proteins of interest, the donor and acceptor will be brought into close proximity and energy transfer of chemical reaction-induced luminescence to its corresponding acceptor will result in an increased emission at an acceptor-defined wavelength, generating the BRET signal. We leverage the advantages of the superior optical properties of the NanoLuc(®) luciferase (NLuc) as a BRET donor coupled with Venus, a yellow fluorescent protein, as acceptor. We term this NLuc-based BRET platform "BRET(n)". BRET(n) has been demonstrated to have significantly improved assay performance, compared to previous BRET technologies, in terms of sensitivity and scalability. This chapter describes a step-by-step practical protocol for developing a BRET(n) assay in a multi-well plate format to detect PPIs in live mammalian cells.

  10. Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Bi-Qing Li

    2013-01-01

    Full Text Available Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC and nonsmall cell lung cancer (NSCLC. In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutation P value less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

  11. Optimizing Protein-Protein van der Waals Interactions for the AMBER ff9x/ff12 Force Field.

    Science.gov (United States)

    Chapman, Dail E; Steck, Jonathan K; Nerenberg, Paul S

    2014-01-14

    The quality of molecular dynamics (MD) simulations relies heavily on the accuracy of the underlying force field. In recent years, considerable effort has been put into developing more accurate dihedral angle potentials for MD force fields, but relatively little work has focused on the nonbonded parameters, many of which are two decades old. In this work, we assess the accuracy of protein-protein van der Waals interactions in the AMBER ff9x/ff12 force field. Across a test set of 44 neat organic liquids containing the moieties present in proteins, we find root-mean-square (RMS) errors of 1.26 kcal/mol in enthalpy of vaporization and 0.36 g/cm(3) in liquid densities. We then optimize the van der Waals radii and well depths for all of the relevant atom types using these observables, which lowers the RMS errors in enthalpy of vaporization and liquid density of our validation set to 0.59 kcal/mol (53% reduction) and 0.019 g/cm(3) (46% reduction), respectively. Limitations in our parameter optimization were evident for certain atom types, however, and we discuss the implications of these observations for future force field development.

  12. Development of small molecules to target the IgE:FcεRI protein-protein interaction in allergies.

    Science.gov (United States)

    Smith, Lucy D; Leatherbarrow, Robin J; Spivey, Alan C

    2013-08-01

    The protein-protein interaction (PPI) between IgE and its high-affinity receptor (FcεRI) is a key component of the allergic response. Inhibiting the IgE:FcεRI PPI is an attractive strategy for therapeutic intervention and the development of allergy treatments. This PPI has been validated as a viable target by the monoclonal anti-IgE antibody omalizumab (Xolair(®)), which has demonstrated clinical efficacy when prescribed to treat moderate-to-severe asthma and hay fever, but small molecules would be a more convenient form of treatment. Cyclic peptides, small proteins and a natural product have all been developed to target the IgE:FcεRI PPI, and these will be discussed in this review. Targeting the IgE:FcεRI complex with small molecules presents various challenges, some of which are inherent in all PPI targets but some of which are unique to this system, which presents great opportunities for the development of new therapeutics for the treatment of allergies.

  13. Lateral Protein-Protein Interactions at Hydrophobic and Charged Surfaces as a Function of pH and Salt Concentration.

    Science.gov (United States)

    Hladílková, Jana; Callisen, Thomas H; Lund, Mikael

    2016-04-07

    Surface adsorption of Thermomyces lanuginosus lipase (TLL)-a widely used industrial biocatalyst-is studied experimentally and theoretically at different pH and salt concentrations. The maximum achievable surface coverage on a hydrophobic surface occurs around the protein isoelectric point and adsorption is reduced when either increasing or decreasing pH, indicating that electrostatic protein-protein interactions in the adsorbed layer play an important role. Using Metropolis Monte Carlo (MC) simulations, where proteins are coarse grained to the amino acid level, we estimate the protein isoelectric point in the vicinity of charged surfaces as well as the lateral osmotic pressure in the adsorbed monolayer. Good agreement with available experimental data is achieved and we further make predictions of the protein orientation at hydrophobic and charged surfaces. Finally, we present a perturbation theory for predicting shifts in the protein isoelectric point due to close proximity to charged surfaces. Although this approximate model requires only single protein properties (mean charge and its variance), excellent agreement is found with MC simulations.

  14. CapsidMaps: protein-protein interaction pattern discovery platform for the structural analysis of virus capsids using Google Maps.

    Science.gov (United States)

    Carrillo-Tripp, Mauricio; Montiel-García, Daniel Jorge; Brooks, Charles L; Reddy, Vijay S

    2015-04-01

    Structural analysis and visualization of protein-protein interactions is a challenging task since it is difficult to appreciate easily the extent of all contacts made by the residues forming the interfaces. In the case of viruses, structural analysis becomes even more demanding because several interfaces coexist and, in most cases, these are formed by hundreds of contacting residues that belong to multiple interacting coat proteins. CapsidMaps is an interactive analysis and visualization tool that is designed to benefit the structural virology community. Developed as an improved extension of the φ-ψ Explorer, here we describe the details of its design and implementation. We present results of analysis of a spherical virus to showcase the features and utility of the new tool. CapsidMaps also facilitates the comparison of quaternary interactions between two spherical virus particles by computing a similarity (S)-score. The tool can also be used to identify residues that are solvent exposed and in the process of locating antigenic epitope regions as well as residues forming the inside surface of the capsid that interact with the nucleic acid genome. CapsidMaps is part of the VIPERdb Science Gateway, and is freely available as a web-based and cross-browser compliant application at http://viperdb.scripps.edu.

