WorldWideScience

Sample records for protein interactions effects

  1. Quantifying the molecular origins of opposite solvent effects on protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Vincent Vagenende

    Full Text Available Although the nature of solvent-protein interactions is generally weak and non-specific, addition of cosolvents such as denaturants and osmolytes strengthens protein-protein interactions for some proteins, whereas it weakens protein-protein interactions for others. This is exemplified by the puzzling observation that addition of glycerol oppositely affects the association constants of two antibodies, D1.3 and D44.1, with lysozyme. To resolve this conundrum, we develop a methodology based on the thermodynamic principles of preferential interaction theory and the quantitative characterization of local protein solvation from molecular dynamics simulations. We find that changes of preferential solvent interactions at the protein-protein interface quantitatively account for the opposite effects of glycerol on the antibody-antigen association constants. Detailed characterization of local protein solvation in the free and associated protein states reveals how opposite solvent effects on protein-protein interactions depend on the extent of dewetting of the protein-protein contact region and on structural changes that alter cooperative solvent-protein interactions at the periphery of the protein-protein interface. These results demonstrate the direct relationship between macroscopic solvent effects on protein-protein interactions and atom-scale solvent-protein interactions, and establish a general methodology for predicting and understanding solvent effects on protein-protein interactions in diverse biological environments.

  2. The effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on protein-protein interactions.

    Science.gov (United States)

    Yates, Christopher M; Sternberg, Michael J E

    2013-11-01

    Non-synonymous single nucleotide polymorphisms (nsSNPs) are single base changes leading to a change to the amino acid sequence of the encoded protein. Many of these variants are associated with disease, so nsSNPs have been well studied, with studies looking at the effects of nsSNPs on individual proteins, for example, on stability and enzyme active sites. In recent years, the impact of nsSNPs upon protein-protein interactions has also been investigated, giving a greater insight into the mechanisms by which nsSNPs can lead to disease. In this review, we summarize these studies, looking at the various mechanisms by which nsSNPs can affect protein-protein interactions. We focus on structural changes that can impair interaction, changes to disorder, gain of interaction, and post-translational modifications before looking at some examples of nsSNPs at human-pathogen protein-protein interfaces and the analysis of nsSNPs from a network perspective. © 2013.

  3. Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Peng Liu

    2015-01-01

    Full Text Available A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

  4. Water-Protein Interactions: The Secret of Protein Dynamics

    Directory of Open Access Journals (Sweden)

    Silvia Martini

    2013-01-01

    Full Text Available Water-protein interactions help to maintain flexible conformation conditions which are required for multifunctional protein recognition processes. The intimate relationship between the protein surface and hydration water can be analyzed by studying experimental water properties measured in protein systems in solution. In particular, proteins in solution modify the structure and the dynamics of the bulk water at the solute-solvent interface. The ordering effects of proteins on hydration water are extended for several angstroms. In this paper we propose a method for analyzing the dynamical properties of the water molecules present in the hydration shells of proteins. The approach is based on the analysis of the effects of protein-solvent interactions on water protons NMR relaxation parameters. NMR relaxation parameters, especially the nonselective (R1NS and selective (R1SE spin-lattice relaxation rates of water protons, are useful for investigating the solvent dynamics at the macromolecule-solvent interfaces as well as the perturbation effects caused by the water-macromolecule interactions on the solvent dynamical properties. In this paper we demonstrate that Nuclear Magnetic Resonance Spectroscopy can be used to determine the dynamical contributions of proteins to the water molecules belonging to their hydration shells.

  5. Effects of salts on protein-surface interactions: applications for column chromatography.

    Science.gov (United States)

    Tsumoto, Kouhei; Ejima, Daisuke; Senczuk, Anna M; Kita, Yoshiko; Arakawa, Tsutomu

    2007-07-01

    Development of protein pharmaceuticals depends on the availability of high quality proteins. Various column chromatographies are used to purify proteins and characterize the purity and properties of the proteins. Most column chromatographies require salts, whether inorganic or organic, for binding, elution or simply better recovery and resolution. The salts modulate affinity of the proteins for particular columns and nonspecific protein-protein or protein-surface interactions, depending on the type and concentration of the salts, in both specific and nonspecific manners. Salts also affect the binding capacity of the column, which determines the size of the column to be used. Binding capacity, whether equilibrium or dynamic (under an approximation of a slow flow rate), depends on the binding constant, protein concentration and the number of the binding site on the column as well as nonspecific binding. This review attempts to summarize the mechanism of the salt effects on binding affinity and capacity for various column chromatographies and on nonspecific protein-protein or protein-surface interactions. Understanding such salt effects should also be useful in preventing nonspecific protein binding to various containers. Copyright 2007 Wiley-Liss, Inc.

  6. 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

  7. Specificity of molecular interactions in transient protein-protein interaction interfaces.

    Science.gov (United States)

    Cho, Kyu-il; Lee, KiYoung; Lee, Kwang H; Kim, Dongsup; Lee, Doheon

    2006-11-15

    In this study, we investigate what types of interactions are specific to their biological function, and what types of interactions are persistent regardless of their functional category in transient protein-protein heterocomplexes. This is the first approach to analyze protein-protein interfaces systematically at the molecular interaction level in the context of protein functions. We perform systematic analysis at the molecular interaction level using classification and feature subset selection technique prevalent in the field of pattern recognition. To represent the physicochemical properties of protein-protein interfaces, we design 18 molecular interaction types using canonical and noncanonical interactions. Then, we construct input vector using the frequency of each interaction type in protein-protein interface. We analyze the 131 interfaces of transient protein-protein heterocomplexes in PDB: 33 protease-inhibitors, 52 antibody-antigens, 46 signaling proteins including 4 cyclin dependent kinase and 26 G-protein. Using kNN classification and feature subset selection technique, we show that there are specific interaction types based on their functional category, and such interaction types are conserved through the common binding mechanism, rather than through the sequence or structure conservation. The extracted interaction types are C(alpha)-- H...O==C interaction, cation...anion interaction, amine...amine interaction, and amine...cation interaction. With these four interaction types, we achieve the classification success rate up to 83.2% with leave-one-out cross-validation at k = 15. Of these four interaction types, C(alpha)--H...O==C shows binding specificity for protease-inhibitor complexes, while cation-anion interaction is predominant in signaling complexes. The amine ... amine and amine...cation interaction give a minor contribution to the classification accuracy. When combined with these two interactions, they increase the accuracy by 3.8%. In the case of

  8. Thermal motion in proteins: Large effects on the time-averaged interaction energies

    International Nuclear Information System (INIS)

    Goethe, Martin; Rubi, J. Miguel; Fita, Ignacio

    2016-01-01

    As a consequence of thermal motion, inter-atomic distances in proteins fluctuate strongly around their average values, and hence, also interaction energies (i.e. the pair-potentials evaluated at the fluctuating distances) are not constant in time but exhibit pronounced fluctuations. These fluctuations cause that time-averaged interaction energies do generally not coincide with the energy values obtained by evaluating the pair-potentials at the average distances. More precisely, time-averaged interaction energies behave typically smoother in terms of the average distance than the corresponding pair-potentials. This averaging effect is referred to as the thermal smoothing effect. Here, we estimate the strength of the thermal smoothing effect on the Lennard-Jones pair-potential for globular proteins at ambient conditions using x-ray diffraction and simulation data of a representative set of proteins. For specific atom species, we find a significant smoothing effect where the time-averaged interaction energy of a single atom pair can differ by various tens of cal/mol from the Lennard-Jones potential at the average distance. Importantly, we observe a dependency of the effect on the local environment of the involved atoms. The effect is typically weaker for bulky backbone atoms in beta sheets than for side-chain atoms belonging to other secondary structure on the surface of the protein. The results of this work have important practical implications for protein software relying on free energy expressions. We show that the accuracy of free energy expressions can largely be increased by introducing environment specific Lennard-Jones parameters accounting for the fact that the typical thermal motion of protein atoms depends strongly on their local environment.

  9. Thermal motion in proteins: Large effects on the time-averaged interaction energies

    Energy Technology Data Exchange (ETDEWEB)

    Goethe, Martin, E-mail: martingoethe@ub.edu; Rubi, J. Miguel [Departament de Física Fonamental, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona (Spain); Fita, Ignacio [Institut de Biologia Molecular de Barcelona, Baldiri Reixac 10, 08028 Barcelona (Spain)

    2016-03-15

    As a consequence of thermal motion, inter-atomic distances in proteins fluctuate strongly around their average values, and hence, also interaction energies (i.e. the pair-potentials evaluated at the fluctuating distances) are not constant in time but exhibit pronounced fluctuations. These fluctuations cause that time-averaged interaction energies do generally not coincide with the energy values obtained by evaluating the pair-potentials at the average distances. More precisely, time-averaged interaction energies behave typically smoother in terms of the average distance than the corresponding pair-potentials. This averaging effect is referred to as the thermal smoothing effect. Here, we estimate the strength of the thermal smoothing effect on the Lennard-Jones pair-potential for globular proteins at ambient conditions using x-ray diffraction and simulation data of a representative set of proteins. For specific atom species, we find a significant smoothing effect where the time-averaged interaction energy of a single atom pair can differ by various tens of cal/mol from the Lennard-Jones potential at the average distance. Importantly, we observe a dependency of the effect on the local environment of the involved atoms. The effect is typically weaker for bulky backbone atoms in beta sheets than for side-chain atoms belonging to other secondary structure on the surface of the protein. The results of this work have important practical implications for protein software relying on free energy expressions. We show that the accuracy of free energy expressions can largely be increased by introducing environment specific Lennard-Jones parameters accounting for the fact that the typical thermal motion of protein atoms depends strongly on their local environment.

  10. 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

  11. 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

  12. Selection of peptides interfering with protein-protein interaction.

    Science.gov (United States)

    Gaida, Annette; Hagemann, Urs B; Mattay, Dinah; Räuber, Christina; Müller, Kristian M; Arndt, Katja M

    2009-01-01

    Cell physiology depends on a fine-tuned network of protein-protein interactions, and misguided interactions are often associated with various diseases. Consequently, peptides, which are able to specifically interfere with such adventitious interactions, are of high interest for analytical as well as medical purposes. One of the most abundant protein interaction domains is the coiled-coil motif, and thus provides a premier target. Coiled coils, which consist of two or more alpha-helices wrapped around each other, have one of the simplest interaction interfaces, yet they are able to confer highly specific homo- and heterotypic interactions involved in virtually any cellular process. While there are several ways to generate interfering peptides, the combination of library design with a powerful selection system seems to be one of the most effective and promising approaches. This chapter guides through all steps of such a process, starting with library options and cloning, detailing suitable selection techniques and ending with purification for further down-stream characterization. Such generated peptides will function as versatile tools to interfere with the natural function of their targets thereby illuminating their down-stream signaling and, in general, promoting understanding of factors leading to specificity and stability in protein-protein interactions. Furthermore, peptides interfering with medically relevant proteins might become important diagnostics and therapeutics.

  13. Protein-protein interactions: an application of Tus-Ter mediated protein microarray system.

    Science.gov (United States)

    Sitaraman, Kalavathy; Chatterjee, Deb K

    2011-01-01

    In this chapter, we present a novel, cost-effective microarray strategy that utilizes expression-ready plasmid DNAs to generate protein arrays on-demand and its use to validate protein-protein interactions. These expression plasmids were constructed in such a way so as to serve a dual purpose of synthesizing the protein of interest as well as capturing the synthesized protein. The microarray system is based on the high affinity binding of Escherichia coli "Tus" protein to "Ter," a 20 bp DNA sequence involved in the regulation of DNA replication. The protein expression is carried out in a cell-free protein synthesis system, with rabbit reticulocyte lysates, and the target proteins are detected either by labeled incorporated tag specific or by gene-specific antibodies. This microarray system has been successfully used for the detection of protein-protein interaction because both the target protein and the query protein can be transcribed and translated simultaneously in the microarray slides. The utility of this system for detecting protein-protein interaction is demonstrated by a few well-known examples: Jun/Fos, FRB/FKBP12, p53/MDM2, and CDK4/p16. In all these cases, the presence of protein complexes resulted in the localization of fluorophores at the specific sites of the immobilized target plasmids. Interestingly, during our interactions studies we also detected a previously unknown interaction between CDK2 and p16. Thus, this Tus-Ter based system of protein microarray can be used for the validation of known protein interactions as well as for identifying new protein-protein interactions. In addition, it can be used to examine and identify targets of nucleic acid-protein, ligand-receptor, enzyme-substrate, and drug-protein interactions.

  14. 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.

  15. Specific effects of c-Jun NH2-terminal kinase-interacting protein 1 in neuronal axons

    Directory of Open Access Journals (Sweden)

    Shu Tang

    2016-01-01

    Full Text Available c-Jun NH2-terminal kinase (JNK-interacting protein 3 plays an important role in brain-derived neurotrophic factor/tropomyosin-related kinase B (TrkB anterograde axonal transport. It remains unclear whether JNK-interacting protein 1 mediates similar effects, or whether JNK-interacting protein 1 affects the regulation of TrkB anterograde axonal transport. In this study, we isolated rat embryonic hippocampus and cultured hippocampal neurons in vitro. Coimmunoprecipitation results demonstrated that JNK-interacting protein 1 formed TrkB complexes in vitro and in vivo. Immunocytochemistry results showed that when JNK-interacting protein 1 was highly expressed, the distribution of TrkB gradually increased in axon terminals. However, the distribution of TrkB reduced in axon terminals after knocking out JNK-interacting protein 1. In addition, there were differences in distribution of TrkB after JNK-interacting protein 1 was knocked out compared with not. However, knockout of JNK-interacting protein 1 did not affect the distribution of TrkB in dendrites. These findings confirm that JNK-interacting protein 1 can interact with TrkB in neuronal cells, and can regulate the transport of TrkB in axons, but not in dendrites.

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

    Directory of Open Access Journals (Sweden)

    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

  17. Protein function prediction using neighbor relativity in protein-protein interaction network.

    Science.gov (United States)

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

    There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

  18. 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.

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

    KAUST Repository

    Sá nchez Claros, Carmen; Tramontano, Anna

    2012-01-01

    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.

  20. A credit-card library approach for disrupting protein-protein interactions.

    Science.gov (United States)

    Xu, Yang; Shi, Jin; Yamamoto, Noboru; Moss, Jason A; Vogt, Peter K; Janda, Kim D

    2006-04-15

    Protein-protein interfaces are prominent in many therapeutically important targets. Using small organic molecules to disrupt protein-protein interactions is a current challenge in chemical biology. An important example of protein-protein interactions is provided by the Myc protein, which is frequently deregulated in human cancers. Myc belongs to the family of basic helix-loop-helix leucine zipper (bHLH-ZIP) transcription factors. It is biologically active only as heterodimer with the bHLH-ZIP protein Max. Herein, we report a new strategy for the disruption of protein-protein interactions that has been corroborated through the design and synthesis of a small parallel library composed of 'credit-card' compounds. These compounds are derived from a planar, aromatic scaffold and functionalized with four points of diversity. From a 285 membered library, several hits were obtained that disrupted the c-Myc-Max interaction and cellular functions of c-Myc. The IC50 values determined for this small focused library for the disruption of Myc-Max dimerization are quite potent, especially since small molecule antagonists of protein-protein interactions are notoriously difficult to find. Furthermore, several of the compounds were active at the cellular level as shown by their biological effects on Myc action in chicken embryo fibroblast assays. In light of our findings, this approach is considered a valuable addition to the armamentarium of new molecules being developed to interact with protein-protein interfaces. Finally, this strategy for disrupting protein-protein interactions should prove applicable to other families of proteins.

  1. 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.

  2. Prediction of thermodynamic instabilities of protein solutions from simple protein–protein interactions

    International Nuclear Information System (INIS)

    D’Agostino, Tommaso; Solana, José Ramón; Emanuele, Antonio

    2013-01-01

    Highlights: ► We propose a model of effective protein–protein interaction embedding solvent effects. ► A previous square-well model is enhanced by giving to the interaction a free energy character. ► The temperature dependence of the interaction is due to entropic effects of the solvent. ► The validity of the original SW model is extended to entropy driven phase transitions. ► We get good fits for lysozyme and haemoglobin spinodal data taken from literature. - Abstract: Statistical thermodynamics of protein solutions is often studied in terms of simple, microscopic models of particles interacting via pairwise potentials. Such modelling can reproduce the short range structure of protein solutions at equilibrium and predict thermodynamics instabilities of these systems. We introduce a square well model of effective protein–protein interaction that embeds the solvent’s action. We modify an existing model [45] by considering a well depth having an explicit dependence on temperature, i.e. an explicit free energy character, thus encompassing the statistically relevant configurations of solvent molecules around proteins. We choose protein solutions exhibiting demixing upon temperature decrease (lysozyme, enthalpy driven) and upon temperature increase (haemoglobin, entropy driven). We obtain satisfactory fits of spinodal curves for both the two proteins without adding any mean field term, thus extending the validity of the original model. Our results underline the solvent role in modulating or stretching the interaction potential

  3. Aquaporin Protein-Protein Interactions

    Directory of Open Access Journals (Sweden)

    Jennifer Virginia Roche

    2017-10-01

    Full Text Available Aquaporins are tetrameric membrane-bound channels that facilitate transport of water and other small solutes across cell membranes. In eukaryotes, they are frequently regulated by gating or trafficking, allowing for the cell to control membrane permeability in a specific manner. Protein–protein interactions play crucial roles in both regulatory processes and also mediate alternative functions such as cell adhesion. In this review, we summarize recent knowledge about aquaporin protein–protein interactions; dividing the interactions into three types: (1 interactions between aquaporin tetramers; (2 interactions between aquaporin monomers within a tetramer (hetero-tetramerization; and (3 transient interactions with regulatory proteins. We particularly focus on the structural aspects of the interactions, discussing the small differences within a conserved overall fold that allow for aquaporins to be differentially regulated in an organism-, tissue- and trigger-specific manner. A deep knowledge about these differences is needed to fully understand aquaporin function and regulation in many physiological processes, and may enable design of compounds targeting specific aquaporins for treatment of human disease.

  4. Targeting protein-protein interactions for parasite control.

    Directory of Open Access Journals (Sweden)

    Christina M Taylor

    2011-04-01

    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. Our interests in protein-protein interactions

    Indian Academy of Sciences (India)

    protein interactions. Evolution of P-P partnerships. Evolution of P-P structures. Evolutionary dynamics of P-P interactions. Dynamics of P-P interaction network. Host-pathogen interactions. CryoEM mapping of gigantic protein assemblies.

  6. Evolution of protein-protein interactions

    Indian Academy of Sciences (India)

    Evolution of protein-protein interactions · Our interests in protein-protein interactions · Slide 3 · Slide 4 · Slide 5 · Slide 6 · Slide 7 · Slide 8 · Slide 9 · Slide 10 · Slide 11 · Slide 12 · Slide 13 · Slide 14 · Slide 15 · Slide 16 · Slide 17 · Slide 18 · Slide 19 · Slide 20.

  7. Proteins interacting with cloning scars: a source of false positive protein-protein interactions.

    Science.gov (United States)

    Banks, Charles A S; Boanca, Gina; Lee, Zachary T; Florens, Laurence; Washburn, Michael P

    2015-02-23

    A common approach for exploring the interactome, the network of protein-protein interactions in cells, uses a commercially available ORF library to express affinity tagged bait proteins; these can be expressed in cells and endogenous cellular proteins that copurify with the bait can be identified as putative interacting proteins using mass spectrometry. Control experiments can be used to limit false-positive results, but in many cases, there are still a surprising number of prey proteins that appear to copurify specifically with the bait. Here, we have identified one source of false-positive interactions in such studies. We have found that a combination of: 1) the variable sequence of the C-terminus of the bait with 2) a C-terminal valine "cloning scar" present in a commercially available ORF library, can in some cases create a peptide motif that results in the aberrant co-purification of endogenous cellular proteins. Control experiments may not identify false positives resulting from such artificial motifs, as aberrant binding depends on sequences that vary from one bait to another. It is possible that such cryptic protein binding might occur in other systems using affinity tagged proteins; this study highlights the importance of conducting careful follow-up studies where novel protein-protein interactions are suspected.