  15. AraPPISite: a database of fine-grained protein-protein interaction site annotations for Arabidopsis thaliana.

    Science.gov (United States)

    Li, Hong; Yang, Shiping; Wang, Chuan; Zhou, Yuan; Zhang, Ziding

    2016-09-01

    Knowledge about protein interaction sites provides detailed information of protein-protein interactions (PPIs). To date, nearly 20,000 of PPIs from Arabidopsis thaliana have been identified. Nevertheless, the interaction site information has been largely missed by previously published PPI databases. Here, AraPPISite, a database that presents fine-grained interaction details for A. thaliana PPIs is established. First, the experimentally determined 3D structures of 27 A. thaliana PPIs are collected from the Protein Data Bank database and the predicted 3D structures of 3023 A. thaliana PPIs are modeled by using two well-established template-based docking methods. For each experimental/predicted complex structure, AraPPISite not only provides an interactive user interface for browsing interaction sites, but also lists detailed evolutionary and physicochemical properties of these sites. Second, AraPPISite assigns domain-domain interactions or domain-motif interactions to 4286 PPIs whose 3D structures cannot be modeled. In this case, users can easily query protein interaction regions at the sequence level. AraPPISite is a free and user-friendly database, which does not require user registration or any configuration on local machines. We anticipate AraPPISite can serve as a helpful database resource for the users with less experience in structural biology or protein bioinformatics to probe the details of PPIs, and thus accelerate the studies of plant genetics and functional genomics. AraPPISite is available at http://systbio.cau.edu.cn/arappisite/index.html .

  16. Surface dynamics in allosteric regulation of protein-protein interactions: modulation of calmodulin functions by Ca2+.

    Directory of Open Access Journals (Sweden)

    Yosef Y Kuttner

    2013-04-01

    Full Text Available Knowledge of the structural basis of protein-protein interactions (PPI is of fundamental importance for understanding the organization and functioning of biological networks and advancing the design of therapeutics which target PPI. Allosteric modulators play an important role in regulating such interactions by binding at site(s orthogonal to the complex interface and altering the protein's propensity for complex formation. In this work, we apply an approach recently developed by us for analyzing protein surfaces based on steered molecular dynamics simulation (SMD to the study of the dynamic properties of functionally distinct conformations of a model protein, calmodulin (CaM, whose ability to interact with target proteins is regulated by the presence of the allosteric modulator Ca(2+. Calmodulin is a regulatory protein that acts as an intracellular Ca(2+ sensor to control a wide variety of cellular processes. We demonstrate that SMD analysis is capable of pinpointing CaM surfaces implicated in the recognition of both the allosteric modulator Ca(2+ and target proteins. Our analysis of changes in the dynamic properties of the CaM backbone elicited by Ca(2+ binding yielded new insights into the molecular mechanism of allosteric regulation of CaM-target interactions.

  17. Associative learning in biochemical networks.

    Science.gov (United States)

    Gandhi, Nikhil; Ashkenasy, Gonen; Tannenbaum, Emmanuel

    2007-11-07

    It has been recently suggested that there are likely generic features characterizing the emergence of systems constructed from the self-organization of self-replicating agents acting under one or more selection pressures. Therefore, structures and behaviors at one length scale may be used to infer analogous structures and behaviors at other length scales. Motivated by this suggestion, we seek to characterize various "animate" behaviors in biochemical networks, and the influence that these behaviors have on genomic evolution. Specifically, in this paper, we develop a simple, chemostat-based model illustrating how a process analogous to associative learning can occur in a biochemical network. Associative learning is a form of learning whereby a system "learns" to associate two stimuli with one another. Associative learning, also known as conditioning, is believed to be a powerful learning process at work in the brain (associative learning is essentially "learning by analogy"). In our model, two types of replicating molecules, denoted as A and B, are present in some initial concentration in the chemostat. Molecules A and B are stimulated to replicate by some growth factors, denoted as G(A) and G(B), respectively. It is also assumed that A and B can covalently link, and that the conjugated molecule can be stimulated by either the G(A) or G(B) growth factors (and can be degraded). We show that, if the chemostat is stimulated by both growth factors for a certain time, followed by a time gap during which the chemostat is not stimulated at all, and if the chemostat is then stimulated again by only one of the growth factors, then there will be a transient increase in the number of molecules activated by the other growth factor. Therefore, the chemostat bears the imprint of earlier, simultaneous stimulation with both growth factors, which is indicative of associative learning. It is interesting to note that the dynamics of our model is consistent with certain aspects of

  18. Biochemical genetic markers in sugarcane.

    Science.gov (United States)

    Glaszmann, J C; Fautret, A; Noyer, J L; Feldmann, P; Lanaud, C

    1989-10-01

    Isozyme variation was used to identify biochemical markers of potential utility in sugarcane genetics and breeding. Electrophoretic polymorphism was surveyed for nine enzymes among 39 wild and noble sugarcane clones, belonging to the species most closely related to modern varieties. Up to 114 distinct bands showing presence versus absence type of variation were revealed and used for qualitative characterization of the materials. Multivariate analysis of the data isolated the Erianthus clone sampled and separated the Saccharum spontaneum clones from the S. robustum and S. officinarum clones; the latter two were not differentiated from one another. The analysis of self-progenies of a 2n=112 S. spontaneum and of a commercial variety showed examples of mono- and polyfactorial segregations. Within the progeny of the variety, co-segregation of two isozymes frequent in S. spontaneum led to them being assigned to a single chromosome initially contributed by a S. spontaneum donor. This illustrates how combined survey of ancestral species and segregation analysis in modern breeding materials should permit using the lack of interspecific cross-over to establish linkage groups in a sugarcane genome.

  19. An efficient heuristic method for active feature acquisition and its application to protein-protein interaction prediction

    Directory of Open Access Journals (Sweden)

    Thahir Mohamed

    2012-11-01

    Full Text Available Abstract Background Machine learning approaches for classification learn the pattern of the feature space of different classes, or learn a boundary that separates the feature space into different classes. The features of the data instances are usually available, and it is only the class-labels of the instances that are unavailable. For example, to classify text documents into different topic categories, the words in the documents are features and they are readily available, whereas the topic is what is predicted. However, in some domains obtaining features may be resource-intensive because of which not all features may be available. An example is that of protein-protein interaction prediction, where not only are the labels ('interacting' or 'non-interacting' unavailable, but so are some of the features. It may be possible to obtain at least some of the missing features by carrying out a few experiments as permitted by the available resources. If only a few experiments can be carried out to acquire missing features, which proteins should be studied and which features of those proteins should be determined? From the perspective of machine learning for PPI prediction, it would be desirable that those features be acquired which when used in training the classifier, the accuracy of the classifier is improved the most. That is, the utility of the feature-acquisition is measured in terms of how much acquired features contribute to improving the accuracy of the classifier. Active feature acquisition (AFA is a strategy to preselect such instance-feature combinations (i.e. protein and experiment combinations for maximum utility. The goal of AFA is the creation of optimal training set that would result in the best classifier, and not in determining the best classification model itself. Results We present a heuristic method for active feature acquisition to calculate the utility of acquiring a missing feature. This heuristic takes into account the change in

  20. Broadband electromagnetic charaterization of materials for accelerator components

    CERN Document Server

    De Michele, G; Grudiev, A; Metral, E; Pieloni, T; Rumolo, G

    2011-01-01

    Electromagnetic (EM) characterization of materials up to high frequencies is a major requirement for the correct modeling of many accelerator components: collimators, kickers, high order modes damping devices for accelerating cavities. In this scenario, the coaxial line method has gained much importance compared to other methods because of its applicability in a wide range of frequencies. In this paper we describe a new coaxial line method that allows using only one measurement setup to characterize the material in a range of frequency from few MHz up to several GHz. A coaxial cable fed at one side is filled with the material under test and closed on a known load on the other side. The properties of the material are obtained from the measured reflection coefficient by using it as input for a transmission line (TL) model, which describes the measurements setup. In addition, 3-D electromagnetic simulations have been performed to correct for the differences between the real geometry and the simplified TL model. ...

  1. Spatial charaterization of El Pardo landscape using Detrended Fluctuation analysis

    Science.gov (United States)

    Castellanos, Maria Teresa; Morato, Maria Carmen; Aguado, Pedro L.; del Monte, Juan P.; Tarquis, Ana M.

    2017-04-01

    The interactions among abiotic, biotic, and anthropic factors and their influence at different scales create a complex dynamic in landscape evolution. Scaling and multifractal analysis has the potential to characterize the landscape in terms of the statistical signature of the measure selected, in this case altitude. The study zone is a matrix of 2053 x 2053 pixels, with a resolution of 5m (25 m2 by pixel), obtained from a digital terrain model (DTM) using the latest informatics tools. This zone corresponds to homogeneous region with respect to soil characteristics and climatology but with topographic distinctive areas, known as "Monte de El Pardo". We found that the fluctuation statistics at different scales revealed a non-Gaussian character in the data. Generalized Hurst dimensions were calculated on several transects crossing the area studied exhibit multifractality in all of them within a certain range. Analysis of the directionality by means of a Generalized Hurst rose plot showed differences in scaling characteristics along river and reservoir direction and across it. The results show a growth of persistent behavior in all the directions and a clear anisotropy to be considered in bi-dimensional detrended fluctuation analysis.

  2. ANALYSIS OF THE TANK 5F FINAL CHARATERIZATION SAMPLES-2011

    Energy Technology Data Exchange (ETDEWEB)

    Oji, L.; Diprete, D.; Coleman, C.; Hay, M.