  8. Non-interacting surface solvation and dynamics in protein-protein interactions

    NARCIS (Netherlands)

    Visscher, Koen M.; Kastritis, Panagiotis L.|info:eu-repo/dai/nl/315886668; Bonvin, Alexandre M J J|info:eu-repo/dai/nl/113691238

    2015-01-01

    Protein-protein interactions control a plethora of cellular processes, including cell proliferation, differentiation, apoptosis, and signal transduction. Understanding how and why proteins interact will inevitably lead to novel structure-based drug design methods, as well as design of de novo

  9. 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.

  10. Inferring Domain-Domain Interactions from Protein-Protein Interactions with Formal Concept Analysis

    Science.gov (United States)

    Khor, Susan

    2014-01-01

    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. PMID:24586450

  11. Detecting protein-protein interactions in living cells

    DEFF Research Database (Denmark)

    Gottschalk, Marie; Bach, Anders; Hansen, Jakob Lerche

    2009-01-01

    to the endogenous C-terminal peptide of the NMDA receptor, as evaluated by a cell-free protein-protein interaction assay. However, it is important to address both membrane permeability and effect in living cells. Therefore a bioluminescence resonance energy transfer (BRET) assay was established, where the C......-terminal of the NMDA receptor and PDZ2 of PSD-95 were fused to green fluorescent protein (GFP) and Renilla luciferase (Rluc) and expressed in COS7 cells. A robust and specific BRET signal was obtained by expression of the appropriate partner proteins and subsequently, the assay was used to evaluate a Tat......The PDZ domain mediated interaction between the NMDA receptor and its intracellular scaffolding protein, PSD-95, is a potential target for treatment of ischemic brain diseases. We have recently developed a number of peptide analogues with improved affinity for the PDZ domains of PSD-95 compared...

  12. Effects of solute-solute interactions on protein stability studied using various counterions and dendrimers.

    Directory of Open Access Journals (Sweden)

    Curtiss P Schneider

    Full Text Available Much work has been performed on understanding the effects of additives on protein thermodynamics and degradation kinetics, in particular addressing the Hofmeister series and other broad empirical phenomena. Little attention, however, has been paid to the effect of additive-additive interactions on proteins. Our group and others have recently shown that such interactions can actually govern protein events, such as aggregation. Here we use dendrimers, which have the advantage that both size and surface chemical groups can be changed and therein studied independently. Dendrimers are a relatively new and broad class of materials which have been demonstrated useful in biological and therapeutic applications, such as drug delivery, perturbing amyloid formation, etc. Guanidinium modified dendrimers pose an interesting case given that guanidinium can form multiple attractive hydrogen bonds with either a protein surface or other components in solution, such as hydrogen bond accepting counterions. Here we present a study which shows that the behavior of such macromolecule species (modified PAMAM dendrimers is governed by intra-solvent interactions. Attractive guanidinium-anion interactions seem to cause clustering in solution, which inhibits cooperative binding to the protein surface but at the same time, significantly suppresses nonnative aggregation.

  13. Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    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

  14. Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning

    Science.gov (United States)

    Zhao, Nan; Han, Jing Ginger; Shyu, Chi-Ren; Korkin, Dmitry

    2014-01-01

    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 rewiring of

  15. 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

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

    International Nuclear Information System (INIS)

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

    2016-01-01

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Feng, Mei [Department of Physics, Institute of Quantitative Biology, Zhejiang University, Hangzhou 310027 (China); Kang, Hongsuk; Luan, Binquan [Computational Biological Center, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598 (United States); Yang, Zaixing [Institute of Quantitative Biology and Medicine, SRMP and RAD-X, and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123 (China); Zhou, Ruhong, E-mail: ruhong@us.ibm.com [Department of Physics, Institute of Quantitative Biology, Zhejiang University, Hangzhou 310027 (China); Computational Biological Center, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598 (United States); Department of Chemistry, Columbia University, New York, New York 10027 (United States)

    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.

  18. 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 for s...... and with independence of the partner. This information is encoded at the residue level and could be easily incorporated in the initial grid scoring for Fast Fourier Transform rigid-body docking methods.......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...... for selecting rigid-body docking poses. These potentials include the energetic component that provides the residues with a particular secondary structure and surface accessibility. These scoring functions have been tested on a state-of-art benchmark dataset and on a decoy dataset of permanent interactions. Our...

  19. Effective Identification of Akt Interacting Proteins by Two-Step Chemical Crosslinking, Co-Immunoprecipitation and Mass Spectrometry

    Science.gov (United States)

    Huang, Bill X.; Kim, Hee-Yong

    2013-01-01

    Akt is a critical protein for cell survival and known to interact with various proteins. However, Akt binding partners that modulate or regulate Akt activation have not been fully elucidated. Identification of Akt-interacting proteins has been customarily achieved by co-immunoprecipitation combined with western blot and/or MS analysis. An intrinsic problem of the method is loss of interacting proteins during procedures to remove non-specific proteins. Moreover, antibody contamination often interferes with the detection of less abundant proteins. Here, we developed a novel two-step chemical crosslinking strategy to overcome these problems which resulted in a dramatic improvement in identifying Akt interacting partners. Akt antibody was first immobilized on protein A/G beads using disuccinimidyl suberate and allowed to bind to cellular Akt along with its interacting proteins. Subsequently, dithiobis[succinimidylpropionate], a cleavable crosslinker, was introduced to produce stable complexes between Akt and binding partners prior to the SDS-PAGE and nanoLC-MS/MS analysis. This approach enabled identification of ten Akt partners from cell lysates containing as low as 1.5 mg proteins, including two new potential Akt interacting partners. None of these but one protein was detectable without crosslinking procedures. The present method provides a sensitive and effective tool to probe Akt-interacting proteins. This strategy should also prove useful for other protein interactions, particularly those involving less abundant or weakly associating partners. PMID:23613850

  20. Interaction between plate make and protein in protein crystallisation screening.

    Directory of Open Access Journals (Sweden)

    Gordon J King

    Full Text Available BACKGROUND: Protein crystallisation screening involves the parallel testing of large numbers of candidate conditions with the aim of identifying conditions suitable as a starting point for the production of diffraction quality crystals. Generally, condition screening is performed in 96-well plates. While previous studies have examined the effects of protein construct, protein purity, or crystallisation condition ingredients on protein crystallisation, few have examined the effect of the crystallisation plate. METHODOLOGY/PRINCIPAL FINDINGS: We performed a statistically rigorous examination of protein crystallisation, and evaluated interactions between crystallisation success and plate row/column, different plates of same make, different plate makes and different proteins. From our analysis of protein crystallisation, we found a significant interaction between plate make and the specific protein being crystallised. CONCLUSIONS/SIGNIFICANCE: Protein crystal structure determination is the principal method for determining protein structure but is limited by the need to produce crystals of the protein under study. Many important proteins are difficult to crystallize, so that identification of factors that assist crystallisation could open up the structure determination of these more challenging targets. Our findings suggest that protein crystallisation success may be improved by matching a protein with its optimal plate make.

  1. 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.

  2. Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces.

    Directory of Open Access Journals (Sweden)

    Ching-Tai Chen

    Full Text Available 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

  3. 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

  4. Protein-material interactions: From micro-to-nano scale

    International Nuclear Information System (INIS)

    Tsapikouni, Theodora S.; Missirlis, Yannis F.

    2008-01-01

    The article presents a survey on the significance of protein-material interactions, the mechanisms which control them and the techniques used for their study. Protein-surface interactions play a key role in regenerative medicine, drug delivery, biosensor technology and chromatography, while it is related to various undesired effects such as biofouling and bio-prosthetic malfunction. Although the effects of protein-surface interaction concern the micro-scale, being sometimes obvious even with bare eyes, they derive from biophysical events at the nano-scale. The sequential steps for protein adsorption involve events at the single biomolecule level and the forces driving or inhibiting protein adsorption act at the molecular level too. Following the scaling of protein-surface interactions, various techniques have been developed for their study both in the micro- and nano-scale. Protein labelling with radioisotopes or fluorescent probes, colorimetric assays and the quartz crystal microbalance were the first techniques used to monitor protein adsorption isotherms, while the surface force apparatus was used to measure the interaction forces between protein layers at the micro-scale. Recently, more elaborate techniques like total internal reflection fluorescence (TIRF), Fourier transform infrared spectroscopy (FTIR), surface plasmon resonance, Raman spectroscopy, ellipsometry and time of flight secondary ion mass spectrometry (ToF-SIMS) have been applied for the investigation of protein density, structure or orientation at the interfaces. However, a turning point in the study of protein interactions with the surfaces was the invention and the wide-spread use of atomic force microscopy (AFM) which can both image single protein molecules on surfaces and directly measure the interaction force

  5. Specificity and affinity quantification of protein-protein interactions.

    Science.gov (United States)

    Yan, Zhiqiang; Guo, Liyong; Hu, Liang; Wang, Jin

    2013-05-01

    Most biological processes are mediated by the protein-protein interactions. Determination of the protein-protein structures and insight into their interactions are vital to understand the mechanisms of protein functions. Currently, compared with the isolated protein structures, only a small fraction of protein-protein structures are experimentally solved. Therefore, the computational docking methods play an increasing role in predicting the structures and interactions of protein-protein complexes. The scoring function of protein-protein interactions is the key responsible for the accuracy of the computational docking. Previous scoring functions were mostly developed by optimizing the binding affinity which determines the stability of the protein-protein complex, but they are often lack of the consideration of specificity which determines the discrimination of native protein-protein complex against competitive ones. We developed a scoring function (named as SPA-PP, specificity and affinity of the protein-protein interactions) by incorporating both the specificity and affinity into the optimization strategy. The testing results and comparisons with other scoring functions show that SPA-PP performs remarkably on both predictions of binding pose and binding affinity. Thus, SPA-PP is a promising quantification of protein-protein interactions, which can be implemented into the protein docking tools and applied for the predictions of protein-protein structure and affinity. The algorithm is implemented in C language, and the code can be downloaded from http://dl.dropbox.com/u/1865642/Optimization.cpp.

  6. The Ser/Thr Protein Kinase Protein-Protein Interaction Map of M. tuberculosis.

    Science.gov (United States)

    Wu, Fan-Lin; Liu, Yin; Jiang, He-Wei; Luan, Yi-Zhao; Zhang, Hai-Nan; He, Xiang; Xu, Zhao-Wei; Hou, Jing-Li; Ji, Li-Yun; Xie, Zhi; Czajkowsky, Daniel M; Yan, Wei; Deng, Jiao-Yu; Bi, Li-Jun; Zhang, Xian-En; Tao, Sheng-Ce

    2017-08-01

    Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, the leading cause of death among all infectious diseases. There are 11 eukaryotic-like serine/threonine protein kinases (STPKs) in Mtb, which are thought to play pivotal roles in cell growth, signal transduction and pathogenesis. However, their underlying mechanisms of action remain largely uncharacterized. In this study, using a Mtb proteome microarray, we have globally identified the binding proteins in Mtb for all of the STPKs, and constructed the first STPK protein interaction (KPI) map that includes 492 binding proteins and 1,027 interactions. Bioinformatics analysis showed that the interacting proteins reflect diverse functions, including roles in two-component system, transcription, protein degradation, and cell wall integrity. Functional investigations confirmed that PknG regulates cell wall integrity through key components of peptidoglycan (PG) biosynthesis, e.g. MurC. The global STPK-KPIs network constructed here is expected to serve as a rich resource for understanding the key signaling pathways in Mtb, thus facilitating drug development and effective control of Mtb. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  7. PPI-IRO: A two-stage method for protein-protein interaction extraction based on interaction relation ontology

    KAUST Repository

    Li, Chuanxi; Chen, Peng; Wang, Rujing; Wang, Xiujie; Su, Yaru; Li, Jinyan

    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

  8. The effects of incomplete protein interaction data on structural and evolutionary inferences

    DEFF Research Database (Denmark)

    de Silva, E; Thorne, T; Ingram, P

    2006-01-01

    of the inherent noise in protein interaction data. The effects of the incomplete nature of network data become very noticeable, especially for so-called network motifs. We also consider the effect of incomplete network data on functional and evolutionary inferences. Conclusion Crucially, when only small, partial...

  9. Force spectroscopy studies on protein-ligand interactions: a single protein mechanics perspective.

    Science.gov (United States)

    Hu, Xiaotang; Li, Hongbin

    2014-10-01

    Protein-ligand interactions are ubiquitous and play important roles in almost every biological process. The direct elucidation of the thermodynamic, structural and functional consequences of protein-ligand interactions is thus of critical importance to decipher the mechanism underlying these biological processes. A toolbox containing a variety of powerful techniques has been developed to quantitatively study protein-ligand interactions in vitro as well as in living systems. The development of atomic force microscopy-based single molecule force spectroscopy techniques has expanded this toolbox and made it possible to directly probe the mechanical consequence of ligand binding on proteins. Many recent experiments have revealed how ligand binding affects the mechanical stability and mechanical unfolding dynamics of proteins, and provided mechanistic understanding on these effects. The enhancement effect of mechanical stability by ligand binding has been used to help tune the mechanical stability of proteins in a rational manner and develop novel functional binding assays for protein-ligand interactions. Single molecule force spectroscopy studies have started to shed new lights on the structural and functional consequence of ligand binding on proteins that bear force under their biological settings. Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  10. Protein interactions in genome maintenance as novel antibacterial targets.

    Directory of Open Access Journals (Sweden)

    Aimee H Marceau

    Full Text Available Antibacterial compounds typically act by directly inhibiting essential bacterial enzyme activities. Although this general mechanism of action has fueled traditional antibiotic discovery efforts for decades, new antibiotic development has not kept pace with the emergence of drug resistant bacterial strains. These limitations have severely restricted the therapeutic tools available for treating bacterial infections. Here we test an alternative antibacterial lead-compound identification strategy in which essential protein-protein interactions are targeted rather than enzymatic activities. Bacterial single-stranded DNA-binding proteins (SSBs form conserved protein interaction "hubs" that are essential for recruiting many DNA replication, recombination, and repair proteins to SSB/DNA nucleoprotein substrates. Three small molecules that block SSB/protein interactions are shown to have antibacterial activity against diverse bacterial species. Consistent with a model in which the compounds target multiple SSB/protein interactions, treatment of Bacillus subtilis cultures with the compounds leads to rapid inhibition of DNA replication and recombination, and ultimately to cell death. The compounds also have unanticipated effects on protein synthesis that could be due to a previously unknown role for SSB/protein interactions in translation or to off-target effects. Our results highlight the potential of targeting protein-protein interactions, particularly those that mediate genome maintenance, as a powerful approach for identifying new antibacterial compounds.

  11. 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...... interacting and non-interacting protein pairs to classify the structural features that sustain the binding (or non-binding) behavior. Our study indicates that not only the interacting region but also the rest of the protein surface are important for the interaction fate. The interpretation...... to score the likelihood of the interaction between two proteins and to develop a method for the prediction of PPIs. We have tested our method on several sets with unbalanced ratios of interactions and non-interactions to simulate real conditions, obtaining accuracies higher than 25% in the most unfavorable...

  12. Evolutionary reprograming of protein-protein interaction specificity.

    Science.gov (United States)

    Akiva, Eyal; Babbitt, Patricia C

    2015-10-22

    Using mutation libraries and deep sequencing, Aakre et al. study the evolution of protein-protein interactions using a toxin-antitoxin model. The results indicate probable trajectories via "intermediate" proteins that are promiscuous, thus avoiding transitions via non-interactions. These results extend observations about other biological interactions and enzyme evolution, suggesting broadly general principles. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. A rice kinase-protein interaction map.

    Science.gov (United States)

    Ding, Xiaodong; Richter, Todd; Chen, Mei; Fujii, Hiroaki; Seo, Young Su; Xie, Mingtang; Zheng, Xianwu; Kanrar, Siddhartha; Stevenson, Rebecca A; Dardick, Christopher; Li, Ying; Jiang, Hao; Zhang, Yan; Yu, Fahong; Bartley, Laura E; Chern, Mawsheng; Bart, Rebecca; Chen, Xiuhua; Zhu, Lihuang; Farmerie, William G; Gribskov, Michael; Zhu, Jian-Kang; Fromm, Michael E; Ronald, Pamela C; Song, Wen-Yuan

    2009-03-01

    Plants uniquely contain large numbers of protein kinases, and for the vast majority of the 1,429 kinases predicted in the rice (Oryza sativa) genome, little is known of their functions. Genetic approaches often fail to produce observable phenotypes; thus, new strategies are needed to delineate kinase function. We previously developed a cost-effective high-throughput yeast two-hybrid system. Using this system, we have generated a protein interaction map of 116 representative rice kinases and 254 of their interacting proteins. Overall, the resulting interaction map supports a large number of known or predicted kinase-protein interactions from both plants and animals and reveals many new functional insights. Notably, we found a potential widespread role for E3 ubiquitin ligases in pathogen defense signaling mediated by receptor-like kinases, particularly by the kinases that may have evolved from recently expanded kinase subfamilies in rice. We anticipate that the data provided here will serve as a foundation for targeted functional studies in rice and other plants. The application of yeast two-hybrid and TAPtag analyses for large-scale plant protein interaction studies is also discussed.

  14. A new protein-protein interaction sensor based on tripartite split-GFP association.

    Science.gov (United States)

    Cabantous, Stéphanie; Nguyen, Hau B; Pedelacq, Jean-Denis; Koraïchi, Faten; Chaudhary, Anu; Ganguly, Kumkum; Lockard, Meghan A; Favre, Gilles; Terwilliger, Thomas C; Waldo, Geoffrey S

    2013-10-04

    Monitoring protein-protein interactions in living cells is key to unraveling their roles in numerous cellular processes and various diseases. Previously described split-GFP based sensors suffer from poor folding and/or self-assembly background fluorescence. Here, we have engineered a micro-tagging system to monitor protein-protein interactions in vivo and in vitro. The assay is based on tripartite association between two twenty amino-acids long GFP tags, GFP10 and GFP11, fused to interacting protein partners, and the complementary GFP1-9 detector. When proteins interact, GFP10 and GFP11 self-associate with GFP1-9 to reconstitute a functional GFP. Using coiled-coils and FRB/FKBP12 model systems we characterize the sensor in vitro and in Escherichia coli. We extend the studies to mammalian cells and examine the FK-506 inhibition of the rapamycin-induced association of FRB/FKBP12. The small size of these tags and their minimal effect on fusion protein behavior and solubility should enable new experiments for monitoring protein-protein association by fluorescence.

  15. 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...... to characterize protein interaction networks. In this chapter we describe in detail the use of stable isotope labeling by amino acids in cell culture (SILAC) for the quantitative analysis of stimulus-dependent dynamic protein interactions.......Proteins exert their function inside a cell generally in multiprotein complexes. These complexes are highly dynamic structures changing their composition over time and cell state. The same protein may thereby fulfill different functions depending on its binding partners. Quantitative mass...

  16. Protein- protein interaction detection system using fluorescent protein microdomains

    Science.gov (United States)

    Waldo, Geoffrey S.; Cabantous, Stephanie

    2010-02-23

    The invention provides a protein labeling and interaction detection system based on engineered fragments of fluorescent and chromophoric proteins that require fused interacting polypeptides to drive the association of the fragments, and further are soluble and stable, and do not change the solubility of polypeptides to which they are fused. In one embodiment, a test protein X is fused to a sixteen amino acid fragment of GFP (.beta.-strand 10, amino acids 198-214), engineered to not perturb fusion protein solubility. A second test protein Y is fused to a sixteen amino acid fragment of GFP (.beta.-strand 11, amino acids 215-230), engineered to not perturb fusion protein solubility. When X and Y interact, they bring the GFP strands into proximity, and are detected by complementation with a third GFP fragment consisting of GFP amino acids 1-198 (strands 1-9). When GFP strands 10 and 11 are held together by interaction of protein X and Y, they spontaneous association with GFP strands 1-9, resulting in structural complementation, folding, and concomitant GFP fluorescence.

  17. Interaction of bovine gallbladder mucin and calcium-binding protein: effects on calcium phosphate precipitation

    NARCIS (Netherlands)

    Afdhal, N. H.; Ostrow, J. D.; Koehler, R.; Niu, N.; Groen, A. K.; Veis, A.; Nunes, D. P.; Offner, G. D.