    2012-01-20

    The Savannah River National Laboratory (SRNL) was requested by SRR to provide sample preparation and analysis of the Tank 5F final characterization samples to determine the residual tank inventory prior to grouting. Two types of samples were collected and delivered to SRNL: floor samples across the tank and subsurface samples from mounds near risers 1 and 5 of Tank 5F. These samples were taken from Tank 5F between January and March 2011. These samples from individual locations in the tank (nine floor samples and six mound Tank 5F samples) were each homogenized and combined in a given proportion into 3 distinct composite samples to mimic the average composition in the entire tank. These Tank 5F composite samples were analyzed for radiological, chemical and elemental components. Additional measurements performed on the Tank 5F composite samples include bulk density and water leaching of the solids to account for water soluble species. With analyses for certain challenging radionuclides as the exception, all composite Tank 5F samples were analyzed and reported in triplicate. The target detection limits for isotopes analyzed were based on customer desired detection limits as specified in the technical task request documents. SRNL developed new methodologies to meet these target detection limits and provide data for the extensive suite of components. While many of the target detection limits were met for the species characterized for Tank 5F, as specified in the technical task request, some were not met. In a few cases, the relatively high levels of radioactive species of the same element or a chemically similar element precluded the ability to measure some isotopes to low levels. The Technical Task Request allows that while the analyses of these isotopes is needed, meeting the detection limits for these isotopes is a lower priority than meeting detection limits for the other specified isotopes. The isotopes whose detection limits were not met in all cases included the following: Al-26, Sn-126, Sb-126, Sb-126m, Eu-152 and Cf-249. SRNL, in conjunction with the plant customer, reviewed all these cases and determined that the impacts were negligible.

  3. A charaterization of closed maps using the whyburn construction

    Directory of Open Access Journals (Sweden)

    Yvonne O. Stallings

    1985-01-01

    Full Text Available In this paper we modify the Whyburn construction for a continuous function f:X→Y. If the range is first countable, we get a characterization of closed maps; namely, the constructions are the same if and only if the map is closed.

  4. High content screening biosensor assay to identify disruptors of p53-hDM2 protein-protein interactions.

    Science.gov (United States)

    Hua, Yun; Strock, Christopher J; Johnston, Paul A

    2015-01-01

    This chapter describes the implementation of the p53-hDM2 protein-protein interaction (PPI) biosensor (PPIB) HCS assay to identify disruptors of p53-hDM2 PPIs. Recombinant adenovirus expression constructs were generated bearing the individual p53-GFP and hDM2-RFP PPI partners. The N-terminal p53 transactivating domain that contains the binding site for hDM2 is expressed as a GFP fusion protein that is targeted and anchored in the nucleolus of infected cells by a nuclear localization (NLS) sequence. The p53-GFP biosensor is localized to the nucleolus to enhance and facilitate the image acquisition and analysis of the PPIs. The N-terminus of hDM2 encodes the domain for binding to the transactivating domain of p53, and is expressed as a RFP fusion protein that includes both an NLS and a nuclear export sequence (NES). In U-2 OS cells co-infected with both adenovirus constructs, the binding interactions between hDM2 and p53 result in both biosensors becoming co-localized within the nucleolus. Upon disruption of the p53-hDM2 PPIs, the p53-GFP biosensor remains in the nucleolus while the shuttling hDM2-RFP biosensor redistributes into the cytoplasm. p53-hDM2 PPIs are measured by acquiring fluorescent images of cells co-infected with both adenovirus biosensors on an automated HCS imaging platform and using an image analysis algorithm to quantify the relative distribution of the hDM2-RFP shuttling component of the biosensor between the cytoplasm and nuclear regions of compound treated cells.

  5. The TissueNet v.2 database: A quantitative view of protein-protein interactions across human tissues.

    Science.gov (United States)

    Basha, Omer; Barshir, Ruth; Sharon, Moran; Lerman, Eugene; Kirson, Binyamin F; Hekselman, Idan; Yeger-Lotem, Esti

    2017-01-04

    Knowledge of the molecular interactions of human proteins within tissues is important for identifying their tissue-specific roles and for shedding light on tissue phenotypes. However, many protein-protein interactions (PPIs) have no tissue-contexts. The TissueNet database bridges this gap by associating experimentally-identified PPIs with human tissues that were shown to express both pair-mates. Users can select a protein and a tissue, and obtain a network view of the query protein and its tissue-associated PPIs. TissueNet v.2 is an updated version of the TissueNet database previously featured in NAR. It includes over 40 human tissues profiled via RNA-sequencing or protein-based assays. Users can select their preferred expression data source and interactively set the expression threshold for determining tissue-association. The output of TissueNet v.2 emphasizes qualitative and quantitative features of query proteins and their PPIs. The tissue-specificity view highlights tissue-specific and globally-expressed proteins, and the quantitative view highlights proteins that were differentially expressed in the selected tissue relative to all other tissues. Together, these views allow users to quickly assess the unique versus global functionality of query proteins. Thus, TissueNet v.2 offers an extensive, quantitative and user-friendly interface to study the roles of human proteins across tissues. TissueNet v.2 is available at http://netbio.bgu.ac.il/tissuenet. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  6. NMR spectroscopy reveals unexpected structural variation at the protein-protein interface in MHC class I molecules

    Energy Technology Data Exchange (ETDEWEB)

    Beerbaum, Monika; Ballaschk, Martin; Erdmann, Natalja [Leibniz-Institut fuer Molekulare Pharmakologie (FMP) (Germany); Schnick, Christina [Freie Universitaet Berlin, Institut fuer Immungenetik, Charite-Universitaetsmedizin Berlin (Germany); Diehl, Anne [Leibniz-Institut fuer Molekulare Pharmakologie (FMP) (Germany); Uchanska-Ziegler, Barbara; Ziegler, Andreas [Freie Universitaet Berlin, Institut fuer Immungenetik, Charite-Universitaetsmedizin Berlin (Germany); Schmieder, Peter, E-mail: schmieder@fmp-berlin.de [Leibniz-Institut fuer Molekulare Pharmakologie (FMP) (Germany)