    1995-01-01

    Gallstones consist of calcium salts and cholesterol crystals, arrayed on a matrix of gallbladder mucin (GBM), and regulatory proteins like calcium-binding protein (CBP). To determine if interactions between CBP and GBM follow a biomineralization scheme, their mutual binding and effects on CaHPO4

  18. On the role of electrostatics on protein-protein interactions

    Science.gov (United States)

    Zhang, Zhe; Witham, Shawn; Alexov, Emil

    2011-01-01

    The role of electrostatics on protein-protein interactions and binding is reviewed in this article. A brief outline of the computational modeling, in the framework of continuum electrostatics, is presented and basic electrostatic effects occurring upon the formation of the complex are discussed. The role of the salt concentration and pH of the water phase on protein-protein binding free energy is demonstrated and indicates that the increase of the salt concentration tends to weaken the binding, an observation that is attributed to the optimization of the charge-charge interactions across the interface. It is pointed out that the pH-optimum (pH of optimal binding affinity) varies among the protein-protein complexes, and perhaps is a result of their adaptation to particular subcellular compartment. At the end, the similarities and differences between hetero- and homo-complexes are outlined and discussed with respect to the binding mode and charge complementarity. PMID:21572182

  19. Parallel force assay for protein-protein interactions.

    Science.gov (United States)

    Aschenbrenner, Daniela; Pippig, Diana A; Klamecka, Kamila; Limmer, Katja; Leonhardt, Heinrich; Gaub, Hermann E

    2014-01-01

    Quantitative proteome research is greatly promoted by high-resolution parallel format assays. A characterization of protein complexes based on binding forces offers an unparalleled dynamic range and allows for the effective discrimination of non-specific interactions. Here we present a DNA-based Molecular Force Assay to quantify protein-protein interactions, namely the bond between different variants of GFP and GFP-binding nanobodies. We present different strategies to adjust the maximum sensitivity window of the assay by influencing the binding strength of the DNA reference duplexes. The binding of the nanobody Enhancer to the different GFP constructs is compared at high sensitivity of the assay. Whereas the binding strength to wild type and enhanced GFP are equal within experimental error, stronger binding to superfolder GFP is observed. This difference in binding strength is attributed to alterations in the amino acids that form contacts according to the crystal structure of the initial wild type GFP-Enhancer complex. Moreover, we outline the potential for large-scale parallelization of the assay.

  20. Kinome signaling through regulated protein-protein interactions in normal and cancer cells.

    Science.gov (United States)

    Pawson, Tony; Kofler, Michael

    2009-04-01

    The flow of molecular information through normal and oncogenic signaling pathways frequently depends on protein phosphorylation, mediated by specific kinases, and the selective binding of the resulting phosphorylation sites to interaction domains present on downstream targets. This physical and functional interplay of catalytic and interaction domains can be clearly seen in cytoplasmic tyrosine kinases such as Src, Abl, Fes, and ZAP-70. Although the kinase and SH2 domains of these proteins possess similar intrinsic properties of phosphorylating tyrosine residues or binding phosphotyrosine sites, they also undergo intramolecular interactions when linked together, in a fashion that varies from protein to protein. These cooperative interactions can have diverse effects on substrate recognition and kinase activity, and provide a variety of mechanisms to link the stimulation of catalytic activity to substrate recognition. Taken together, these data have suggested how protein kinases, and the signaling pathways in which they are embedded, can evolve complex properties through the stepwise linkage of domains within single polypeptides or multi-protein assemblies.

  1. Human cancer protein-protein interaction network: a structural perspective.

    Directory of Open Access Journals (Sweden)

    Gozde Kar

    2009-12-01

    Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub

  2. Topology and weights in a protein domain interaction network--a novel way to predict protein interactions.

    Science.gov (United States)

    Wuchty, Stefan

    2006-05-23

    While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions

  3. An ontology-based search engine for protein-protein interactions.

    Science.gov (United States)

    Park, Byungkyu; Han, Kyungsook

    2010-01-18

    Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database. We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions. Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology.

  4. Can infrared spectroscopy provide information on protein-protein interactions?

    Science.gov (United States)

    Haris, Parvez I

    2010-08-01

    For most biophysical techniques, characterization of protein-protein interactions is challenging; this is especially true with methods that rely on a physical phenomenon that is common to both of the interacting proteins. Thus, for example, in IR spectroscopy, the carbonyl vibration (1600-1700 cm(-1)) associated with the amide bonds from both of the interacting proteins will overlap extensively, making the interpretation of spectral changes very complicated. Isotope-edited infrared spectroscopy, where one of the interacting proteins is uniformly labelled with (13)C or (13)C,(15)N has been introduced as a solution to this problem, enabling the study of protein-protein interactions using IR spectroscopy. The large shift of the amide I band (approx. 45 cm(-1) towards lower frequency) upon (13)C labelling of one of the proteins reveals the amide I band of the unlabelled protein, enabling it to be used as a probe for monitoring conformational changes. With site-specific isotopic labelling, structural resolution at the level of individual amino acid residues can be achieved. Furthermore, the ability to record IR spectra of proteins in diverse environments means that isotope-edited IR spectroscopy can be used to structurally characterize difficult systems such as protein-protein complexes bound to membranes or large insoluble peptide/protein aggregates. In the present article, examples of application of isotope-edited IR spectroscopy for studying protein-protein interactions are provided.

  5. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

    Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

  6. 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

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

    Directory of Open Access Journals (Sweden)

    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

  8. Alignment of non-covalent interactions at protein-protein interfaces.

    Directory of Open Access Journals (Sweden)

    Hongbo Zhu

    Full Text Available BACKGROUND: The study and comparison of protein-protein interfaces is essential for the understanding of the mechanisms of interaction between proteins. While there are many methods for comparing protein structures and protein binding sites, so far no methods have been reported for comparing the geometry of non-covalent interactions occurring at protein-protein interfaces. METHODOLOGY/PRINCIPAL FINDINGS: Here we present a method for aligning non-covalent interactions between different protein-protein interfaces. The method aligns the vector representations of van der Waals interactions and hydrogen bonds based on their geometry. The method has been applied to a dataset which comprises a variety of protein-protein interfaces. The alignments are consistent to a large extent with the results obtained using two other complementary approaches. In addition, we apply the method to three examples of protein mimicry. The method successfully aligns respective interfaces and allows for recognizing conserved interface regions. CONCLUSIONS/SIGNIFICANCE: The Galinter method has been validated in the comparison of interfaces in which homologous subunits are involved, including cases of mimicry. The method is also applicable to comparing interfaces involving non-peptidic compounds. Galinter assists users in identifying local interface regions with similar patterns of non-covalent interactions. This is particularly relevant to the investigation of the molecular basis of interaction mimicry.

  9. Biospecific protein immobilization for rapid analysis of weak protein interactions using self-interaction nanoparticle spectroscopy.

    Science.gov (United States)

    Bengali, Aditya N; Tessier, Peter M

    2009-10-01

    "Reversible" protein interactions govern diverse biological behavior ranging from intracellular transport and toxic protein aggregation to protein crystallization and inactivation of protein therapeutics. Much less is known about weak protein interactions than their stronger counterparts since they are difficult to characterize, especially in a parallel format (in contrast to a sequential format) necessary for high-throughput screening. We have recently introduced a highly efficient approach of characterizing protein self-association, namely self-interaction nanoparticle spectroscopy (SINS; Tessier et al., 2008; J Am Chem Soc 130:3106-3112). This approach exploits the separation-dependent optical properties of gold nanoparticles to detect weak self-interactions between proteins immobilized on nanoparticles. A limitation of our previous work is that differences in the sequence and structure of proteins can lead to significant differences in their affinity to adsorb to nanoparticle surfaces, which complicates analysis of the corresponding protein self-association behavior. In this work we demonstrate a highly specific approach for coating nanoparticles with proteins using biotin-avidin interactions to generate protein-nanoparticle conjugates that report protein self-interactions through changes in their optical properties. Using lysozyme as a model protein that is refractory to characterization by conventional SINS, we demonstrate that surface Plasmon wavelengths for gold-avidin-lysozyme conjugates over a range of solution conditions (i.e., pH and ionic strength) are well correlated with lysozyme osmotic second virial coefficient measurements. Since SINS requires orders of magnitude less protein and time than conventional methods (e.g., static light scattering), we envision this approach will find application in large screens of protein self-association aimed at either preventing (e.g., protein aggregation) or promoting (e.g., protein crystallization) these

  10. 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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Parallel force assay for protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Daniela Aschenbrenner

    Full Text Available Quantitative proteome research is greatly promoted by high-resolution parallel format assays. A characterization of protein complexes based on binding forces offers an unparalleled dynamic range and allows for the effective discrimination of non-specific interactions. Here we present a DNA-based Molecular Force Assay to quantify protein-protein interactions, namely the bond between different variants of GFP and GFP-binding nanobodies. We present different strategies to adjust the maximum sensitivity window of the assay by influencing the binding strength of the DNA reference duplexes. The binding of the nanobody Enhancer to the different GFP constructs is compared at high sensitivity of the assay. Whereas the binding strength to wild type and enhanced GFP are equal within experimental error, stronger binding to superfolder GFP is observed. This difference in binding strength is attributed to alterations in the amino acids that form contacts according to the crystal structure of the initial wild type GFP-Enhancer complex. Moreover, we outline the potential for large-scale parallelization of the assay.

  12. Yeast Interacting Proteins Database: YLR447C, YOR047C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available xpression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Sp...; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt15p; act

  13. Semantic integration to identify overlapping functional modules in protein interaction networks

    Directory of Open Access Journals (Sweden)

    Ramanathan Murali

    2007-07-01

    Full Text Available Abstract Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.

  14. Topology and weights in a protein domain interaction network – a novel way to predict protein interactions

    Directory of Open Access Journals (Sweden)

    Wuchty Stefan

    2006-05-01

    Full Text Available Abstract Background While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. Results We consider a web of interactions between protein domains of the Protein Family database (PFAM, which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Conclusion Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we

  15. Coevolution study of mitochondria respiratory chain proteins: toward the understanding of protein--protein interaction.

    Science.gov (United States)

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

    2011-05-20

    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 × 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. Copyright © 2011. Published by Elsevier Ltd.

  16. Protein-protein interactions and cancer: targeting the central dogma.

    Science.gov (United States)

    Garner, Amanda L; Janda, Kim D

    2011-01-01

    Between 40,000 and 200,000 protein-protein interactions have been predicted to exist within the human interactome. As these interactions are of a critical nature in many important cellular functions and their dysregulation is causal of disease, the modulation of these binding events has emerged as a leading, yet difficult therapeutic arena. In particular, the targeting of protein-protein interactions relevant to cancer is of fundamental importance as the tumor-promoting function of several aberrantly expressed proteins in the cancerous state is directly resultant of its ability to interact with a protein-binding partner. Of significance, these protein complexes play a crucial role in each of the steps of the central dogma of molecular biology, the fundamental processes of genetic transmission. With the many important discoveries being made regarding the mechanisms of these genetic process, the identification of new chemical probes are needed to better understand and validate the druggability of protein-protein interactions related to the central dogma. In this review, we provide an overview of current small molecule-based protein-protein interaction inhibitors for each stage of the central dogma: transcription, mRNA splicing and translation. Importantly, through our analysis we have uncovered a lack of necessary probes targeting mRNA splicing and translation, thus, opening up the possibility for expansion of these fields.

  17. A conserved mammalian protein interaction network.

    Directory of Open Access Journals (Sweden)

    Åsa Pérez-Bercoff

    Full Text Available Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.

  18. Interactive protein manipulation

    International Nuclear Information System (INIS)

    2003-01-01

    We describe an interactive visualization and modeling program for the creation of protein structures ''from scratch''. The input to our program is an amino acid sequence -decoded from a gene- and a sequence of predicted secondary structure types for each amino acid-provided by external structure prediction programs. Our program can be used in the set-up phase of a protein structure prediction process; the structures created with it serve as input for a subsequent global internal energy minimization, or another method of protein structure prediction. Our program supports basic visualization methods for protein structures, interactive manipulation based on inverse kinematics, and visualization guides to aid a user in creating ''good'' initial structures

  19. Quality control methodology for high-throughput protein-protein interaction screening.

    Science.gov (United States)

    Vazquez, Alexei; Rual, Jean-François; Venkatesan, Kavitha

    2011-01-01

    Protein-protein interactions are key to many aspects of the cell, including its cytoskeletal structure, the signaling processes in which it is involved, or its metabolism. Failure to form protein complexes or signaling cascades may sometimes translate into pathologic conditions such as cancer or neurodegenerative diseases. The set of all protein interactions between the proteins encoded by an organism constitutes its protein interaction network, representing a scaffold for biological function. Knowing the protein interaction network of an organism, combined with other sources of biological information, can unravel fundamental biological circuits and may help better understand the molecular basics of human diseases. The protein interaction network of an organism can be mapped by combining data obtained from both low-throughput screens, i.e., "one gene at a time" experiments and high-throughput screens, i.e., screens designed to interrogate large sets of proteins at once. In either case, quality controls are required to deal with the inherent imperfect nature of experimental assays. In this chapter, we discuss experimental and statistical methodologies to quantify error rates in high-throughput protein-protein interactions screens.

  20. Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations.

    Science.gov (United States)

    Yim, Soorin; Yu, Hasun; Jang, Dongjin; Lee, Doheon

    2018-04-11

    Signaling pathways can be reconstructed by identifying 'effect types' (i.e. activation/inhibition) of protein-protein interactions (PPIs). Effect types are composed of 'directions' (i.e. upstream/downstream) and 'signs' (i.e. positive/negative), thereby requiring directions as well as signs of PPIs to predict signaling events from PPI networks. Here, we propose a computational method for systemically annotating effect types to PPIs using relations between functional information of proteins. We used regulates, positively regulates, and negatively regulates relations in Gene Ontology (GO) to predict directions and signs of PPIs. These relations indicate both directions and signs between GO terms so that we can project directions and signs between relevant GO terms to PPIs. Independent test results showed that our method is effective for predicting both directions and signs of PPIs. Moreover, our method outperformed a previous GO-based method that did not consider the relations between GO terms. We annotated effect types to human PPIs and validated several highly confident effect types against literature. The annotated human PPIs are available in Additional file 2 to aid signaling pathway reconstruction and network biology research. We annotated effect types to PPIs by using regulates, positively regulates, and negatively regulates relations in GO. We demonstrated that those relations are effective for predicting not only signs, but also directions of PPIs. The usefulness of those relations suggests their potential applications to other types of interactions such as protein-DNA interactions.

  1. Hot-spot analysis for drug discovery targeting protein-protein interactions.

    Science.gov (United States)

    Rosell, Mireia; Fernández-Recio, Juan

    2018-04-01

    Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

  2. Yeast Interacting Proteins Database: YGR013W, YKL012W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available tion U1 snRNP protein involved in splicing, interacts with the branchpoint-binding protein during the formation of the second commitm... PRP40 U1 snRNP protein involved in splicing, interacts with the branchpoint-binding protein during the form...ation of the second commitment complex Rows with this prey as prey (1) Rows with

  3. NMR Studies of Protein Hydration and Protein-Ligand Interactions

    Science.gov (United States)

    Chong, Yuan

    Water on the surface of a protein is called hydration water. Hydration water is known to play a crucial role in a variety of biological processes including protein folding, enzymatic activation, and drug binding. Although the significance of hydration water has been recognized, the underlying mechanism remains far from being understood. This dissertation employs a unique in-situ nuclear magnetic resonance (NMR) technique to study the mechanism of protein hydration and the role of hydration in alcohol-protein interactions. Water isotherms in proteins are measured at different temperatures via the in-situ NMR technique. Water is found to interact differently with hydrophilic and hydrophobic groups on the protein. Water adsorption on hydrophilic groups is hardly affected by the temperature, while water adsorption on hydrophobic groups strongly depends on the temperature around 10 C, below which the adsorption is substantially reduced. This effect is induced by the dramatic decrease in the protein flexibility below 10 C. Furthermore, nanosecond to microsecond protein dynamics and the free energy, enthalpy, and entropy of protein hydration are studied as a function of hydration level and temperature. A crossover at 10 C in protein dynamics and thermodynamics is revealed. The effect of water at hydrophilic groups on protein dynamics and thermodynamics shows little temperature dependence, whereas water at hydrophobic groups has stronger effect above 10 C. In addition, I investigate the role of water in alcohol binding to the protein using the in-situ NMR detection. The isotherms of alcohols are first measured on dry proteins, then on proteins with a series of controlled hydration levels. The free energy, enthalpy, and entropy of alcohol binding are also determined. Two distinct types of alcohol binding are identified. On the one hand, alcohols can directly bind to a few specific sites on the protein. This type of binding is independent of temperature and can be

  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. 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

  6. Stapled Voltage-Gated Calcium Channel (CaV) α-Interaction Domain (AID) Peptides Act As Selective Protein-Protein Interaction Inhibitors of CaV Function.

    Science.gov (United States)

    Findeisen, Felix; Campiglio, Marta; Jo, Hyunil; Abderemane-Ali, Fayal; Rumpf, Christine H; Pope, Lianne; Rossen, Nathan D; Flucher, Bernhard E; DeGrado, William F; Minor, Daniel L

    2017-06-21

    For many voltage-gated ion channels (VGICs), creation of a properly functioning ion channel requires the formation of specific protein-protein interactions between the transmembrane pore-forming subunits and cystoplasmic accessory subunits. Despite the importance of such protein-protein interactions in VGIC function and assembly, their potential as sites for VGIC modulator development has been largely overlooked. Here, we develop meta-xylyl (m-xylyl) stapled peptides that target a prototypic VGIC high affinity protein-protein interaction, the interaction between the voltage-gated calcium channel (Ca V ) pore-forming subunit α-interaction domain (AID) and cytoplasmic β-subunit (Ca V β). We show using circular dichroism spectroscopy, X-ray crystallography, and isothermal titration calorimetry that the m-xylyl staples enhance AID helix formation are structurally compatible with native-like AID:Ca V β interactions and reduce the entropic penalty associated with AID binding to Ca V β. Importantly, electrophysiological studies reveal that stapled AID peptides act as effective inhibitors of the Ca V α 1 :Ca V β interaction that modulate Ca V function in an Ca V β isoform-selective manner. Together, our studies provide a proof-of-concept demonstration of the use of protein-protein interaction inhibitors to control VGIC function and point to strategies for improved AID-based Ca V modulator design.

  7. Interactive protein manipulation

    Energy Technology Data Exchange (ETDEWEB)

    SNCrivelli@lbl.gov

    2003-07-01

    We describe an interactive visualization and modeling program for the creation of protein structures ''from scratch''. The input to our program is an amino acid sequence -decoded from a gene- and a sequence of predicted secondary structure types for each amino acid-provided by external structure prediction programs. Our program can be used in the set-up phase of a protein structure prediction process; the structures created with it serve as input for a subsequent global internal energy minimization, or another method of protein structure prediction. Our program supports basic visualization methods for protein structures, interactive manipulation based on inverse kinematics, and visualization guides to aid a user in creating ''good'' initial structures.

  8. [Detection of protein-protein interactions by FRET and BRET methods].

    Science.gov (United States)

    Matoulková, E; Vojtěšek, B

    2014-01-01

    Nowadays, in vivo protein-protein interaction studies have become preferable detecting meth-ods that enable to show or specify (already known) protein interactions and discover their inhibitors. They also facilitate detection of protein conformational changes and discovery or specification of signaling pathways in living cells. One group of in vivo methods enabling these findings is based on fluorescent resonance energy transfer (FRET) and its bio-luminescent modification (BRET). They are based on visualization of protein-protein interactions via light or enzymatic excitation of fluorescent or bio-luminescent proteins. These methods allow not only protein localization within the cell or its organelles (or small animals) but they also allow us to quantify fluorescent signals and to discover weak or strong interaction partners. In this review, we explain the principles of FRET and BRET, their applications in the characterization of protein-protein interactions and we describe several findings using these two methods that clarify molecular and cellular mechanisms and signals related to cancer biology.

  9. Yeast Interacting Proteins Database: YGL237C, YOR047C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ene expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding prote... expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein

  10. Yeast Interacting Proteins Database: YKL002W, YOR047C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ene expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding prote...xpression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Sp

  11. 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 (InWeb_InBioMap,......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...

  12. General and specific lipid-protein interactions in Na,K-ATPase.

    Science.gov (United States)

    Cornelius, F; Habeck, M; Kanai, R; Toyoshima, C; Karlish, S J D

    2015-09-01

    The molecular activity of Na,K-ATPase and other P2 ATPases like Ca(2+)-ATPase is influenced by the lipid environment via both general (physical) and specific (chemical) interactions. Whereas the general effects of bilayer structure on membrane protein function are fairly well described and understood, the importance of the specific interactions has only been realized within the last decade due particularly to the growing field of membrane protein crystallization, which has shed new light on the molecular details of specific lipid-protein interactions. It is a remarkable observation that specific lipid-protein interactions seem to be evolutionarily conserved, and conformations of specifically bound lipids at the lipid-protein surface within the membrane are similar in crystal structures determined with different techniques and sources of the protein, despite the rather weak lipid-protein interaction energy. Studies of purified detergent-soluble recombinant αβ or αβFXYD Na,K-ATPase complexes reveal three separate functional effects of phospholipids and cholesterol with characteristic structural selectivity. The observations suggest that these three effects are exerted at separate binding sites for phophatidylserine/cholesterol (stabilizing), polyunsaturated phosphatidylethanolamine (stimulatory), and saturated PC or sphingomyelin/cholesterol (inhibitory), which may be located within three lipid-binding pockets identified in recent crystal structures of Na,K-ATPase. The findings point to a central role of direct and specific interactions of different phospholipids and cholesterol in determining both stability and molecular activity of Na,K-ATPase and possible implications for physiological regulation by membrane lipid composition. This article is part of a special issue titled "Lipid-Protein Interactions." Copyright © 2015 Elsevier B.V. All rights reserved.

  13. Prediction of protein–protein interactions: unifying evolution and structure at protein interfaces

    International Nuclear Information System (INIS)

    Tuncbag, Nurcan; Gursoy, Attila; Keskin, Ozlem

    2011-01-01

    The vast majority of the chores in the living cell involve protein–protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein–protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations

  14. Interactions between whey proteins and kaolinite surfaces

    Energy Technology Data Exchange (ETDEWEB)

    Barral, S. [Department of Chemical Engineering and Environmental Technology, University of Oviedo, Julian Claveria 8, 33006 Oviedo (Spain); Villa-Garcia, M.A. [Department of Organic and Inorganic Chemistry, University of Oviedo, Julian Claveria 8, 33006 Oviedo (Spain)], E-mail: mavg@uniovi.es; Rendueles, M. [Project Management Area, University of Oviedo, Independencia 13, 33004 Oviedo (Spain); Diaz, M. [Department of Chemical Engineering and Environmental Technology, University of Oviedo, Julian Claveria 8, 33006 Oviedo (Spain)

    2008-07-15

    The nature of the interactions between whey proteins and kaolinite surfaces was investigated by adsorption-desorption experiments at room temperature, performed at the isoelectric point (IEP) of the proteins and at pH 7. It was found that kaolinite is a strong adsorbent for proteins, reaching the maximum adsorption capacity at the IEP of each protein. At pH 7.0, the retention capacity decreased considerably. The adsorption isotherms showed typical Langmuir characteristics. X-ray diffraction data for the protein-kaolinite complexes showed that protein molecules were not intercalated in the mineral structure, but immobilized at the external surfaces and the edges of the kaolinite. Fourier transform IR results indicate the absence of hydrogen bonding between kaolinite surfaces and the polypeptide chain. The adsorption patterns appear to be related to electrostatic interactions, although steric effects should be also considered.

  15. Interactions between whey proteins and kaolinite surfaces

    International Nuclear Information System (INIS)

    Barral, S.; Villa-Garcia, M.A.; Rendueles, M.; Diaz, M.

    2008-01-01

    The nature of the interactions between whey proteins and kaolinite surfaces was investigated by adsorption-desorption experiments at room temperature, performed at the isoelectric point (IEP) of the proteins and at pH 7. It was found that kaolinite is a strong adsorbent for proteins, reaching the maximum adsorption capacity at the IEP of each protein. At pH 7.0, the retention capacity decreased considerably. The adsorption isotherms showed typical Langmuir characteristics. X-ray diffraction data for the protein-kaolinite complexes showed that protein molecules were not intercalated in the mineral structure, but immobilized at the external surfaces and the edges of the kaolinite. Fourier transform IR results indicate the absence of hydrogen bonding between kaolinite surfaces and the polypeptide chain. The adsorption patterns appear to be related to electrostatic interactions, although steric effects should be also considered

  16. Detection of protein-protein interactions by ribosome display and protein in situ immobilisation.

    Science.gov (United States)

    He, Mingyue; Liu, Hong; Turner, Martin; Taussig, Michael J

    2009-12-31

    We describe a method for identification of protein-protein interactions by combining two cell-free protein technologies, namely ribosome display and protein in situ immobilisation. The method requires only PCR fragments as the starting material, the target proteins being made through cell-free protein synthesis, either associated with their encoding mRNA as ribosome complexes or immobilised on a solid surface. The use of ribosome complexes allows identification of interacting protein partners from their attached coding mRNA. To demonstrate the procedures, we have employed the lymphocyte signalling proteins Vav1 and Grb2 and confirmed the interaction between Grb2 and the N-terminal SH3 domain of Vav1. The method has promise for library screening of pairwise protein interactions, down to the analytical level of individual domain or motif mapping.

  17. Mass spectrometric analysis of protein interactions

    DEFF Research Database (Denmark)

    Borch, Jonas; Jørgensen, Thomas J. D.; Roepstorff, Peter

    2005-01-01

    Mass spectrometry is a powerful tool for identification of interaction partners and structural characterization of protein interactions because of its high sensitivity, mass accuracy and tolerance towards sample heterogeneity. Several tools that allow studies of protein interaction are now...... available and recent developments that increase the confidence of studies of protein interaction by mass spectrometry include quantification of affinity-purified proteins by stable isotope labeling and reagents for surface topology studies that can be identified by mass-contributing reporters (e.g. isotope...... labels, cleavable cross-linkers or fragment ions. The use of mass spectrometers to study protein interactions using deuterium exchange and for analysis of intact protein complexes recently has progressed considerably....

  18. Evolutionary diversification of protein-protein interactions by interface add-ons.

    Science.gov (United States)

    Plach, Maximilian G; Semmelmann, Florian; Busch, Florian; Busch, Markus; Heizinger, Leonhard; Wysocki, Vicki H; Merkl, Rainer; Sterner, Reinhard

    2017-10-03

    Cells contain a multitude of protein complexes whose subunits interact with high specificity. However, the number of different protein folds and interface geometries found in nature is limited. This raises the question of how protein-protein interaction specificity is achieved on the structural level and how the formation of nonphysiological complexes is avoided. Here, we describe structural elements called interface add-ons that fulfill this function and elucidate their role for the diversification of protein-protein interactions during evolution. We identified interface add-ons in 10% of a representative set of bacterial, heteromeric protein complexes. The importance of interface add-ons for protein-protein interaction specificity is demonstrated by an exemplary experimental characterization of over 30 cognate and hybrid glutamine amidotransferase complexes in combination with comprehensive genetic profiling and protein design. Moreover, growth experiments showed that the lack of interface add-ons can lead to physiologically harmful cross-talk between essential biosynthetic pathways. In sum, our complementary in silico, in vitro, and in vivo analysis argues that interface add-ons are a practical and widespread evolutionary strategy to prevent the formation of nonphysiological complexes by specializing protein-protein interactions.

  19. 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

  20. Yeast Interacting Proteins Database: YPR103W, YOR047C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available tein involved in control of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sensors...gulated gene expression; interacts with protein kinase Snf1p, glucose sensors Snf

  1. Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology.

    Science.gov (United States)

    DeBlasio, Stacy L; Chavez, Juan D; Alexander, Mariko M; Ramsey, John; Eng, Jimmy K; Mahoney, Jaclyn; Gray, Stewart M; Bruce, James E; Cilia, Michelle

    2016-02-15

    Demonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus [PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in the Luteoviridae and with unrelated viruses in the Herpesviridae and Adenoviridae. Functional analysis of three PLRV-interacting host proteins in planta using a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection-hallmarks of host-pathogen interactions-were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies. The exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used protein interaction

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

    Science.gov (United States)

    Yu, Xueping; Ivanic, Joseph; Memišević, Vesna; Wallqvist, Anders; Reifman, Jaques

    2011-01-01

    We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not

  3. Interactions of polyphenols with carbohydrates, lipids and proteins.

    Science.gov (United States)

    Jakobek, Lidija

    2015-05-15

    Polyphenols are secondary metabolites in plants, investigated intensively because of their potential positive effects on human health. Their bioavailability and mechanism of positive effects have been studied, in vitro and in vivo. Lately, a high number of studies takes into account the interactions of polyphenols with compounds present in foods, like carbohydrates, proteins or lipids, because these food constituents can have significant effects on the activity of phenolic compounds. This paper reviews the interactions between phenolic compounds and lipids, carbohydrates and proteins and their impact on polyphenol activity. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Protein complex prediction in large ontology attributed protein-protein interaction networks.

    Science.gov (United States)

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

    2013-01-01

    Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

  5. Computational studies on the interactions of nanomaterials with proteins and their impacts

    International Nuclear Information System (INIS)

    An De-Yi; Li Jing-Yuan; Su Ji-Guo; Li Chun-Hua

    2015-01-01

    The intensive concern over the biosafety of nanomaterials demands the systematic study of the mechanisms underlying their biological effects. Many of the effects of nanomaterials can be attributed to their interactions with proteins and their impacts on protein function. On the other hand, nanomaterials show potential for a variety of biomedical applications, many of which also involve direct interactions with proteins. In this paper, we review some recent computational studies on this subject, especially those investigating the interactions of carbon and gold nanomaterials. Beside hydrophobic and π-stacking interactions, the mode of interaction of carbon nanomaterials can also be regulated by their functional groups. The coatings of gold nanomaterials similarly adjust their mode of interaction, in addition to coordination interactions with the sulfur groups of cysteine residues and the imidazole groups of histidine residues. Nanomaterials can interact with multiple proteins and their impacts on protein activity are attributed to a wide spectrum of mechanisms. These findings on the mechanisms of nanomaterial–protein interactions can further guide the design and development of nanomaterials to realize their application in disease diagnosis and treatment. (paper)

  6. Protein-protein interactions in the regulation of WRKY transcription factors.

    Science.gov (United States)

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

    2013-03-01

    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 TTGACC/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.

  7. quinolinium iodide in suppression of protein–protein interactions

    Indian Academy of Sciences (India)

    In searching for alternative ways to reduce protein–protein interactions or to inhibit the amyloid formation, the inhibitory effects ..... ing the exposure of hydrophobic surfaces mirrors the ... is well-supported by electrostatic interactions between.

  8. DARC: Mapping Surface Topography by Ray-Casting for Effective Virtual Screening at Protein Interaction Sites.

    Science.gov (United States)

    Gowthaman, Ragul; Miller, Sven A; Rogers, Steven; Khowsathit, Jittasak; Lan, Lan; Bai, Nan; Johnson, David K; Liu, Chunjing; Xu, Liang; Anbanandam, Asokan; Aubé, Jeffrey; Roy, Anuradha; Karanicolas, John

    2016-05-12

    Protein-protein interactions represent an exciting and challenging target class for therapeutic intervention using small molecules. Protein interaction sites are often devoid of the deep surface pockets presented by "traditional" drug targets, and crystal structures reveal that inhibitors typically engage these sites using very shallow binding modes. As a consequence, modern virtual screening tools developed to identify inhibitors of traditional drug targets do not perform as well when they are instead deployed at protein interaction sites. To address the need for novel inhibitors of important protein interactions, here we introduce an alternate docking strategy specifically designed for this regime. Our method, termed DARC (Docking Approach using Ray-Casting), matches the topography of a surface pocket "observed" from within the protein to the topography "observed" when viewing a potential ligand from the same vantage point. We applied DARC to carry out a virtual screen against the protein interaction site of human antiapoptotic protein Mcl-1 and found that four of the top-scoring 21 compounds showed clear inhibition in a biochemical assay. The Ki values for these compounds ranged from 1.2 to 21 μM, and each had ligand efficiency comparable to promising small-molecule inhibitors of other protein-protein interactions. These hit compounds do not resemble the natural (protein) binding partner of Mcl-1, nor do they resemble any known inhibitors of Mcl-1. Our results thus demonstrate the utility of DARC for identifying novel inhibitors of protein-protein interactions.

  9. Yeast Interacting Proteins Database: YMR280C, YOR047C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available olved in control of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sensor... glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, an

  10. Protein-Protein Interaction Databases

    DEFF Research Database (Denmark)

    Szklarczyk, Damian; Jensen, Lars Juhl

    2015-01-01

    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...

  11. The potential of protein-nanomaterial interaction for advanced drug delivery.

    Science.gov (United States)

    Peng, Qiang; Mu, Huiling

    2016-03-10

    Nanomaterials, like nanoparticles, micelles, nano-sheets, nanotubes and quantum dots, have great potentials in biomedical fields. However, their delivery is highly limited by the formation of protein corona upon interaction with endogenous proteins. This new identity, instead of nanomaterial itself, would be the real substance the organs and cells firstly encounter. Consequently, the behavior of nanomaterials in vivo is uncontrollable and some undesired effects may occur, like rapid clearance from blood stream; risk of capillary blockage; loss of targeting capacity; and potential toxicity. Therefore, protein-nanomaterial interaction is a great challenge for nanomaterial systems and should be inhibited. However, this interaction can also be used to functionalize nanomaterials by forming a selected protein corona. Unlike other decoration using exogenous molecules, nanomaterials functionalized by selected protein corona using endogenous proteins would have greater promise for clinical use. In this review, we aim to provide a comprehensive understanding of protein-nanomaterial interaction. Importantly, a discussion about how to use such interaction is launched and some possible applications of such interaction for advanced drug delivery are presented. Copyright © 2016 Elsevier B.V. All rights reserved.

  12. Detection of protein complex from protein-protein interaction network using Markov clustering

    International Nuclear Information System (INIS)

    Ochieng, P J; Kusuma, W A; Haryanto, T

    2017-01-01

    Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks. (paper)

  13. Catching the PEG-induced attractive interaction between proteins.

    Science.gov (United States)

    Vivarès, D; Belloni, L; Tardieu, A; Bonneté, F

    2002-09-01

    We present the experimental and theoretical background of a method to characterize the protein-protein attractive potential induced by one of the mostly used crystallizing agents in the protein-field, the poly(ethylene glycol) (PEG). This attractive interaction is commonly called, in colloid physics, the depletion interaction. Small-Angle X-ray Scattering experiments and numerical treatments based on liquid-state theories were performed on urate oxidase-PEG mixtures with two different PEGs (3350 Da and 8000 Da). A "two-component" approach was used in which the polymer-polymer, the protein-polymer and the protein-protein pair potentials were determined. The resulting effective protein-protein potential was characterized. This potential is the sum of the free-polymer protein-protein potential and of the PEG-induced depletion potential. The depletion potential was found to be hardly dependent upon the protein concentration but strongly function of the polymer size and concentration. Our results were also compared with two models, which give an analytic expression for the depletion potential.

  14. Multiplex single-molecule interaction profiling of DNA-barcoded proteins.

    Science.gov (United States)

    Gu, Liangcai; Li, Chao; Aach, John; Hill, David E; Vidal, Marc; Church, George M

    2014-11-27

    In contrast with advances in massively parallel DNA sequencing, high-throughput protein analyses are often limited by ensemble measurements, individual analyte purification and hence compromised quality and cost-effectiveness. Single-molecule protein detection using optical methods is limited by the number of spectrally non-overlapping chromophores. Here we introduce a single-molecular-interaction sequencing (SMI-seq) technology for parallel protein interaction profiling leveraging single-molecule advantages. DNA barcodes are attached to proteins collectively via ribosome display or individually via enzymatic conjugation. Barcoded proteins are assayed en masse in aqueous solution and subsequently immobilized in a polyacrylamide thin film to construct a random single-molecule array, where barcoding DNAs are amplified into in situ polymerase colonies (polonies) and analysed by DNA sequencing. This method allows precise quantification of various proteins with a theoretical maximum array density of over one million polonies per square millimetre. Furthermore, protein interactions can be measured on the basis of the statistics of colocalized polonies arising from barcoding DNAs of interacting proteins. Two demanding applications, G-protein coupled receptor and antibody-binding profiling, are demonstrated. SMI-seq enables 'library versus library' screening in a one-pot assay, simultaneously interrogating molecular binding affinity and specificity.

  15. Drosophila protein interaction map (DPiM): a paradigm for metazoan protein complex interactions.

    Science.gov (United States)

    Guruharsha, K G; Obar, Robert A; Mintseris, Julian; Aishwarya, K; Krishnan, R T; Vijayraghavan, K; Artavanis-Tsakonas, Spyros

    2012-01-01

    Proteins perform essential cellular functions as part of protein complexes, often in conjunction with RNA, DNA, metabolites and other small molecules. The genome encodes thousands of proteins but not all of them are expressed in every cell type; and expressed proteins are not active at all times. Such diversity of protein expression and function accounts for the level of biological intricacy seen in nature. Defining protein-protein interactions in protein complexes, and establishing the when, what and where of potential interactions, is therefore crucial to understanding the cellular function of any protein-especially those that have not been well studied by traditional molecular genetic approaches. We generated a large-scale resource of affinity-tagged expression-ready clones and used co-affinity purification combined with tandem mass-spectrometry to identify protein partners of nearly 5,000 Drosophila melanogaster proteins. The resulting protein complex "map" provided a blueprint of metazoan protein complex organization. Here we describe how the map has provided valuable insights into protein function in addition to generating hundreds of testable hypotheses. We also discuss recent technological advancements that will be critical in addressing the next generation of questions arising from the map.

  16. 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.

  17. Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

    Science.gov (United States)

    Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

    2016-10-06

    Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

  18. 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.

  19. PSAIA – Protein Structure and Interaction Analyzer

    Directory of Open Access Journals (Sweden)

    Vlahoviček Kristian

    2008-04-01

    Full Text Available Abstract Background PSAIA (Protein Structure and Interaction Analyzer was developed to compute geometric parameters for large sets of protein structures in order to predict and investigate protein-protein interaction sites. Results In addition to most relevant established algorithms, PSAIA offers a new method PIADA (Protein Interaction Atom Distance Algorithm for the determination of residue interaction pairs. We found that PIADA produced more satisfactory results than comparable algorithms implemented in PSAIA. Particular advantages of PSAIA include its capacity to combine different methods to detect the locations and types of interactions between residues and its ability, without any further automation steps, to handle large numbers of protein structures and complexes. Generally, the integration of a variety of methods enables PSAIA to offer easier automation of analysis and greater reliability of results. PSAIA can be used either via a graphical user interface or from the command-line. Results are generated in either tabular or XML format. Conclusion In a straightforward fashion and for large sets of protein structures, PSAIA enables the calculation of protein geometric parameters and the determination of location and type for protein-protein interaction sites. XML formatted output enables easy conversion of results to various formats suitable for statistic analysis. Results from smaller data sets demonstrated the influence of geometry on protein interaction sites. Comprehensive analysis of properties of large data sets lead to new information useful in the prediction of protein-protein interaction sites.

  20. Interactions between Surfactants in Solution and Electrospun Protein Fibers: Effects on Release Behavior and Fiber Properties

    DEFF Research Database (Denmark)

    Boutrup Stephansen, Karen; García-Díaz, María; Jessen, Flemming

    2016-01-01

    , and drug delivery. In the present study, we present a systematic investigation of how surfactants and proteins, as physiologically relevant components, interact with insulin-loaded fish sarcoplasmic protein (FSP) electrospun fibers (FSP-Ins fibers) in solution and thereby affect fiber properties...... such as accessible surface hydrophilicity, physical stability, and release characteristics of an encapsulated drug. Interactions between insulin-loaded protein fibers and five anionic surfactants (sodium taurocholate, sodium taurodeoxycholate, sodium glycocholate, sodium glycodeoxycholate, and sodium dodecyl sulfate......), a cationic surfactant (benzalkonium chloride), and a neutral surfactant (Triton X-100) were studied. The anionic surfactants increased the insulin release in a concentration-dependent manner, whereas the neutral surfactant had no significant effect on the release. Interestingly, only minute amounts...