    2013-10-15

    {beta}{sub 2}-Microglobulin ({beta}{sub 2}m) is a small, monomorphic protein non-covalently bound to the heavy chain (HC) in polymorphic major histocompatibility complex (MHC) class I molecules. Given the high evolutionary conservation of structural features of {beta}{sub 2}m in various MHC molecules as shown by X-ray crystallography, {beta}{sub 2}m is often considered as a mere scaffolding protein. Using nuclear magnetic resonance (NMR) spectroscopy, we investigate here whether {beta}{sub 2}m residues at the interface to the HC exhibit changes depending on HC polymorphisms and the peptides bound to the complex in solution. First we show that human {beta}{sub 2}m can effectively be produced in deuterated form using high-cell-density-fermentation and we employ the NMR resonance assignments obtained for triple-labeled {beta}{sub 2}m bound to the HLA-B*27:09 HC to examine the {beta}{sub 2}m-HC interface. We then proceed to compare the resonances of {beta}{sub 2}m in two minimally distinct subtypes, HLA-B*27:09 and HLA-B*27:05, that are differentially associated with the spondyloarthropathy Ankylosing Spondylitis. Each of these subtypes is complexed with four distinct peptides for which structural information is already available. We find that only the resonances at the {beta}{sub 2}m-HC interface show a variation of their chemical shifts between the different complexes. This indicates the existence of an unexpected plasticity that enables {beta}{sub 2}m to accommodate changes that depend on HC polymorphism as well as on the bound peptide through subtle structural variations of the protein-protein interface.

  7. PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine

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    Baskin Berivan

    2003-03-01

    Full Text Available Abstract Background The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND. Results Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days. Conclusions Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information.

  8. BioC-compatible full-text passage detection for protein-protein interactions using extended dependency graph.

    Science.gov (United States)

    Peng, Yifan; Arighi, Cecilia; Wu, Cathy H; Vijay-Shanker, K

    2016-01-01

    There has been a large growth in the number of biomedical publications that report experimental results. Many of these results concern detection of protein-protein interactions (PPI). In BioCreative V, we participated in the BioC task and developed a PPI system to detect text passages with PPIs in the full-text articles. By adopting the BioC format, the output of the system can be seamlessly added to the biocuration pipeline with little effort required for the system integration. A distinctive feature of our PPI system is that it utilizes extended dependency graph, an intermediate level of representation that attempts to abstract away syntactic variations in text. As a result, we are able to use only a limited set of rules to extract PPI pairs in the sentences, and additional rules to detect additional passages for PPI pairs. For evaluation, we used the 95 articles that were provided for the BioC annotation task. We retrieved the unique PPIs from the BioGRID database for these articles and show that our system achieves a recall of 83.5%. In order to evaluate the detection of passages with PPIs, we further annotated Abstract and Results sections of 20 documents from the dataset and show that an f-value of 80.5% was obtained. To evaluate the generalizability of the system, we also conducted experiments on AIMed, a well-known PPI corpus. We achieved an f-value of 76.1% for sentence detection and an f-value of 64.7% for unique PPI detection.Database URL: http://proteininformationresource.org/iprolink/corpora.

  9. De novo design of protein-protein interactions through modification of inter-molecular helix-helix interface residues.

    Science.gov (United States)

    Yagi, Sota; Akanuma, Satoshi; Yamagishi, Manami; Uchida, Tatsuya; Yamagishi, Akihiko

    2016-05-01

    For de novo design of protein-protein interactions (PPIs), information on the shape and chemical complementarity of their interfaces is generally required. Recent advances in computational PPI design have allowed for de novo design of protein complexes, and several successful examples have been reported. In addition, a simple and easy-to-use approach has also been reported that arranges leucines on a solvent-accessible region of an α-helix and places charged residues around the leucine patch to induce interactions between the two helical peptides. For this study, we adopted this approach to de novo design a new PPI between the helical bundle proteins sulerythrin and LARFH. A non-polar patch was created on an α-helix of LARFH around which arginine residues were introduced to retain its solubility. The strongest interaction found was for the LARFH variant cysLARFH-IV-3L3R and the sulerythrin mutant 6L6D (KD=0.16 μM). This artificial protein complex is maintained by hydrophobic and ionic interactions formed by the inter-molecular helical bundle structure. Therefore, by the simple and easy-to-use approach to create de novo interfaces on the α-helices, we successfully generated an artificial PPI. We also created a second LARFH variant with the non-polar patch surrounded by positively charged residues at each end. Upon mixing this LARFH variant with 6L6D, mesh-like fibrous nanostructures were observed by atomic force microscopy. Our method may, therefore, also be applicable to the de novo design of protein nanostructures. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. The Prediction of Key Cytoskeleton Components Involved in Glomerular Diseases Based on a Protein-Protein Interaction Network.

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    Fangrui Ding

    Full Text Available Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet, which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and

  11. Lectin receptor kinases participate in protein-protein interactions to mediate plasma membrane-cell wall adhesions in Arabidopsis.