  1. Scoring functions for protein-protein interactions.

    Science.gov (United States)

    Moal, Iain H; Moretti, Rocco; Baker, David; Fernández-Recio, Juan

    2013-12-01

    The computational evaluation of protein-protein interactions will play an important role in organising the wealth of data being generated by high-throughput initiatives. Here we discuss future applications, report recent developments and identify areas requiring further investigation. Many functions have been developed to quantify the structural and energetic properties of interacting proteins, finding use in interrelated challenges revolving around the relationship between sequence, structure and binding free energy. These include loop modelling, side-chain refinement, docking, multimer assembly, affinity prediction, affinity change upon mutation, hotspots location and interface design. Information derived from models optimised for one of these challenges can be used to benefit the others, and can be unified within the theoretical frameworks of multi-task learning and Pareto-optimal multi-objective learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

  2. Transcriptional robustness and protein interactions are associated in yeast

    Directory of Open Access Journals (Sweden)

    Conant Gavin C

    2011-05-01

    Full Text Available Abstract Background Robustness to insults, both external and internal, is a characteristic feature of life. One level of biological organization for which noise and robustness have been extensively studied is gene expression. Cells have a variety of mechanisms for buffering noise in gene expression, but it is not completely clear what rules govern whether or not a given gene uses such tools to maintain appropriate expression. Results Here, we show a general association between the degree to which yeast cells have evolved mechanisms to buffer changes in gene expression and whether they possess protein-protein interactions. We argue that this effect bears an affinity to epistasis, because yeast appears to have evolved regulatory mechanisms such that distant changes in gene copy number for a protein-protein interaction partner gene can alter a gene's expression. This association is not unexpected given recent work linking epistasis and the deleterious effects of changes in gene dosage (i.e., the dosage balance hypothesis. Using gene expression data from artificial aneuploid strains of bakers' yeast, we found that genes coding for proteins that physically interact with other proteins show less expression variation in response to aneuploidy than do other genes. This effect is even more pronounced for genes whose products interact with proteins encoded on aneuploid chromosomes. We further found that genes targeted by transcription factors encoded on aneuploid chromosomes were more likely to change in expression after aneuploidy. Conclusions We suggest that these observations can be best understood as resulting from the higher fitness cost of misexpression in epistatic genes and a commensurate greater regulatory control of them.

  3. Effect of fat content and homogenization under conventional or ultra-high-pressure conditions on interactions between proteins in rennet curds.

    Science.gov (United States)

    Zamora, A; Trujillo, A J; Armaforte, E; Waldron, D S; Kelly, A L

    2012-09-01

    The objective of this study was to investigate the influence of conventional and ultra-high-pressure homogenization on interactions between proteins within drained rennet curds. The effect of fat content of milk (0.0, 1.8, or 3.6%) and homogenization treatment on dissociation of proteins by different chemical agents was thus studied. Increasing the fat content of raw milk increased levels of unbound whey proteins and calcium-bonded caseins in curds; in contrast, hydrophobic interactions and hydrogen bonds were inhibited. Both homogenization treatments triggered the incorporation of unbound whey proteins in the curd, and of caseins through ionic bonds involving calcium salts. Conventional homogenization-pasteurization enhanced interactions between caseins through hydrogen bonds and hydrophobic interactions. In contrast, ultra-high-pressure homogenization impaired hydrogen bonding, led to the incorporation of both whey proteins and caseins through hydrophobic interactions and increased the amount of unbound caseins. Thus, both homogenization treatments provoked changes in the protein interactions within rennet curds; however, the nature of the changes depended on the homogenization conditions. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Kaare eTeilum

    2015-07-01

    Full Text Available 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 strong determinant for their function. This has fostered the notion that IDP’s bind with low affinity but high specificity. Here we have analyzed available detailed thermodynamic data for protein-protein interactions to put to the test if the thermodynamic profiles of IDP interactions differ from those 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.

  5. Multiplex single-molecule interaction profiling of DNA barcoded proteins

    Science.gov (United States)

    Gu, Liangcai; Li, Chao; Aach, John; Hill, David E.; Vidal, Marc; Church, George M.

    2014-01-01

    In contrast with advances in massively parallel DNA sequencing1, high-throughput protein analyses2-4 are often limited by ensemble measurements, individual analyte purification and hence compromised quality and cost-effectiveness. Single-molecule (SM) protein detection achieved using optical methods5 is limited by the number of spectrally nonoverlapping chromophores. Here, we introduce a single molecular interaction-sequencing (SMI-Seq) technology for parallel protein interaction profiling leveraging SM advantages. DNA barcodes are attached to proteins collectively via ribosome display6 or individually via enzymatic conjugation. Barcoded proteins are assayed en masse in aqueous solution and subsequently immobilized in a polyacrylamide (PAA) thin film to construct a random SM array, where barcoding DNAs are amplified into in situ polymerase colonies (polonies)7 and analyzed by DNA sequencing. This method allows precise quantification of various proteins with a theoretical maximum array density of over one million polonies per square millimeter. Furthermore, protein interactions can be measured based on the statistics of colocalized polonies arising from barcoding DNAs of interacting proteins. Two demanding applications, G-protein coupled receptor (GPCR) and antibody binding profiling, were demonstrated. SMI-Seq enables “library vs. library” screening in a one-pot assay, simultaneously interrogating molecular binding affinity and specificity. PMID:25252978

  6. Emergence of modularity and disassortativity in protein-protein interaction networks.

    Science.gov (United States)

    Wan, Xi; Cai, Shuiming; Zhou, Jin; Liu, Zengrong

    2010-12-01

    In this paper, we present a simple evolution model of protein-protein interaction networks by introducing a rule of small-preference duplication of a node, meaning that the probability of a node chosen to duplicate is inversely proportional to its degree, and subsequent divergence plus nonuniform heterodimerization based on some plausible mechanisms in biology. We show that our model cannot only reproduce scale-free connectivity and small-world pattern, but also exhibit hierarchical modularity and disassortativity. After comparing the features of our model with those of real protein-protein interaction networks, we believe that our model can provide relevant insights into the mechanism underlying the evolution of protein-protein interaction networks. © 2010 American Institute of Physics.

  7. Direct measurements of protein-stabilized gold nanoparticle interactions.

    Science.gov (United States)

    Eichmann, Shannon L; Bevan, Michael A

    2010-09-21

    We report integrated video and total internal reflection microscopy measurements of protein stabilized 110 nm Au nanoparticles confined in 280 nm gaps in physiological media. Measured potential energy profiles display quantitative agreement with Brownian dynamic simulations that include hydrodynamic interactions and camera exposure time and noise effects. Our results demonstrate agreement between measured nonspecific van der Waals and adsorbed protein interactions with theoretical potentials. Confined, lateral nanoparticle diffusivity measurements also display excellent agreement with predictions. These findings provide a basis to interrogate specific biomacromolecular interactions in similar experimental configurations and to design future improved measurement methods.

  8. Dendrimer-protein interactions versus dendrimer-based nanomedicine.

    Science.gov (United States)

    Shcharbin, Dzmitry; Shcharbina, Natallia; Dzmitruk, Volha; Pedziwiatr-Werbicka, Elzbieta; Ionov, Maksim; Mignani, Serge; de la Mata, F Javier; Gómez, Rafael; Muñoz-Fernández, Maria Angeles; Majoral, Jean-Pierre; Bryszewska, Maria

    2017-04-01

    Dendrimers are hyperbranched polymers belonging to the huge class of nanomedical devices. Their wide application in biology and medicine requires understanding of the fundamental mechanisms of their interactions with biological systems. Summarizing, electrostatic force plays the predominant role in dendrimer-protein interactions, especially with charged dendrimers. Other kinds of interactions have been proven, such as H-bonding, van der Waals forces, and even hydrophobic interactions. These interactions depend on the characteristics of both participants: flexibility and surface charge of a dendrimer, rigidity of protein structure and the localization of charged amino acids at its surface. pH and ionic strength of solutions can significantly modulate interactions. Ligands and cofactors attached to a protein can also change dendrimer-protein interactions. Binding of dendrimers to a protein can change its secondary structure, conformation, intramolecular mobility and functional activity. However, this strongly depends on rigidity versus flexibility of a protein's structure. In addition, the potential applications of dendrimers to nanomedicine are reviwed related to dendrimer-protein interactions. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. A coevolution analysis for identifying protein-protein interactions by Fourier transform

    Science.gov (United States)

    Yin, Changchuan; Yau, Stephen S. -T.

    2017-01-01

    Protein-protein interactions (PPIs) play key roles in life processes, such as signal transduction, transcription regulations, and immune response, etc. Identification of PPIs enables better understanding of the functional networks within a cell. Common experimental methods for identifying PPIs are time consuming and expensive. However, recent developments in computational approaches for inferring PPIs from protein sequences based on coevolution theory avoid these problems. In the coevolution theory model, interacted proteins may show coevolutionary mutations and have similar phylogenetic trees. The existing coevolution methods depend on multiple sequence alignments (MSA); however, the MSA-based coevolution methods often produce high false positive interactions. In this paper, we present a computational method using an alignment-free approach to accurately detect PPIs and reduce false positives. In the method, protein sequences are numerically represented by biochemical properties of amino acids, which reflect the structural and functional differences of proteins. Fourier transform is applied to the numerical representation of protein sequences to capture the dissimilarities of protein sequences in biophysical context. The method is assessed for predicting PPIs in Ebola virus. The results indicate strong coevolution between the protein pairs (NP-VP24, NP-VP30, NP-VP40, VP24-VP30, VP24-VP40, and VP30-VP40). The method is also validated for PPIs in influenza and E.coli genomes. Since our method can reduce false positive and increase the specificity of PPI prediction, it offers an effective tool to understand mechanisms of disease pathogens and find potential targets for drug design. The Python programs in this study are available to public at URL (https://github.com/cyinbox/PPI). PMID:28430779

  10. A coevolution analysis for identifying protein-protein interactions by Fourier transform.

    Directory of Open Access Journals (Sweden)

    Changchuan Yin

    Full Text Available Protein-protein interactions (PPIs play key roles in life processes, such as signal transduction, transcription regulations, and immune response, etc. Identification of PPIs enables better understanding of the functional networks within a cell. Common experimental methods for identifying PPIs are time consuming and expensive. However, recent developments in computational approaches for inferring PPIs from protein sequences based on coevolution theory avoid these problems. In the coevolution theory model, interacted proteins may show coevolutionary mutations and have similar phylogenetic trees. The existing coevolution methods depend on multiple sequence alignments (MSA; however, the MSA-based coevolution methods often produce high false positive interactions. In this paper, we present a computational method using an alignment-free approach to accurately detect PPIs and reduce false positives. In the method, protein sequences are numerically represented by biochemical properties of amino acids, which reflect the structural and functional differences of proteins. Fourier transform is applied to the numerical representation of protein sequences to capture the dissimilarities of protein sequences in biophysical context. The method is assessed for predicting PPIs in Ebola virus. The results indicate strong coevolution between the protein pairs (NP-VP24, NP-VP30, NP-VP40, VP24-VP30, VP24-VP40, and VP30-VP40. The method is also validated for PPIs in influenza and E.coli genomes. Since our method can reduce false positive and increase the specificity of PPI prediction, it offers an effective tool to understand mechanisms of disease pathogens and find potential targets for drug design. The Python programs in this study are available to public at URL (https://github.com/cyinbox/PPI.

  11. 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

    as a strong determinant for their function. This has fostered the notion that IDP's bind with low affinity but high specificity. Here we have analyzed available detailed thermodynamic data for protein-protein interactions to put to the test if the thermodynamic profiles of IDP interactions differ from those...... 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)....

  12. Effects of Genotype by Environment Interactions on Milk Yield, Energy Balance, and Protein Balance

    NARCIS (Netherlands)

    Beerda, B.; Ouweltjes, W.; Sebek, L.B.J.; Windig, J.J.; Veerkamp, R.F.

    2007-01-01

    Increases in genetic merit for milk yield are associated with increases in mobilization of body reserves. This study assessed the effects of genotype by environment (GxE) interactions on milk yield and energy and protein balances. Heifers (n = 100) with high or low genetic merit for milk yield were

  13. Identification of Protein-Protein Interactions with Glutathione-S-Transferase (GST) Fusion Proteins.

    Science.gov (United States)

    Einarson, Margret B; Pugacheva, Elena N; Orlinick, Jason R

    2007-08-01

    INTRODUCTIONGlutathione-S-transferase (GST) fusion proteins have had a wide range of applications since their introduction as tools for synthesis of recombinant proteins in bacteria. GST was originally selected as a fusion moiety because of several desirable properties. First and foremost, when expressed in bacteria alone, or as a fusion, GST is not sequestered in inclusion bodies (in contrast to previous fusion protein systems). Second, GST can be affinity-purified without denaturation because it binds to immobilized glutathione, which provides the basis for simple purification. Consequently, GST fusion proteins are routinely used for antibody generation and purification, protein-protein interaction studies, and biochemical analysis. This article describes the use of GST fusion proteins as probes for the identification of protein-protein interactions.

  14. Protein complex prediction based on k-connected subgraphs in protein interaction network

    OpenAIRE

    Habibi, Mahnaz; Eslahchi, Changiz; Wong, Limsoon

    2010-01-01

    Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on ...

  15. Development of a Model Protein Interaction Pair as a Benchmarking Tool for the Quantitative Analysis of 2-Site Protein-Protein Interactions.

    Science.gov (United States)

    Yamniuk, Aaron P; Newitt, John A; Doyle, Michael L; Arisaka, Fumio; Giannetti, Anthony M; Hensley, Preston; Myszka, David G; Schwarz, Fred P; Thomson, James A; Eisenstein, Edward

    2015-12-01

    A significant challenge in the molecular interaction field is to accurately determine the stoichiometry and stepwise binding affinity constants for macromolecules having >1 binding site. The mission of the Molecular Interactions Research Group (MIRG) of the Association of Biomolecular Resource Facilities (ABRF) is to show how biophysical technologies are used to quantitatively characterize molecular interactions, and to educate the ABRF members and scientific community on the utility and limitations of core technologies [such as biosensor, microcalorimetry, or analytic ultracentrifugation (AUC)]. In the present work, the MIRG has developed a robust model protein interaction pair consisting of a bivalent variant of the Bacillus amyloliquefaciens extracellular RNase barnase and a variant of its natural monovalent intracellular inhibitor protein barstar. It is demonstrated that this system can serve as a benchmarking tool for the quantitative analysis of 2-site protein-protein interactions. The protein interaction pair enables determination of precise binding constants for the barstar protein binding to 2 distinct sites on the bivalent barnase binding partner (termed binase), where the 2 binding sites were engineered to possess affinities that differed by 2 orders of magnitude. Multiple MIRG laboratories characterized the interaction using isothermal titration calorimetry (ITC), AUC, and surface plasmon resonance (SPR) methods to evaluate the feasibility of the system as a benchmarking model. Although general agreement was seen for the binding constants measured using solution-based ITC and AUC approaches, weaker affinity was seen for surface-based method SPR, with protein immobilization likely affecting affinity. An analysis of the results from multiple MIRG laboratories suggests that the bivalent barnase-barstar system is a suitable model for benchmarking new approaches for the quantitative characterization of complex biomolecular interactions.

  16. Building blocks for protein interaction devices

    Science.gov (United States)

    Grünberg, Raik; Ferrar, Tony S.; van der Sloot, Almer M.; Constante, Marco; Serrano, Luis

    2010-01-01

    Here, we propose a framework for the design of synthetic protein networks from modular protein–protein or protein–peptide interactions and provide a starter toolkit of protein building blocks. Our proof of concept experiments outline a general work flow for part–based protein systems engineering. We streamlined the iterative BioBrick cloning protocol and assembled 25 synthetic multidomain proteins each from seven standardized DNA fragments. A systematic screen revealed two main factors controlling protein expression in Escherichia coli: obstruction of translation initiation by mRNA secondary structure or toxicity of individual domains. Eventually, 13 proteins were purified for further characterization. Starting from well-established biotechnological tools, two general–purpose interaction input and two readout devices were built and characterized in vitro. Constitutive interaction input was achieved with a pair of synthetic leucine zippers. The second interaction was drug-controlled utilizing the rapamycin-induced binding of FRB(T2098L) to FKBP12. The interaction kinetics of both devices were analyzed by surface plasmon resonance. Readout was based on Förster resonance energy transfer between fluorescent proteins and was quantified for various combinations of input and output devices. Our results demonstrate the feasibility of parts-based protein synthetic biology. Additionally, we identify future challenges and limitations of modular design along with approaches to address them. PMID:20215443

  17. Effect of Astringent Stimuli on Salivary Protein Interactions Elucidated by Complementary Proteomics Approaches.

    Science.gov (United States)

    Delius, Judith; Médard, Guillaume; Kuster, Bernhard; Hofmann, Thomas

    2017-03-15

    The interaction of astringent substances with salivary proteins, which results in protein precipitation, is considered a key event in the molecular mechanism underlying the oral sensation of puckering astringency. As the chemical nature of orally active astringents is diverse and the knowledge of their interactions with salivary proteins rather fragmentary, human whole saliva samples were incubated with suprathreshold and isointensity solutions of the astringent polyphenol (-)-epigallocatechin gallate, the multivalent metal salt iron(III) sulfate, the amino-functionalized polysaccharide chitosan, and the basic protein lysozyme. After separation of the precipitated proteins, the proteins affected by the astringents were identified and relatively quantified for the first time by complementary bottom-up and top-down mass spectrometry-based proteomics approaches. Major salivary target proteins, which may be involved in astringency perception, are reported here for each astringent stimulus.

  18. Analysis of intraviral protein-protein interactions of the SARS coronavirus ORFeome.

    Directory of Open Access Journals (Sweden)

    Albrecht von Brunn

    2007-05-01

    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.

  19. Boosting compound-protein interaction prediction by deep learning.

    Science.gov (United States)

    Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

    2016-11-01

    The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. BioPlex Display: An Interactive Suite for Large-Scale AP-MS Protein-Protein Interaction Data.

    Science.gov (United States)

    Schweppe, Devin K; Huttlin, Edward L; Harper, J Wade; Gygi, Steven P

    2018-01-05

    The development of large-scale data sets requires a new means to display and disseminate research studies to large audiences. Knowledge of protein-protein interaction (PPI) networks has become a principle interest of many groups within the field of proteomics. At the confluence of technologies, such as cross-linking mass spectrometry, yeast two-hybrid, protein cofractionation, and affinity purification mass spectrometry (AP-MS), detection of PPIs can uncover novel biological inferences at a high-throughput. Thus new platforms to provide community access to large data sets are necessary. To this end, we have developed a web application that enables exploration and dissemination of the growing BioPlex interaction network. BioPlex is a large-scale interactome data set based on AP-MS of baits from the human ORFeome. The latest BioPlex data set release (BioPlex 2.0) contains 56 553 interactions from 5891 AP-MS experiments. To improve community access to this vast compendium of interactions, we developed BioPlex Display, which integrates individual protein querying, access to empirical data, and on-the-fly annotation of networks within an easy-to-use and mobile web application. BioPlex Display enables rapid acquisition of data from BioPlex and development of hypotheses based on protein interactions.

  1. Evidence for the interaction of the regulatory protein Ki-1/57 with p53 and its interacting proteins

    International Nuclear Information System (INIS)

    Nery, Flavia C.; Rui, Edmilson; Kuniyoshi, Tais M.; Kobarg, Joerg

    2006-01-01

    Ki-1/57 is a cytoplasmic and nuclear phospho-protein of 57 kDa and interacts with the adaptor protein RACK1, the transcription factor MEF2C, and the chromatin remodeling factor CHD3, suggesting that it might be involved in the regulation of transcription. Here, we describe yeast two-hybrid studies that identified a total of 11 proteins interacting with Ki-1/57, all of which interact or are functionally associated with p53 or other members of the p53 family of proteins. We further found that Ki-1/57 is able to interact with p53 itself in the yeast two-hybrid system when the interaction was tested directly. This interaction could be confirmed by pull down assays with purified proteins in vitro and by reciprocal co-immunoprecipitation assays from the human Hodgkin analogous lymphoma cell line L540. Furthermore, we found that the phosphorylation of p53 by PKC abolishes its interaction with Ki-1/57 in vitro

  2. Prediction of Effective Drug Combinations by Chemical Interaction, Protein Interaction and Target Enrichment of KEGG Pathways

    Directory of Open Access Journals (Sweden)

    Lei Chen

    2013-01-01

    Full Text Available Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1 chemical interaction between drugs, (2 protein interactions between drugs’ targets, and (3 target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

  3. Protein complex prediction based on k-connected subgraphs in protein interaction network

    Directory of Open Access Journals (Sweden)

    Habibi Mahnaz

    2010-09-01

    Full Text Available Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy, containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision. Conclusions Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.

  4. Molecular simulations of lipid-mediated protein-protein interactions

    NARCIS (Netherlands)

    de Meyer, F.J.M.; Venturoli, M.; Smit, B.

    2008-01-01

    Recent experimental results revealed that lipid-mediated interactions due to hydrophobic forces may be important in determining the protein topology after insertion in the membrane, in regulating the protein activity, in protein aggregation and in signal transduction. To gain insight into the

  5. Efficient extraction of protein-protein interactions from full-text articles.

    Science.gov (United States)

    Hakenberg, Jörg; Leaman, Robert; Vo, Nguyen Ha; Jonnalagadda, Siddhartha; Sullivan, Ryan; Miller, Christopher; Tari, Luis; Baral, Chitta; Gonzalez, Graciela

    2010-01-01

    Proteins and their interactions govern virtually all cellular processes, such as regulation, signaling, metabolism, and structure. Most experimental findings pertaining to such interactions are discussed in research papers, which, in turn, get curated by protein interaction databases. Authors, editors, and publishers benefit from efforts to alleviate the tasks of searching for relevant papers, evidence for physical interactions, and proper identifiers for each protein involved. The BioCreative II.5 community challenge addressed these tasks in a competition-style assessment to evaluate and compare different methodologies, to make aware of the increasing accuracy of automated methods, and to guide future implementations. In this paper, we present our approaches for protein-named entity recognition, including normalization, and for extraction of protein-protein interactions from full text. Our overall goal is to identify efficient individual components, and we compare various compositions to handle a single full-text article in between 10 seconds and 2 minutes. We propose strategies to transfer document-level annotations to the sentence-level, which allows for the creation of a more fine-grained training corpus; we use this corpus to automatically derive around 5,000 patterns. We rank sentences by relevance to the task of finding novel interactions with physical evidence, using a sentence classifier built from this training corpus. Heuristics for paraphrasing sentences help to further remove unnecessary information that might interfere with patterns, such as additional adjectives, clauses, or bracketed expressions. In BioCreative II.5, we achieved an f-score of 22 percent for finding protein interactions, and 43 percent for mapping proteins to UniProt IDs; disregarding species, f-scores are 30 percent and 55 percent, respectively. On average, our best-performing setup required around 2 minutes per full text. All data and pattern sets as well as Java classes that

  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. Signatures of pleiotropy, economy and convergent evolution in a domain-resolved map of human-virus protein-protein interaction networks.

    Science.gov (United States)

    Garamszegi, Sara; Franzosa, Eric A; Xia, Yu

    2013-01-01

    A central challenge in host-pathogen systems biology is the elucidation of general, systems-level principles that distinguish host-pathogen interactions from within-host interactions. Current analyses of host-pathogen and within-host protein-protein interaction networks are largely limited by their resolution, treating proteins as nodes and interactions as edges. Here, we construct a domain-resolved map of human-virus and within-human protein-protein interaction networks by annotating protein interactions with high-coverage, high-accuracy, domain-centric interaction mechanisms: (1) domain-domain interactions, in which a domain in one protein binds to a domain in a second protein, and (2) domain-motif interactions, in which a domain in one protein binds to a short, linear peptide motif in a second protein. Analysis of these domain-resolved networks reveals, for the first time, significant mechanistic differences between virus-human and within-human interactions at the resolution of single domains. While human proteins tend to compete with each other for domain binding sites by means of sequence similarity, viral proteins tend to compete with human proteins for domain binding sites in the absence of sequence similarity. Independent of their previously established preference for targeting human protein hubs, viral proteins also preferentially target human proteins containing linear motif-binding domains. Compared to human proteins, viral proteins participate in more domain-motif interactions, target more unique linear motif-binding domains per residue, and contain more unique linear motifs per residue. Together, these results suggest that viruses surmount genome size constraints by convergently evolving multiple short linear motifs in order to effectively mimic, hijack, and manipulate complex host processes for their survival. Our domain-resolved analyses reveal unique signatures of pleiotropy, economy, and convergent evolution in viral-host interactions that are

  8. An analysis pipeline for the inference of protein-protein interaction networks

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, Ronald C.; Singhal, Mudita; Daly, Don S.; Gilmore, Jason M.; Cannon, William R.; Domico, Kelly O.; White, Amanda M.; Auberry, Deanna L.; Auberry, Kenneth J.; Hooker, Brian S.; Hurst, G. B.; McDermott, Jason E.; McDonald, W. H.; Pelletier, Dale A.; Schmoyer, Denise A.; Wiley, H. S.

    2009-12-01

    An analysis pipeline has been created for deployment of a novel algorithm, the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro), for use in the reconstruction of protein-protein interaction networks. We have combined the Software Environment for BIological Network Inference (SEBINI), an interactive environment for the deployment and testing of network inference algorithms that use high-throughput data, and the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources, to allow interactions computed by BEPro to be stored, visualized, and further analyzed. Incorporating BEPro into SEBINI and automatically feeding the resulting inferred network into CABIN, we have created a structured workflow for protein-protein network inference and supplemental analysis from sets of mass spectrometry bait-prey experiment data. SEBINI demo site: https://www.emsl.pnl.gov /SEBINI/ Contact: ronald.taylor@pnl.gov. BEPro is available at http://www.pnl.gov/statistics/BEPro3/index.htm. Contact: ds.daly@pnl.gov. CABIN is available at http://www.sysbio.org/dataresources/cabin.stm. Contact: mudita.singhal@pnl.gov.

  9. Effects of non-covalent interactions with 5-O-caffeoylquinic acid (chlorogenic acid) on the heat denaturation and solubility of globular proteins

    NARCIS (Netherlands)

    Prigent, S.V.E.; Gruppen, H.; Visser, A.J.W.G.; Koningsveld, G.A. van; Jong, G.A.H. de; Voragen, A.G.J.

    2003-01-01

    The non-covalent interactions between the monomeric phenolic compound chlorogenic acid (5-CQA) and bovine serum albumin (BSA), lysozyme, and α-lactalbumin were characterized, and their effect on protein properties was examined. 5-CQA had a low affinity for all three proteins, and these interactions

  10. Identification of structural protein-protein interactions of herpes simplex virus type 1.

    Science.gov (United States)

    Lee, Jin H; Vittone, Valerio; Diefenbach, Eve; Cunningham, Anthony L; Diefenbach, Russell J

    2008-09-01

    In this study we have defined protein-protein interactions between the structural proteins of herpes simplex virus type 1 (HSV-1) using a LexA yeast two-hybrid system. The majority of the capsid, tegument and envelope proteins of HSV-1 were screened in a matrix approach. A total of 40 binary interactions were detected including 9 out of 10 previously identified tegument-tegument interactions (Vittone, V., Diefenbach, E., Triffett, D., Douglas, M.W., Cunningham, A.L., and Diefenbach, R.J., 2005. Determination of interactions between tegument proteins of herpes simplex virus type 1. J. Virol. 79, 9566-9571). A total of 12 interactions involving the capsid protein pUL35 (VP26) and 11 interactions involving the tegument protein pUL46 (VP11/12) were identified. The most significant novel interactions detected in this study, which are likely to play a role in viral assembly, include pUL35-pUL37 (capsid-tegument), pUL46-pUL37 (tegument-tegument) and pUL49 (VP22)-pUS9 (tegument-envelope). This information will provide further insights into the pathways of HSV-1 assembly and the identified interactions are potential targets for new antiviral drugs.

  11. A domain-based approach to predict protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Resat Haluk

    2007-06-01

    Full Text Available Abstract Background Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins. Results DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms. Conclusion We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed

  12. 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.

  13. Interaction of Proteins Identified in Human Thyroid Cells

    Science.gov (United States)

    Pietsch, Jessica; Riwaldt, Stefan; Bauer, Johann; Sickmann, Albert; Weber, Gerhard; Grosse, Jirka; Infanger, Manfred; Eilles, Christoph; Grimm, Daniela

    2013-01-01

    Influence of gravity forces on the regulation of protein expression by healthy and malignant thyroid cells was studied with the aim to identify protein interactions. Western blot analyses of a limited number of proteins suggested a time-dependent regulation of protein expression by simulated microgravity. After applying free flow isoelectric focusing and mass spectrometry to search for differently expressed proteins by thyroid cells exposed to simulated microgravity for three days, a considerable number of candidates for gravi-sensitive proteins were detected. In order to show how proteins sensitive to microgravity could directly influence other proteins, we investigated all polypeptide chains identified with Mascot scores above 100, looking for groups of interacting proteins. Hence, UniProtKB entry numbers of all detected proteins were entered into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and processed. The program indicated that we had detected various groups of interacting proteins in each of the three cell lines studied. The major groups of interacting proteins play a role in pathways of carbohydrate and protein metabolism, regulation of cell growth and cell membrane structuring. Analyzing these groups, networks of interaction could be established which show how a punctual influence of simulated microgravity may propagate via various members of interaction chains. PMID:23303277

  14. Interaction of Proteins Identified in Human Thyroid Cells

    Directory of Open Access Journals (Sweden)

    Jessica Pietsch

    2013-01-01

    Full Text Available Influence of gravity forces on the regulation of protein expression by healthy and malignant thyroid cells was studied with the aim to identify protein interactions. Western blot analyses of a limited number of proteins suggested a time-dependent regulation of protein expression by simulated microgravity. After applying free flow isoelectric focusing and mass spectrometry to search for differently expressed proteins by thyroid cells exposed to simulated microgravity for three days, a considerable number of candidates for gravi-sensitive proteins were detected. In order to show how proteins sensitive to microgravity could directly influence other proteins, we investigated all polypeptide chains identified with Mascot scores above 100, looking for groups of interacting proteins. Hence, UniProtKB entry numbers of all detected proteins were entered into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING and processed. The program indicated that we had detected various groups of interacting proteins in each of the three cell lines studied. The major groups of interacting proteins play a role in pathways of carbohydrate and protein metabolism, regulation of cell growth and cell membrane structuring. Analyzing these groups, networks of interaction could be established which show how a punctual influence of simulated microgravity may propagate via various members of interaction chains.

  15. Molecular imaging of drug-modulated protein-protein interactions in living subjects.

    Science.gov (United States)

    Paulmurugan, Ramasamy; Massoud, Tarik F; Huang, Jing; Gambhir, Sanjiv S

    2004-03-15

    Networks of protein interactions mediate cellular responses to environmental stimuli and direct the execution of many different cellular functional pathways. Small molecules synthesized within cells or recruited from the external environment mediate many protein interactions. The study of small molecule-mediated interactions of proteins is important to understand abnormal signal transduction pathways in cancer and in drug development and validation. In this study, we used split synthetic renilla luciferase (hRLUC) protein fragment-assisted complementation to evaluate heterodimerization of the human proteins FRB and FKBP12 mediated by the small molecule rapamycin. The concentration of rapamycin required for efficient dimerization and that of its competitive binder ascomycin required for dimerization inhibition were studied in cell lines. The system was dually modulated in cell culture at the transcription level, by controlling nuclear factor kappaB promoter/enhancer elements using tumor necrosis factor alpha, and at the interaction level, by controlling the concentration of the dimerizer rapamycin. The rapamycin-mediated dimerization of FRB and FKBP12 also was studied in living mice by locating, quantifying, and timing the hRLUC complementation-based bioluminescence imaging signal using a cooled charged coupled device camera. This split reporter system can be used to efficiently screen small molecule drugs that modulate protein-protein interactions and also to assess drugs in living animals. Both are essential steps in the preclinical evaluation of candidate pharmaceutical agents targeting protein-protein interactions, including signaling pathways in cancer cells.

  16. Intercellular protein-protein interactions at synapses.

    Science.gov (United States)

    Yang, Xiaofei; Hou, Dongmei; Jiang, Wei; Zhang, Chen

    2014-06-01

    Chemical synapses are asymmetric intercellular junctions through which neurons send nerve impulses to communicate with other neurons or excitable cells. The appropriate formation of synapses, both spatially and temporally, is essential for brain function and depends on the intercellular protein-protein interactions of cell adhesion molecules (CAMs) at synaptic clefts. The CAM proteins link pre- and post-synaptic sites, and play essential roles in promoting synapse formation and maturation, maintaining synapse number and type, accumulating neurotransmitter receptors and ion channels, controlling neuronal differentiation, and even regulating synaptic plasticity directly. Alteration of the interactions of CAMs leads to structural and functional impairments, which results in many neurological disorders, such as autism, Alzheimer's disease and schizophrenia. Therefore, it is crucial to understand the functions of CAMs during development and in the mature neural system, as well as in the pathogenesis of some neurological disorders. Here, we review the function of the major classes of CAMs, and how dysfunction of CAMs relates to several neurological disorders.

  17. 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.

  18. 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.

  19. Effectively identifying compound-protein interactions by learning from positive and unlabeled examples.

    Science.gov (United States)

    Cheng, Zhanzhan; Zhou, Shuigeng; Wang, Yang; Liu, Hui; Guan, Jihong; Chen, Yi-Ping Phoebe

    2016-05-18

    Prediction of compound-protein interactions (CPIs) is to find new compound-protein pairs where a protein is targeted by at least a compound, which is a crucial step in new drug design. Currently, a number of machine learning based methods have been developed to predict new CPIs in the literature. However, as there is not yet any publicly available set of validated negative CPIs, most existing machine learning based approaches use the unknown interactions (not validated CPIs) selected randomly as the negative examples to train classifiers for predicting new CPIs. Obviously, this is not quite reasonable and unavoidably impacts the CPI prediction performance. In this paper, we simply take the unknown CPIs as unlabeled examples, and propose a new method called PUCPI (the abbreviation of PU learning for Compound-Protein Interaction identification) that employs biased-SVM (Support Vector Machine) to predict CPIs using only positive and unlabeled examples. PU learning is a class of learning methods that leans from positive and unlabeled (PU) samples. To the best of our knowledge, this is the first work that identifies CPIs using only positive and unlabeled examples. We first collect known CPIs as positive examples and then randomly select compound-protein pairs not in the positive set as unlabeled examples. For each CPI/compound-protein pair, we extract protein domains as protein features and compound substructures as chemical features, then take the tensor product of the corresponding compound features and protein features as the feature vector of the CPI/compound-protein pair. After that, biased-SVM is employed to train classifiers on different datasets of CPIs and compound-protein pairs. Experiments over various datasets show that our method outperforms six typical classifiers, including random forest, L1- and L2-regularized logistic regression, naive Bayes, SVM and k-nearest neighbor (kNN), and three types of existing CPI prediction models. Source code, datasets and

  20. Exploiting conformational ensembles in modeling protein-protein interactions on the proteome scale

    Science.gov (United States)

    Kuzu, Guray; Gursoy, Attila; Nussinov, Ruth; Keskin, Ozlem

    2013-01-01

    Cellular functions are performed through protein-protein interactions; therefore, identification of these interactions is crucial for understanding biological processes. Recent studies suggest that knowledge-based approaches are more useful than ‘blind’ docking for modeling at large scales. However, a caveat of knowledge-based approaches is that they treat molecules as rigid structures. The Protein Data Bank (PDB) offers a wealth of conformations. Here, we exploited ensemble of the conformations in predictions by a knowledge-based method, PRISM. We tested ‘difficult’ cases in a docking-benchmark dataset, where the unbound and bound protein forms are structurally different. Considering alternative conformations for each protein, the percentage of successfully predicted interactions increased from ~26% to 66%, and 57% of the interactions were successfully predicted in an ‘unbiased’ scenario, in which data related to the bound forms were not utilized. If the appropriate conformation, or relevant template interface, is unavailable in the PDB, PRISM could not predict the interaction successfully. The pace of the growth of the PDB promises a rapid increase of ensemble conformations emphasizing the merit of such knowledge-based ensemble strategies for higher success rates in protein-protein interaction predictions on an interactome-scale. We constructed the structural network of ERK interacting proteins as a case study. PMID:23590674

  1. A feature-based approach to modeling protein-protein interaction hot spots.

    Science.gov (United States)

    Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

    2009-05-01

    Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to pi-related interactions, especially pi . . . pi interactions.

  2. Evidence of probabilistic behaviour in protein interaction networks

    Directory of Open Access Journals (Sweden)

    Reifman Jaques

    2008-01-01

    Full Text Available Abstract Background Data from high-throughput experiments of protein-protein interactions are commonly used to probe the nature of biological organization and extract functional relationships between sets of proteins. What has not been appreciated is that the underlying mechanisms involved in assembling these networks may exhibit considerable probabilistic behaviour. Results We find that the probability of an interaction between two proteins is generally proportional to the numerical product of their individual interacting partners, or degrees. The degree-weighted behaviour is manifested throughout the protein-protein interaction networks studied here, except for the high-degree, or hub, interaction areas. However, we find that the probabilities of interaction between the hubs are still high. Further evidence is provided by path length analyses, which show that these hubs are separated by very few links. Conclusion The results suggest that protein-protein interaction networks incorporate probabilistic elements that lead to scale-rich hierarchical architectures. These observations seem to be at odds with a biologically-guided organization. One interpretation of the findings is that we are witnessing the ability of proteins to indiscriminately bind rather than the protein-protein interactions that are actually utilized by the cell in biological processes. Therefore, the topological study of a degree-weighted network requires a more refined methodology to extract biological information about pathways, modules, or other inferred relationships among proteins.

  3. NatalieQ: A web server for protein-protein interaction network querying

    NARCIS (Netherlands)

    El-Kebir, M.; Brandt, B.W.; Heringa, J.; Klau, G.W.

    2014-01-01

    Background Molecular interactions need to be taken into account to adequately model the complex behavior of biological systems. These interactions are captured by various types of biological networks, such as metabolic, gene-regulatory, signal transduction and protein-protein interaction networks.

  4. 3DProIN: Protein-Protein Interaction Networks and Structure Visualization.

    Science.gov (United States)

    Li, Hui; Liu, Chunmei

    2014-06-14

    3DProIN is a computational tool to visualize protein-protein interaction networks in both two dimensional (2D) and three dimensional (3D) view. It models protein-protein interactions in a graph and explores the biologically relevant features of the tertiary structures of each protein in the network. Properties such as color, shape and name of each node (protein) of the network can be edited in either 2D or 3D views. 3DProIN is implemented using 3D Java and C programming languages. The internet crawl technique is also used to parse dynamically grasped protein interactions from protein data bank (PDB). It is a java applet component that is embedded in the web page and it can be used on different platforms including Linux, Mac and Window using web browsers such as Firefox, Internet Explorer, Chrome and Safari. It also was converted into a mac app and submitted to the App store as a free app. Mac users can also download the app from our website. 3DProIN is available for academic research at http://bicompute.appspot.com.

  5. Osmolyte Effects on Monoclonal Antibody Stability and Concentration-Dependent Protein Interactions with Water and Common Osmolytes.