    Science.gov (United States)

    Gouget, Anne; Senchou, Virginie; Govers, Francine; Sanson, Arnaud; Barre, Annick; Rougé, Pierre; Pont-Lezica, Rafael; Canut, Hervé

    2006-01-01

    Interactions between plant cell walls and plasma membranes are essential for cells to function properly, but the molecules that mediate the structural continuity between wall and membrane are unknown. Some of these interactions, which are visualized upon tissue plasmolysis in Arabidopsis (Arabidopsis thaliana), are disrupted by the RGD (arginine-glycine-aspartic acid) tripeptide sequence, a characteristic cell adhesion motif in mammals. In planta induced-O (IPI-O) is an RGD-containing protein from the plant pathogen Phytophthora infestans that can disrupt cell wall-plasma membrane adhesions through its RGD motif. To identify peptide sequences that specifically bind the RGD motif of the IPI-O protein and potentially play a role in receptor recognition, we screened a heptamer peptide library displayed in a filamentous phage and selected two peptides acting as inhibitors of the plasma membrane RGD-binding activity of Arabidopsis. Moreover, the two peptides also disrupted cell wall-plasma membrane adhesions. Sequence comparison of the RGD-binding peptides with the Arabidopsis proteome revealed 12 proteins containing amino acid sequences in their extracellular domains common with the two RGD-binding peptides. Eight belong to the receptor-like kinase family, four of which have a lectin-like extracellular domain. The lectin domain of one of these, At5g60300, recognized the RGD motif both in peptides and proteins. These results imply that lectin receptor kinases are involved in protein-protein interactions with RGD-containing proteins as potential ligands, and play a structural and signaling role at the plant cell surfaces.

  12. Lectin Receptor Kinases Participate in Protein-Protein Interactions to Mediate Plasma Membrane-Cell Wall Adhesions in Arabidopsis1

    Science.gov (United States)

    Gouget, Anne; Senchou, Virginie; Govers, Francine; Sanson, Arnaud; Barre, Annick; Rougé, Pierre; Pont-Lezica, Rafael; Canut, Hervé

    2006-01-01

    Interactions between plant cell walls and plasma membranes are essential for cells to function properly, but the molecules that mediate the structural continuity between wall and membrane are unknown. Some of these interactions, which are visualized upon tissue plasmolysis in Arabidopsis (Arabidopsis thaliana), are disrupted by the RGD (arginine-glycine-aspartic acid) tripeptide sequence, a characteristic cell adhesion motif in mammals. In planta induced-O (IPI-O) is an RGD-containing protein from the plant pathogen Phytophthora infestans that can disrupt cell wall-plasma membrane adhesions through its RGD motif. To identify peptide sequences that specifically bind the RGD motif of the IPI-O protein and potentially play a role in receptor recognition, we screened a heptamer peptide library displayed in a filamentous phage and selected two peptides acting as inhibitors of the plasma membrane RGD-binding activity of Arabidopsis. Moreover, the two peptides also disrupted cell wall-plasma membrane adhesions. Sequence comparison of the RGD-binding peptides with the Arabidopsis proteome revealed 12 proteins containing amino acid sequences in their extracellular domains common with the two RGD-binding peptides. Eight belong to the receptor-like kinase family, four of which have a lectin-like extracellular domain. The lectin domain of one of these, At5g60300, recognized the RGD motif both in peptides and proteins. These results imply that lectin receptor kinases are involved in protein-protein interactions with RGD-containing proteins as potential ligands, and play a structural and signaling role at the plant cell surfaces. PMID:16361528

  13. Thioflavin S (NSC71948) interferes with Bcl-2-associated athanogene (BAG-1)-mediated protein-protein interactions.

    Science.gov (United States)

    Sharp, Adam; Crabb, Simon J; Johnson, Peter W M; Hague, Angela; Cutress, Ramsey; Townsend, Paul A; Ganesan, A; Packham, Graham

    2009-11-01

    The C-terminal BAG domain is thought to play a key role in BAG-1-induced survival and proliferation by mediating protein-protein interactions, for example, with heat shock proteins HSC70 and HSP70, and with RAF-1 kinase. Here, we have identified thioflavin S (NSC71948) as a potential small-molecule chemical inhibitor of these interactions. NSC71948 inhibited the interaction of BAG-1 and HSC70 in vitro and decreased BAG-1:HSC70 and BAG-1:HSP70 binding in intact cells. NSC71948 also reduced binding between BAG-1 and RAF-1, but had no effect on the interaction between two unrelated proteins, BIM and MCL-1. NSC71948 functionally reversed the ability of BAG-1 to promote vitamin D3 receptor-mediated transactivation, an activity of BAG-1 that depends on HSC70/HSP70 binding, and reduced phosphorylation of p44/42 mitogen-activate protein kinase. NSC71948 can be used to stain amyloid fibrils; however, structurally related compounds, thioflavin T and BTA-1, had no effect on BAG-1:HSC70 binding, suggesting that structural features important for amyloid fibril binding and inhibition of BAG-1:HSC70 binding may be separable. We demonstrated that NSC71948 inhibited the growth of BAG-1 expressing human ZR-75-1 breast cancer cells and wild-type, but not BAG-1-deficient, mouse embryo fibroblasts. Taken together, these data suggest that NSC71948 may be a useful molecule to investigate the functional significance of BAG-1 C-terminal protein interactions. However, it is important to recognize that NSC71948 may exert additional "off-target" effects. Inhibition of BAG-1 function may be an attractive strategy to inhibit the growth of BAG-1-overexpressing cancers, and further screens of additional compound collections may be warranted.