    Science.gov (United States)

    Barnett, Gregory V; Razinkov, Vladimir I; Kerwin, Bruce A; Blake, Steven; Qi, Wei; Curtis, Robin A; Roberts, Christopher J

    2016-04-07

    Preferential interactions of proteins with water and osmolytes play a major role in controlling the thermodynamics of protein solutions. While changes in protein stability and shifts in phase behavior are often reported with the addition of osmolytes, the underlying protein interactions with water and/or osmolytes are typically inferred rather than measured directly. In this work, Kirkwood-Buff integrals for protein-water interactions (G12) and protein-osmolyte interactions (G23) were determined as a function of osmolyte concentration from density measurements of antistreptavidin immunoglobulin gamma-1 (AS-IgG1) in ternary aqueous solutions for a set of common neutral osmolytes: sucrose, trehalose, sorbitol, and poly(ethylene glycol) (PEG). For sucrose and PEG solutions, both protein-water and protein-osmolyte interactions depend strongly on osmolyte concentrations (c3). Strikingly, both osmolytes change from being preferentially excluded to preferentially accumulated with increasing c3. In contrast, sorbitol and trehalose solutions do not show large enough preferential interactions to be detected by densimetry. G12 and G23 values are used to estimate the transfer free energy for native AS-IgG1 (Δμ2N) and compared with existing models. AS-IgG1 unfolding via calorimetry shows a linear increase in midpoint temperatures as a function of trehalose, sucrose, and sorbitol concentrations, but the opposite behavior for PEG. Together, the results highlight limitations of existing models and common assumptions regarding the mechanisms of protein stabilization by osmolytes. Finally, PEG preferential interactions destabilize the Fab regions of AS-IgG1 more so than the CH2 or CH3 domains, illustrating preferential interactions can be specific to different protein domains.

  6. Cellulose membranes are more effective in holding back vital proteins and exhibit less interaction with plasma proteins during hemodialysis.

    Science.gov (United States)

    Pešić, Ivana; Müller, Gerhard A; Baumann, Cosima; Dihazi, Gry H; Koziolek, Michael J; Eltoweissy, Marwa; Bramlage, Carsten; Asif, Abdul R; Dihazi, Hassan

    2013-04-01

    The vast majority of patients with end-stage renal disease are treated with intermittent hemodialysis as a form of renal replacement therapy. To investigate the impact of hemodialysis membrane material on vital protein removal, dialysates from 26 well-characterized hemodialysis patients were collected 5 min after beginning, during 5h of treatment, as well as 5 min before ending of the dialysis sessions. Dialysis sessions were performed using either modified cellulose (n=12) (low-flux and high flux) or synthetic Polyflux (n=14) (low-flux and high-flux) dialyzer. Protein removal during hemodialysis was quantified and the dialysate proteome patterns were analyzed by 2-DE, MS and Western blot. There was a clear correlation between the type of membrane material and the amount of protein removed. Synthetic Polyflux membranes exhibit strong interaction with plasma proteins resulting in a significantly higher protein loss compared to modified cellulosic membrane. Moreover, the proteomics analysis showed that the removed proteins represented different molecular weight range and different functional groups: transport proteins, protease inhibitors, proteins with role in immune response and regulations, constructive proteins and as a part of HLA immune complex. The effect of this protein removal on hemodialysis treatment outcome should be investigated in further studies. Copyright © 2013 Elsevier B.V. All rights reserved.

  7. Effect of dataset selection on the topological interpretation of protein interaction networks

    Directory of Open Access Journals (Sweden)

    Robertson David L

    2005-09-01

    Full Text Available Abstract Background Studies of the yeast protein interaction network have revealed distinct correlations between the connectivity of individual proteins within the network and the average connectivity of their neighbours. Although a number of biological mechanisms have been proposed to account for these findings, the significance and influence of the specific datasets included in these studies has not been appreciated adequately. Results We show how the use of different interaction data sets, such as those resulting from high-throughput or small-scale studies, and different modelling methodologies for the derivation pair-wise protein interactions, can dramatically change the topology of these networks. Furthermore, we show that some of the previously reported features identified in these networks may simply be the result of experimental or methodological errors and biases. Conclusion When performing network-based studies, it is essential to define what is meant by the term "interaction" and this must be taken into account when interpreting the topologies of the networks generated. Consideration must be given to the type of data included and appropriate controls that take into account the idiosyncrasies of the data must be selected

  8. Bioluminescence resonance energy transfer system for measuring dynamic protein-protein interactions in bacteria.

    Science.gov (United States)

    Cui, Boyu; Wang, Yao; Song, Yunhong; Wang, Tietao; Li, Changfu; Wei, Yahong; Luo, Zhao-Qing; Shen, Xihui

    2014-05-20

    Protein-protein interactions are important for virtually every biological process, and a number of elegant approaches have been designed to detect and evaluate such interactions. However, few of these methods allow the detection of dynamic and real-time protein-protein interactions in bacteria. Here we describe a bioluminescence resonance energy transfer (BRET) system based on the bacterial luciferase LuxAB. We found that enhanced yellow fluorescent protein (eYFP) accepts the emission from LuxAB and emits yellow fluorescence. Importantly, BRET occurred when LuxAB and eYFP were fused, respectively, to the interacting protein pair FlgM and FliA. Furthermore, we observed sirolimus (i.e., rapamycin)-inducible interactions between FRB and FKBP12 and a dose-dependent abolishment of such interactions by FK506, the ligand of FKBP12. Using this system, we showed that osmotic stress or low pH efficiently induced multimerization of the regulatory protein OmpR and that the multimerization induced by low pH can be reversed by a neutralizing agent, further indicating the usefulness of this system in the measurement of dynamic interactions. This method can be adapted to analyze dynamic protein-protein interactions and the importance of such interactions in bacterial processes such as development and pathogenicity. Real-time measurement of protein-protein interactions in prokaryotes is highly desirable for determining the roles of protein complex in the development or virulence of bacteria, but methods that allow such measurement are not available. Here we describe the development of a bioluminescence resonance energy transfer (BRET) technology that meets this need. The use of endogenous excitation light in this strategy circumvents the requirement for the sophisticated instrument demanded by standard fluorescence resonance energy transfer (FRET). Furthermore, because the LuxAB substrate decanal is membrane permeable, the assay can be performed without lysing the bacterial cells

  9. Full Data of Yeast Interacting Proteins Database (Original Version) - Yeast Interacting Proteins Database | LSDB Archive [Life Science Database Archive metadata

    Lifescience Database Archive (English)

    Full Text Available List Contact us Yeast Interacting Proteins Database Full Data of Yeast Interacting Proteins Database (Origin...al Version) Data detail Data name Full Data of Yeast Interacting Proteins Database (Original Version) DOI 10....18908/lsdba.nbdc00742-004 Description of data contents The entire data in the Yeast Interacting Proteins Database...eir interactions are required. Several sources including YPD (Yeast Proteome Database, Costanzo, M. C., Hoga...ematic name in the SGD (Saccharomyces Genome Database; http://www.yeastgenome.org /). Bait gene name The gen

  10. From nonspecific DNA-protein encounter complexes to the prediction of DNA-protein interactions.

    Directory of Open Access Journals (Sweden)

    Mu Gao

    2009-03-01

    Full Text Available DNA-protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA-protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA-protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA-protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA-protein interaction modes exhibit some similarity to specific DNA-protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Calpha deviation from native is up to 5 A from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA-protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein.

  11. Yeast Interacting Proteins Database: YGL127C, YOR047C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ith protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt15p; acts as a regula...rotein involved in control of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sensors

  12. 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.

  13. The Development of Protein Microarrays and Their Applications in DNA-Protein and Protein-Protein Interaction Analyses of Arabidopsis Transcription Factors

    Science.gov (United States)

    Gong, Wei; He, Kun; Covington, Mike; Dinesh-Kumar, S. P.; Snyder, Michael; Harmer, Stacey L.; Zhu, Yu-Xian; Deng, Xing Wang

    2009-01-01

    We used our collection of Arabidopsis transcription factor (TF) ORFeome clones to construct protein microarrays containing as many as 802 TF proteins. These protein microarrays were used for both protein-DNA and protein-protein interaction analyses. For protein-DNA interaction studies, we examined AP2/ERF family TFs and their cognate cis-elements. By careful comparison of the DNA-binding specificity of 13 TFs on the protein microarray with previous non-microarray data, we showed that protein microarrays provide an efficient and high throughput tool for genome-wide analysis of TF-DNA interactions. This microarray protein-DNA interaction analysis allowed us to derive a comprehensive view of DNA-binding profiles of AP2/ERF family proteins in Arabidopsis. It also revealed four TFs that bound the EE (evening element) and had the expected phased gene expression under clock-regulation, thus providing a basis for further functional analysis of their roles in clock regulation of gene expression. We also developed procedures for detecting protein interactions using this TF protein microarray and discovered four novel partners that interact with HY5, which can be validated by yeast two-hybrid assays. Thus, plant TF protein microarrays offer an attractive high-throughput alternative to traditional techniques for TF functional characterization on a global scale. PMID:19802365

  14. Signatures of pleiotropy, economy and convergent evolution in a domain-resolved map of human-virus protein-protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Sara Garamszegi

    Full Text Available A central challenge in host-pathogen systems biology is the elucidation of general, systems-level principles that distinguish host-pathogen interactions from within-host interactions. Current analyses of host-pathogen and within-host protein-protein interaction networks are largely limited by their resolution, treating proteins as nodes and interactions as edges. Here, we construct a domain-resolved map of human-virus and within-human protein-protein interaction networks by annotating protein interactions with high-coverage, high-accuracy, domain-centric interaction mechanisms: (1 domain-domain interactions, in which a domain in one protein binds to a domain in a second protein, and (2 domain-motif interactions, in which a domain in one protein binds to a short, linear peptide motif in a second protein. Analysis of these domain-resolved networks reveals, for the first time, significant mechanistic differences between virus-human and within-human interactions at the resolution of single domains. While human proteins tend to compete with each other for domain binding sites by means of sequence similarity, viral proteins tend to compete with human proteins for domain binding sites in the absence of sequence similarity. Independent of their previously established preference for targeting human protein hubs, viral proteins also preferentially target human proteins containing linear motif-binding domains. Compared to human proteins, viral proteins participate in more domain-motif interactions, target more unique linear motif-binding domains per residue, and contain more unique linear motifs per residue. Together, these results suggest that viruses surmount genome size constraints by convergently evolving multiple short linear motifs in order to effectively mimic, hijack, and manipulate complex host processes for their survival. Our domain-resolved analyses reveal unique signatures of pleiotropy, economy, and convergent evolution in viral

  15. Yeast Interacting Proteins Database: YOR047C, YKL038W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available racts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt15p; acts as a...Bait description Protein involved in control of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose senso...rs Snf3p and Rgt2p, and TATA-binding protein Spt15p; acts as a regulator of the tra

  16. Yeast Interacting Proteins Database: YFR049W, YOR047C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt15p; acts as a regulator... (0) YOR047C STD1 Protein involved in control of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sens...ors Snf3p and Rgt2p, and TATA-binding protein Spt15p; ac

  17. Yeast Interacting Proteins Database: YGL145W, YNL258C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ripheral membrane protein required for Golgi-to-ER retrograde traffic; component ... membrane protein required for Golgi-to-ER retrograde traffic; component of the ER target site that interact

  18. Interaction between -Synuclein and Other Proteins in Neurodegenerative Disorders

    Directory of Open Access Journals (Sweden)

    Kurt A. Jellinger

    2011-01-01

    Full Text Available Protein aggregation is a common characteristic of many neurodegenerative disorders, and the interaction between pathological/toxic proteins to cause neurodegeneration is a hot topic of current neuroscience research. Despite clinical, genetic, and experimental differences, evidence increasingly indicates considerable overlap between synucleinopathies and tauopathies or other protein-misfolding diseases. Inclusions, characteristics of these disorders, also occurring in other neurodegenerative diseases, suggest interactions of pathological proteins engaging common downstream pathways. Novel findings that have shifted our understanding in the role of pathologic proteins in the pathogenesis of Parkinson and Alzheimer diseases have confirmed correlations/overlaps between these and other neurodegenerative disorders. The synergistic effects of α-synuclein, hyperphosphorylated tau, amyloid-β, and other pathologic proteins, and the underlying molecular pathogenic mechanisms, including induction and spread of protein aggregates, are critically reviewed, suggesting a dualism or triad of neurodegeneration in protein-misfolding disorders, although the etiology of most of these processes is still mysterious.

  19. Interactions of glycine betaine with proteins: insights from volume and compressibility measurements.

    Science.gov (United States)

    Shek, Yuen Lai; Chalikian, Tigran V

    2013-01-29

    We report the first application of volume and compressibility measurements to characterization of interactions between cosolvents (osmolytes) and globular proteins. Specifically, we measure the partial molar volumes and adiabatic compressibilities of cytochrome c, ribonuclease A, lysozyme, and ovalbumin in aqueous solutions of the stabilizing osmolyte glycine betaine (GB) at concentrations between 0 and 4 M. The fact that globular proteins do not undergo any conformational transitions in the presence of GB provides an opportunity to study the interactions of GB with proteins in their native states within the entire range of experimentally accessible GB concentrations. We analyze our resulting volumetric data within the framework of a statistical thermodynamic model in which each instance of GB interaction with a protein is viewed as a binding reaction that is accompanied by release of four water molecules. From this analysis, we calculate the association constants, k, as well as changes in volume, ΔV(0), and adiabatic compressibility, ΔK(S0), accompanying each GB-protein association event in an ideal solution. By comparing these parameters with similar characteristics determined for low-molecular weight analogues of proteins, we conclude that there are no significant cooperative effects involved in interactions of GB with any of the proteins studied in this work. We also evaluate the free energies of direct GB-protein interactions. The energetic properties of GB-protein association appear to scale with the size of the protein. For all proteins, the highly favorable change in free energy associated with direct protein-cosolvent interactions is nearly compensated by an unfavorable free energy of cavity formation (excluded volume effect), yielding a modestly unfavorable free energy for the transfer of a protein from water to a GB/water mixture.

  20. Dough and hearth bread characteristics influenced by protein composition, protein content, DATEM, and their interactions

    NARCIS (Netherlands)

    Aamodt, A.; Magnus, E.M.; Hollung, K.; Uhlen, A.K.; Færgestad, E.M.

    2005-01-01

    The effects of protein composition, protein content, diacetyl tartaric acid ester of monoglycerides (DATEM), and their interaction on dough and bread characteristics were studied by small- and pilot-scale hearth bread baking, dough rheological testing using the Kieffer extensibility rig, and size

  1. 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.

  2. Analysis of Protein-Membrane Interactions

    DEFF Research Database (Denmark)

    Kemmer, Gerdi Christine

    Cellular membranes are complex structures, consisting of hundreds of different lipids and proteins. These membranes act as barriers between distinct environments, constituting hot spots for many essential functions of the cell, including signaling, energy conversion, and transport. These functions....... Discovered interactions were then probed on the level of the membrane using liposome-based assays. In the second part, a transmembrane protein was investigated. Assays to probe activity of the plasma membrane ATPase (Arabidopsis thaliana H+ -ATPase isoform 2 (AHA2)) in single liposomes using both giant...... are implemented by soluble proteins reversibly binding to, as well as by integral membrane proteins embedded in, cellular membranes. The activity and interaction of these proteins is furthermore modulated by the lipids of the membrane. Here, liposomes were used as model membrane systems to investigate...

  3. A selection that reports on protein-protein interactions within a thermophilic bacterium.

    Science.gov (United States)

    Nguyen, Peter Q; Silberg, Jonathan J

    2010-07-01

    Many proteins can be split into fragments that exhibit enhanced function upon fusion to interacting proteins. While this strategy has been widely used to create protein-fragment complementation assays (PCAs) for discovering protein-protein interactions within mesophilic organisms, similar assays have not yet been developed for studying natural and engineered protein complexes at the temperatures where thermophilic microbes grow. We describe the development of a selection for protein-protein interactions within Thermus thermophilus that is based upon growth complementation by fragments of Thermotoga neapolitana adenylate kinase (AK(Tn)). Complementation studies with an engineered thermophile (PQN1) that is not viable above 75 degrees C because its adk gene has been replaced by a Geobacillus stearothermophilus ortholog revealed that growth could be restored at 78 degrees C by a vector that coexpresses polypeptides corresponding to residues 1-79 and 80-220 of AK(Tn). In contrast, PQN1 growth was not complemented by AK(Tn) fragments harboring a C156A mutation within the zinc-binding tetracysteine motif unless these fragments were fused to Thermotoga maritima chemotaxis proteins that heterodimerize (CheA and CheY) or homodimerize (CheX). This enhanced complementation is interpreted as arising from chemotaxis protein-protein interactions, since AK(Tn)-C156A fragments having only one polypeptide fused to a chemotaxis protein did not complement PQN1 to the same extent. This selection increases the maximum temperature where a PCA can be used to engineer thermostable protein complexes and to map protein-protein interactions.

  4. Studying RNA-protein interactions in vivo by RNA immunoprecipitation

    DEFF Research Database (Denmark)

    Selth, Luke A; Close, Pierre; Svejstrup, Jesper Q

    2011-01-01

    and have significant effects on gene expression. RNA immunoprecipitation (RIP) is a powerful technique used to detect direct and indirect interactions between individual proteins and specific RNA molecules in vivo. Here, we describe RIP methods for both yeast and mammalian cells.......The crucial roles played by RNA-binding proteins in all aspects of RNA metabolism, particularly in the regulation of transcription, have become increasingly evident. Moreover, other factors that do not directly interact with RNA molecules can nevertheless function proximally to RNA polymerases...

  5. A method for investigating protein-protein interactions related to Salmonella typhimurium pathogenesis

    Energy Technology Data Exchange (ETDEWEB)

    Chowdhury, Saiful M. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Shi, Liang [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Yoon, Hyunjin [Dartmouth College, Hanover, NH (United States); Ansong, Charles [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Rommereim, Leah M. [Dartmouth College, Hanover, NH (United States); Norbeck, Angela D. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Auberry, Kenneth J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Moore, R. J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Adkins, Joshua N. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Heffron, Fred [Oregon Health and Science Univ., Portland, OR (United States); Smith, Richard D. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2009-02-10

    We successfully modified an existing method to investigate protein-protein interactions in the pathogenic bacterium Salmonella typhimurium (STM). This method includes i) addition of a histidine-biotin-histidine tag to the bait proteins via recombinant DNA techniques; ii) in vivo cross-linking with formaldehyde; iii) tandem affinity purification of bait proteins under fully denaturing conditions; and iv) identification of the proteins cross-linked to the bait proteins by liquid-chromatography in conjunction with tandem mass-spectrometry. In vivo cross-linking stabilized protein interactions permitted the subsequent two-step purification step conducted under denaturing conditions. The two-step purification greatly reduced nonspecific binding of non-cross-linked proteins to bait proteins. Two different negative controls were employed to reduce false-positive identification. In an initial demonstration of this approach, we tagged three selected STM proteins- HimD, PduB and PhoP- with known binding partners that ranged from stable (e.g., HimD) to transient (i.e., PhoP). Distinct sets of interacting proteins were identified with each bait protein, including the known binding partners such as HimA for HimD, as well as anticipated and unexpected binding partners. Our results suggest that novel protein-protein interactions may be critical to pathogenesis by Salmonella typhimurium. .

  6. Design principles for cancer therapy guided by changes in complexity of protein-protein interaction networks.

    Science.gov (United States)

    Benzekry, Sebastian; Tuszynski, Jack A; Rietman, Edward A; Lakka Klement, Giannoula

    2015-05-28

    The ever-increasing expanse of online bioinformatics data is enabling new ways to, not only explore the visualization of these data, but also to apply novel mathematical methods to extract meaningful information for clinically relevant analysis of pathways and treatment decisions. One of the methods used for computing topological characteristics of a space at different spatial resolutions is persistent homology. This concept can also be applied to network theory, and more specifically to protein-protein interaction networks, where the number of rings in an individual cancer network represents a measure of complexity. We observed a linear correlation of R = -0.55 between persistent homology and 5-year survival of patients with a variety of cancers. This relationship was used to predict the proteins within a protein-protein interaction network with the most impact on cancer progression. By re-computing the persistent homology after computationally removing an individual node (protein) from the protein-protein interaction network, we were able to evaluate whether such an inhibition would lead to improvement in patient survival. The power of this approach lied in its ability to identify the effects of inhibition of multiple proteins and in the ability to expose whether the effect of a single inhibition may be amplified by inhibition of other proteins. More importantly, we illustrate specific examples of persistent homology calculations, which correctly predict the survival benefit observed effects in clinical trials using inhibitors of the identified molecular target. We propose that computational approaches such as persistent homology may be used in the future for selection of molecular therapies in clinic. The technique uses a mathematical algorithm to evaluate the node (protein) whose inhibition has the highest potential to reduce network complexity. The greater the drop in persistent homology, the greater reduction in network complexity, and thus a larger

  7. 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/.