  14. Compartmentalization and Functionality of Nuclear Disorder: Intrinsic Disorder and Protein-Protein Interactions in Intra-Nuclear Compartments

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    Fanchi Meng

    2015-12-01

    Full Text Available The cell nucleus contains a number of membrane-less organelles or intra-nuclear compartments. These compartments are dynamic structures representing liquid-droplet phases which are only slightly denser than the bulk intra-nuclear fluid. They possess different functions, have diverse morphologies, and are typically composed of RNA (or, in some cases, DNA and proteins. We analyzed 3005 mouse proteins localized in specific intra-nuclear organelles, such as nucleolus, chromatin, Cajal bodies, nuclear speckles, promyelocytic leukemia (PML nuclear bodies, nuclear lamina, nuclear pores, and perinuclear compartment and compared them with ~29,863 non-nuclear proteins from mouse proteome. Our analysis revealed that intrinsic disorder is enriched in the majority of intra-nuclear compartments, except for the nuclear pore and lamina. These compartments are depleted in proteins that lack disordered domains and enriched in proteins that have multiple disordered domains. Moonlighting proteins found in multiple intra-nuclear compartments are more likely to have multiple disordered domains. Protein-protein interaction networks in the intra-nuclear compartments are denser and include more hubs compared to the non-nuclear proteins. Hubs in the intra-nuclear compartments (except for the nuclear pore are enriched in disorder compared with non-nuclear hubs and non-nuclear proteins. Therefore, our work provides support to the idea of the functional importance of intrinsic disorder in the cell nucleus and shows that many proteins associated with sub-nuclear organelles in nuclei of mouse cells are enriched in disorder. This high level of disorder in the mouse nuclear proteins defines their ability to serve as very promiscuous binders, possessing both large quantities of potential disorder-based interaction sites and the ability of a single such site to be involved in a large number of interactions.

  15. Localization of protein-protein interactions among three fluorescent proteins in a single living cell: three-color FRET microscopy

    Science.gov (United States)

    Sun, Yuansheng; Booker, Cynthia F.; Day, Richard N.; Periasamy, Ammasi

    2009-02-01

    Förster resonance energy transfer (FRET) methodology has been used for over 30 years to localize protein-protein interactions in living specimens. The cloning and modification of various visible fluorescent proteins (FPs) has generated a variety of new probes that can be used as FRET pairs to investigate the protein associations in living cells. However, the spectral cross-talk between FRET donor and acceptor channels has been a major limitation to FRET microscopy. Many investigators have developed different ways to eliminate the bleedthrough signals in the FRET channel for one donor and one acceptor. We developed a novel FRET microscopy method for studying interactions among three chromophores: three-color FRET microscopy. We generated a genetic construct that directly links the three FPs - monomeric teal FP (mTFP), Venus and tandem dimer Tomato (tdTomato), and demonstrated the occurrence of mutually dependent energy transfers among the three FPs. When expressed in cells and excited with the 458 nm laser line, the mTFP-Venus-tdTomato fusion proteins yielded parallel (mTFP to Venus and mTFP to tdTomato) and sequential (mTFP to Venus and then to tdTomato) energy transfer signals. To quantify the FRET signals in the three-FP system in a single living cell, we developed an algorithm to remove all the spectral cross-talk components and also to separate different FRET signals at a same emission channel using the laser scanning spectral imaging and linear unmixing techniques on the Zeiss510 META system. Our results were confirmed with fluorescence lifetime measurements and using acceptor photobleaching FRET microscopy.

  16. Analysis of origin and protein-protein interaction maps suggests distinct oncogenic role of nuclear EGFR during cancer evolution

    Science.gov (United States)

    Sharip, Ainur; Abdukhakimova, Diyora; Wang, Xiao; Kim, Alexey; Kim, Yevgeniy; Sharip, Aigul; Orakov, Askarbek; Miao, Lixia; Sun, Qinglei; Chen, Yue; Chen, Zhenbang; Xie, Yingqiu

    2017-01-01

    Receptor tyrosine kinase EGFR usually is localized on plasma membrane to induce progression of many cancers including cancers in children (Bodey et al. In Vivo. 2005, 19:931-41), but it contains a nuclear localization signal (NLS) that mediates EGFR nuclear translocation (Lin et al. Nat Cell Biol. 2001, 3:802-8). Here we report that NLS of EGFR has its old evolutionary origin. Protein-protein interaction maps suggests that nEGFR pathways are different from membrane EGFR and EGF is not found in nEGFR network while androgen receptor (AR) is found, which suggests the evolution of prostate cancer, a well-known AR driven cancer, through changes in androgen- or EGF-dependence. Database analysis suggests that nEGFR correlates with the tumor grades especially in prostate cancer patients. Structural predication analysis suggests that NLS can compromise the differential protein binding to EGFR through stretch linkers with evolutionary mutation from N to V. In experiment, elevation of nEGFR but not membrane EGFR was found in castration resistant prostate cancer cells. Finally, systems analysis of NLS and transmembrane domain (TM) suggests that NLS has old origin while NLS neighboring domain of TM has been undergone accelerated evolution. Thus nEGFR has an old origin resembling the cancer evolution but TM may interfere with NLS driven signaling for natural selection of survival to evade NLS induced aggressive cancers. Our data suggest NLS is a dynamic inducer of EGFR oncogenesis during evolution for advanced cancers. Our model provides novel insights into the evolutionary role of NLS of oncogenic kinases in cancers.

  17. BIOCHEMICAL SCREENING OF DIABETIC NEPHROPATHY

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    Vivek

    2016-01-01

    Full Text Available Diabetic nephropathy is a clinical syndrome characterized by the following- Persistent albuminuria (>300mg/d or >200μg/min, that is confirmed on at least 2 occasions 3-6 months apart diabetic, progressive decline in the Glomerular Filtration Rate (GFR, elevated arterial blood pressure. The earliest biochemical criteria for the diagnosis of diabetic nephropathy is the presence of micro-albumin in the urine, which if left untreated will eventually lead to End-Stage Renal Disease (ESRD. Micro-albuminuria refers to the excretion of albumin in the urine at a rate that exceeds normal limits. The current study was conducted to establish the prevalence of micro-albuminuria in a sequential sample of diabetic patients attending hospital and OPD Clinic to determine its relationship with known and putative risk factors to identify micro- and normo-albuminuric patients in their sample for subsequent comparison in different age, sex, weight and creatinine clearance of the micro- and normo-albuminuric patients. This cross-sectional analytical study was conducted in one hundred patients at Saraswathi Institute of Medical Sciences, Anwarpur, Hapur, U. P. Patients having diabetes mellitus in different age group ranging from 30 to 70 years were selected. Data was analysed by SPSS software. Micro-albuminuria was observed in 35% in patients with type 2 diabetes mellitus. It was observed that 65% patients were free from any type of albuminuria. Also micro-albuminuria was present in 10% of the patients less than 50 yrs. of age, while 15% of the patients more than 50 yrs. of age were having micro-albuminuria. There was a statistically significant correlation of micro-albuminuria with duration of diabetes. Incidence of micro-albuminuria increases with age as well as increased duration of diabetes mellitus. Our study shows that only 5% patients developed macro-albuminuria. Glycosylated haemoglobin and fasting plasma glucose was significantly raised among all these

  18. Detection and analysis of protein-protein interactions in organellar and prokaryotic proteomes by native gel electrophoresis: (Membrane) protein complexes and supercomplexes.

    Science.gov (United States)

    Krause, Frank

    2006-07-01

    It is an essential and challenging task to unravel protein-protein interactions in their actual in vivo context. Native gel systems provide a separation platform allowing the analysis of protein complexes on a rather proteome-wide scale in a single experiment. This review focus on blue-native (BN)-PAGE as the most versatile and successful gel-based approach to separate soluble and membrane protein complexes of intricate protein mixtures derived from all biological sources. BN-PAGE is a charge-shift method with a running pH of 7.5 relying on the gentle binding of anionic CBB dye to all membrane and many soluble protein complexes, leading to separation of protein species essentially according to their size and superior resolution than other fractionation techniques can offer. The closely related colorless-native (CN)-PAGE, whose applicability is restricted to protein species with intrinsic negative net charge, proved to provide an especially mild separation capable of preserving weak protein-protein interactions better than BN-PAGE. The essential conditions determining the success of detecting protein-protein interactions are the sample preparations, e.g. the efficiency/mildness of the detergent solubilization of membrane protein complexes. A broad overview about the achievements of BN- and CN-PAGE studies to elucidate protein-protein interactions in organelles and prokaryotes is presented, e.g. the mitochondrial protein import machinery and oxidative phosphorylation supercomplexes. In many cases, solubilization with digitonin was demonstrated to facilitate an efficient and particularly gentle extraction of membrane protein complexes prone to dissociation by treatment with other detergents. In general, analyses of protein interactomes should be carried out by both BN- and CN-PAGE.

  19. Enabling systematic interrogation of protein-protein interactions in live cells with a versatile ultra-high-throughput biosensor platform | Office of Cancer Genomics

    Science.gov (United States)

    The vast datasets generated by next generation gene sequencing and expression profiling have transformed biological and translational research. However, technologies to produce large-scale functional genomics datasets, such as high-throughput detection of protein-protein interactions (PPIs), are still in early development. While a number of powerful technologies have been employed to detect PPIs, a singular PPI biosensor platform featured with both high sensitivity and robustness in a mammalian cell environment remains to be established.

  20. Nontypeable Haemophilus influenzae exploits the interaction between protein-E and vitronectin for the adherence and invasion to bronchial epithelial cells

    OpenAIRE

    Ikeda, Masaki; 池田, 政輝

    2015-01-01

    Background: Nontypeable Haemophilus influenzae (NTHi) is one of the most common Gram-negative pathogens in otitis media and exacerbation of chronic obstructive pulmonary disease. NTHi has been reported to invade bronchial epithelial cells. This penetration enables NTHi to evade the host immune system and antibiotics, and it seems to be related to the intractable features of these diseases. However, the precise mechanism of the invasion has been unknown. We hypothesized that protein-E, an oute...