  8. Yeast Interacting Proteins Database: YOR358W, YOR047C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt15p; act...rotein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt15p; acts as a regulator o

  9. 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.

  10. Transcription Factor Functional Protein-Protein Interactions in Plant Defense Responses

    Directory of Open Access Journals (Sweden)

    Murilo S. Alves

    2014-03-01

    Full Text Available Responses to biotic stress in plants lead to dramatic reprogramming of gene expression, favoring stress responses at the expense of normal cellular functions. Transcription factors are master regulators of gene expression at the transcriptional level, and controlling the activity of these factors alters the transcriptome of the plant, leading to metabolic and phenotypic changes in response to stress. The functional analysis of interactions between transcription factors and other proteins is very important for elucidating the role of these transcriptional regulators in different signaling cascades. In this review, we present an overview of protein-protein interactions for the six major families of transcription factors involved in plant defense: basic leucine zipper containing domain proteins (bZIP, amino-acid sequence WRKYGQK (WRKY, myelocytomatosis related proteins (MYC, myeloblastosis related proteins (MYB, APETALA2/ ETHYLENE-RESPONSIVE ELEMENT BINDING FACTORS (AP2/EREBP and no apical meristem (NAM, Arabidopsis transcription activation factor (ATAF, and cup-shaped cotyledon (CUC (NAC. We describe the interaction partners of these transcription factors as molecular responses during pathogen attack and the key components of signal transduction pathways that take place during plant defense responses. These interactions determine the activation or repression of response pathways and are crucial to understanding the regulatory networks that modulate plant defense responses.

  11. Prediction of the Ebola Virus Infection Related Human Genes Using Protein-Protein Interaction Network.

    Science.gov (United States)

    Cao, HuanHuan; Zhang, YuHang; Zhao, Jia; Zhu, Liucun; Wang, Yi; Li, JiaRui; Feng, Yuan-Ming; Zhang, Ning

    2017-01-01

    Ebola hemorrhagic fever (EHF) is caused by Ebola virus (EBOV). It is reported that human could be infected by EBOV with a high fatality rate. However, association factors between EBOV and host still tend to be ambiguous. According to the "guilt by association" (GBA) principle, proteins interacting with each other are very likely to function similarly or the same. Based on this assumption, we tried to obtain EBOV infection-related human genes in a protein-protein interaction network using Dijkstra algorithm. We hope it could contribute to the discovery of novel effective treatments. Finally, 15 genes were selected as potential EBOV infection-related human genes. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  12. 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.

  13. Interaction of bovine gallbladder mucin and calcium-binding protein: effects on calcium phosphate precipitation.

    Science.gov (United States)

    Afdhal, N H; Ostrow, J D; Koehler, R; Niu, N; Groen, A K; Veis, A; Nunes, D P; Offner, G D

    1995-11-01

    Gallstones consist of calcium salts and cholesterol crystals, arrayed on a matrix of gallbladder mucin (GBM), and regulatory proteins like calcium-binding protein (CBP). To determine if interactions between CBP and GBM follow a biomineralization scheme, their mutual binding and effects on CaHPO4 precipitation were studied. Binding of CBP to GBM was assessed by inhibition of the fluorescence of the complex of GBM with bis-1,8-anilinonaphthalene sulfonic acid (bis-ANS). The effects of the proteins on precipitation of CaHPO4 were assessed by nephelometry and gravimetry. Precipitates were analyzed for calcium, phosphate, and protein. CBP and bis-ANS competitively displaced each other from 30 binding sites on mucin, with a 1:1 stoichiometry and similar affinity. The rate of precipitation of CaHPO4 was retarded by mucin and CBP. Precipitate mass was unaffected by GBM alone but decreased with the addition of CBP. Complexing CBP with GBM abolished or moderated this latter effect, altered precipitate morphology, and changed the stoichiometric ratios of Ca to PO4 in the precipitates from 1:1 to 3:2. Mucin and CBP were incorporated into the precipitates. These studies suggest that the formation of calcium-containing gallstones is a biomineralization process regulated by both GBM and CBP.

  14. Data management of protein interaction networks

    CERN Document Server

    Cannataro, Mario

    2012-01-01

    Interactomics: a complete survey from data generation to knowledge extraction With the increasing use of high-throughput experimental assays, more and more protein interaction databases are becoming available. As a result, computational analysis of protein-to-protein interaction (PPI) data and networks, now known as interactomics, has become an essential tool to determine functionally associated proteins. From wet lab technologies to data management to knowledge extraction, this timely book guides readers through the new science of interactomics, giving them the tools needed to: Generate

  15. Feature-Based and String-Based Models for Predicting RNA-Protein Interaction

    Directory of Open Access Journals (Sweden)

    Donald Adjeroh

    2018-03-01

    Full Text Available In this work, we study two approaches for the problem of RNA-Protein Interaction (RPI. In the first approach, we use a feature-based technique by combining extracted features from both sequences and secondary structures. The feature-based approach enhanced the prediction accuracy as it included much more available information about the RNA-protein pairs. In the second approach, we apply search algorithms and data structures to extract effective string patterns for prediction of RPI, using both sequence information (protein and RNA sequences, and structure information (protein and RNA secondary structures. This led to different string-based models for predicting interacting RNA-protein pairs. We show results that demonstrate the effectiveness of the proposed approaches, including comparative results against leading state-of-the-art methods.

  16. Identification of Essential Proteins Based on a New Combination of Local Interaction Density and Protein Complexes.

    Directory of Open Access Journals (Sweden)

    Jiawei Luo

    Full Text Available Computational approaches aided by computer science have been used to predict essential proteins and are faster than expensive, time-consuming, laborious experimental approaches. However, the performance of such approaches is still poor, making practical applications of computational approaches difficult in some fields. Hence, the development of more suitable and efficient computing methods is necessary for identification of essential proteins.In this paper, we propose a new method for predicting essential proteins in a protein interaction network, local interaction density combined with protein complexes (LIDC, based on statistical analyses of essential proteins and protein complexes. First, we introduce a new local topological centrality, local interaction density (LID, of the yeast PPI network; second, we discuss a new integration strategy for multiple bioinformatics. The LIDC method was then developed through a combination of LID and protein complex information based on our new integration strategy. The purpose of LIDC is discovery of important features of essential proteins with their neighbors in real protein complexes, thereby improving the efficiency of identification.Experimental results based on three different PPI(protein-protein interaction networks of Saccharomyces cerevisiae and Escherichia coli showed that LIDC outperformed classical topological centrality measures and some recent combinational methods. Moreover, when predicting MIPS datasets, the better improvement of performance obtained by LIDC is over all nine reference methods (i.e., DC, BC, NC, LID, PeC, CoEWC, WDC, ION, and UC.LIDC is more effective for the prediction of essential proteins than other recently developed methods.

  17. Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization.

    Science.gov (United States)

    Wang, Hua; Huang, Heng; Ding, Chris; Nie, Feiping

    2013-04-01

    Protein interactions are central to all the biological processes and structural scaffolds in living organisms, because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Several high-throughput methods, for example, yeast two-hybrid system and mass spectrometry method, can help determine protein interactions, which, however, suffer from high false-positive rates. Moreover, many protein interactions predicted by one method are not supported by another. Therefore, computational methods are necessary and crucial to complete the interactome expeditiously. In this work, we formulate the problem of predicting protein interactions from a new mathematical perspective--sparse matrix completion, and propose a novel nonnegative matrix factorization (NMF)-based matrix completion approach to predict new protein interactions from existing protein interaction networks. Through using manifold regularization, we further develop our method to integrate different biological data sources, such as protein sequences, gene expressions, protein structure information, etc. Extensive experimental results on four species, Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, and Caenorhabditis elegans, have shown that our new methods outperform related state-of-the-art protein interaction prediction methods.

  18. Rationalizing the chemical space of protein-protein interaction inhibitors.

    Science.gov (United States)

    Sperandio, Olivier; Reynès, Christelle H; Camproux, Anne-Claude; Villoutreix, Bruno O

    2010-03-01

    Protein-protein interactions (PPIs) are one of the next major classes of therapeutic targets, although they are too intricate to tackle with standard approaches. This is due, in part, to the inadequacy of today's chemical libraries. However, the emergence of a growing number of experimentally validated inhibitors of PPIs (i-PPIs) allows drug designers to use chemoinformatics and machine learning technologies to unravel the nature of the chemical space covered by the reported compounds. Key characteristics of i-PPIs can then be revealed and highlight the importance of specific shapes and/or aromatic bonds, enabling the design of i-PPI-enriched focused libraries and, therefore, of cost-effective screening strategies. 2009 Elsevier Ltd. All rights reserved.

  19. PREFACE: Physics approaches to protein interactions and gene regulation Physics approaches to protein interactions and gene regulation

    Science.gov (United States)

    Nussinov, Ruth; Panchenko, Anna R.; Przytycka, Teresa

    2011-06-01

    Physics approaches focus on uncovering, modeling and quantitating the general principles governing the micro and macro universe. This has always been an important component of biological research, however recent advances in experimental techniques and the accumulation of unprecedented genome-scale experimental data produced by these novel technologies now allow for addressing fundamental questions on a large scale. These relate to molecular interactions, principles of bimolecular recognition, and mechanisms of signal propagation. The functioning of a cell requires a variety of intermolecular interactions including protein-protein, protein-DNA, protein-RNA, hormones, peptides, small molecules, lipids and more. Biomolecules work together to provide specific functions and perturbations in intermolecular communication channels often lead to cellular malfunction and disease. A full understanding of the interactome requires an in-depth grasp of the biophysical principles underlying individual interactions as well as their organization in cellular networks. Phenomena can be described at different levels of abstraction. Computational and systems biology strive to model cellular processes by integrating and analyzing complex data from multiple experimental sources using interdisciplinary tools. As a result, both the causal relationships between the variables and the general features of the system can be discovered, which even without knowing the details of the underlying mechanisms allow for putting forth hypotheses and predicting the behavior of the systems in response to perturbation. And here lies the strength of in silico models which provide control and predictive power. At the same time, the complexity of individual elements and molecules can be addressed by the fields of molecular biophysics, physical biology and structural biology, which focus on the underlying physico-chemical principles and may explain the molecular mechanisms of cellular function. In this issue

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

    Directory of Open Access Journals (Sweden)

    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.

  1. 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.

  2. Interaction of milk whey protein with common phenolic acids

    Science.gov (United States)

    Zhang, Hao; Yu, Dandan; Sun, Jing; Guo, Huiyuan; Ding, Qingbo; Liu, Ruihai; Ren, Fazheng

    2014-01-01

    Phenolics-rich foods such as fruit juices and coffee are often consumed with milk. In this study, the interactions of α-lactalbumin and β-lactoglobulin with the phenolic acids (chlorogenic acid, caffeic acid, ferulic acid, and coumalic acid) were examined. Fluorescence, CD, and FTIR spectroscopies were used to analyze the binding modes, binding constants, and the effects of complexation on the conformation of whey protein. The results showed that binding constants of each whey protein-phenolic acid interaction ranged from 4 × 105 to 7 × 106 M-n and the number of binding sites n ranged from 1.28 ± 0.13 to 1.54 ± 0.34. Because of these interactions, the conformation of whey protein was altered, with a significant reduction in the amount of α-helix and an increase in the amounts of β-sheet and turn structures.

  3. [Regulation on EGFR function via its interacting proteins and its potential application].

    Science.gov (United States)

    Zheng, Jun-Fang; Chen, Hui-Min; He, Jun-Qi

    2013-12-01

    Epidermal growth factor receptor (EGFR) is imptortant for cell activities, oncogenesis and cell migration, and EGFR inhibitor can treat cancer efficiently, but its side effects, for example, in skin, limited its usage. On the other hand, EGFR interacting proteins may also lead to oncogenesis and its interacting protein as drug targets can avoid cutaneous side effect, which implies possibly a better outcome and life quality of cancer patients. For the multiple EGFR interaction proteins, B1R enhances Erk/MAPK signaling, while PTPN12, Kek1, CEACAM1 and NHERF repress Erk/MAPK signaling. CaM may alter charge of EGFR juxamembrane domain and regulate activation of PI3K/Akt and PLC-gamma/PKC. STAT1, STAT5b are widely thought to be activated by EGFR, while there is unexpectedly inhibiting sequence within EGFR to repress the activity of STATs. LRIG1 and ACK1 enhance the internalization and degration of EGFR, while NHERF and HIP1 repress it. In this article, proteins interacting with EGFR, their interacting sites and their regulation on EGFR signal transduction will be reviewed.

  4. RAIN: RNA-protein Association and Interaction Networks

    DEFF Research Database (Denmark)

    Junge, Alexander; Refsgaard, Jan Christian; Garde, Christian

    2017-01-01

    is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data...

  5. The simulation approach to lipid-protein interactions.

    Science.gov (United States)

    Paramo, Teresa; Garzón, Diana; Holdbrook, Daniel A; Khalid, Syma; Bond, Peter J

    2013-01-01

    The interactions between lipids and proteins are crucial for a range of biological processes, from the folding and stability of membrane proteins to signaling and metabolism facilitated by lipid-binding proteins. However, high-resolution structural details concerning functional lipid/protein interactions are scarce due to barriers in both experimental isolation of native lipid-bound complexes and subsequent biophysical characterization. The molecular dynamics (MD) simulation approach provides a means to complement available structural data, yielding dynamic, structural, and thermodynamic data for a protein embedded within a physiologically realistic, modelled lipid environment. In this chapter, we provide a guide to current methods for setting up and running simulations of membrane proteins and soluble, lipid-binding proteins, using standard atomistically detailed representations, as well as simplified, coarse-grained models. In addition, we outline recent studies that illustrate the power of the simulation approach in the context of biologically relevant lipid/protein interactions.

  6. False positive reduction in protein-protein interaction predictions using gene ontology annotations

    Directory of Open Access Journals (Sweden)

    Lin Yen-Han

    2007-07-01

    Full Text Available Abstract Background Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated. Results Gene Ontology (GO annotations were used to reduce false positive protein-protein interactions (PPI pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The 'strength', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the strength varies between two and ten-fold of randomly removing protein pairs from the datasets. Conclusion Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially

  7. Integral UBL domain proteins: a family of proteasome interacting proteins

    DEFF Research Database (Denmark)

    Hartmann-Petersen, Rasmus; Gordon, Colin

    2004-01-01

    The family of ubiquitin-like (UBL) domain proteins (UDPs) comprises a conserved group of proteins involved in a multitude of different cellular activities. However, recent studies on UBL-domain proteins indicate that these proteins appear to share a common property in their ability to interact...

  8. Yeast Interacting Proteins Database: YOR302W, YOR047C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available rol of glucose-regulated gene expression; interacts with protein kinase Snf1p, glucose sensors Snf3p and Rgt...tein kinase Snf1p, glucose sensors Snf3p and Rgt2p, and TATA-binding protein Spt1

  9. Analysis of protein-protein interaction networks by means of annotated graph mining algorithms

    NARCIS (Netherlands)

    Rahmani, Hossein

    2012-01-01

    This thesis discusses solutions to several open problems in Protein-Protein Interaction (PPI) networks with the aid of Knowledge Discovery. PPI networks are usually represented as undirected graphs, with nodes corresponding to proteins and edges representing interactions among protein pairs. A large

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

    Directory of Open Access Journals (Sweden)

    Melody G Campbell

    2011-01-01

    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.

  11. Enhancement of both salivary protein-enological tannin interactions and astringency perception by ethanol.

    Science.gov (United States)

    Obreque-Slíer, Elías; Peña-Neira, Alvaro; López-Solís, Remigio

    2010-03-24

    Red wine astringency has been associated with interactions of tannins with salivary proteins. Tannins are active protein precipitants. Not much evidence exists demonstrating contribution of other wine components to astringency. We aimed to investigate an eventual role of ethanol both in astringency and salivary protein-enological tannin interactions. A trained sensory panel scored perceived astringency. Salivary protein-tannin interactions were assessed by observing both tannin-dependent changes in salivary protein diffusion on cellulose membranes and tannin-induced salivary protein precipitation. Proanthocyanidins and gallotannins in aqueous and hydroalcoholic solutions were assayed. A biphasic mode of diffusion on cellulose membranes displayed by salivary proteins was unaffected after dilution with water or enological concentrations of ethanol. At those concentrations ethanol was not astringent. In aqueous solution, tannins provoked both restriction of salivary protein diffusion, protein precipitation, and astringency. Those effects were exacerbated by 13% ethanol. In summary, enological concentrations of ethanol exacerbate astringency and salivary protein-tannin interactions.

  12. Potato leafroll virus structural proteins manipulate overlapping, yet distinct protein interaction networks during infection.

    Science.gov (United States)

    DeBlasio, Stacy L; Johnson, Richard; Sweeney, Michelle M; Karasev, Alexander; Gray, Stewart M; MacCoss, Michael J; Cilia, Michelle

    2015-06-01

    Potato leafroll virus (PLRV) produces a readthrough protein (RTP) via translational readthrough of the coat protein amber stop codon. The RTP functions as a structural component of the virion and as a nonincorporated protein in concert with numerous insect and plant proteins to regulate virus movement/transmission and tissue tropism. Affinity purification coupled to quantitative MS was used to generate protein interaction networks for a PLRV mutant that is unable to produce the read through domain (RTD) and compared to the known wild-type PLRV protein interaction network. By quantifying differences in the protein interaction networks, we identified four distinct classes of PLRV-plant interactions: those plant and nonstructural viral proteins interacting with assembled coat protein (category I); plant proteins in complex with both coat protein and RTD (category II); plant proteins in complex with the RTD (category III); and plant proteins that had higher affinity for virions lacking the RTD (category IV). Proteins identified as interacting with the RTD are potential candidates for regulating viral processes that are mediated by the RTP such as phloem retention and systemic movement and can potentially be useful targets for the development of strategies to prevent infection and/or viral transmission of Luteoviridae species that infect important crop species. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. 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/.

  14. Core Data of Yeast Interacting Proteins Database (Original Version) - Yeast Interacting Proteins Database | LSDB Archive [Life Science Database Archive metadata

    Lifescience Database Archive (English)

    Full Text Available y are in the reverse direction. *1 A comprehensive two-hybrid analysis to explore the yeast protein interact...s. 2000 Jan 1;28(1):73-6. *2 The yeast proteome database (YPD) and Caenorhabditis elegans proteome database (WormPD): comprehensive...000 Jan 1;28(1):73-6. *3 A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisia

  15. Deciphering peculiar protein-protein interacting modules in Deinococcus radiodurans

    Directory of Open Access Journals (Sweden)

    Barkallah Insaf

    2009-04-01

    Full Text Available Abstract Interactomes of proteins under positive selection from ionizing-radiation-resistant bacteria (IRRB might be a part of the answer to the question as to how IRRB, particularly Deinococcus radiodurans R1 (Deira, resist ionizing radiation. Here, using the Database of Interacting Proteins (DIP and the Protein Structural Interactome (PSI-base server for PSI map, we have predicted novel interactions of orthologs of the 58 proteins under positive selection in Deira and other IRRB, but which are absent in IRSB. Among these, 18 domains and their interactomes have been identified in DNA checkpoint and repair; kinases pathways; energy and nucleotide metabolisms were the important biological processes that were found to be involved. This finding provides new clues to the cellular pathways that can to be important for ionizing-radiation resistance in Deira.

  16. Using protein design algorithms to understand the molecular basis of disease caused by protein-DNA interactions: the Pax6 example

    DEFF Research Database (Denmark)

    Alibes, A.; Nadra, A.; De Masi, Federico

    2010-01-01

    diseases such as aniridia. The validity of FoldX to deal with protein-DNA interactions was demonstrated by showing that high levels of accuracy can be achieved for mutations affecting these interactions. Also we showed that protein-design algorithms can accurately reproduce experimental DNA-binding logos......Quite often a single or a combination of protein mutations is linked to specific diseases. However, distinguishing from sequence information which mutations have real effects in the protein's function is not trivial. Protein design tools are commonly used to explain mutations that affect protein...... stability, or protein-protein interaction, but not for mutations that could affect protein-DNA binding. Here, we used the protein design algorithm FoldX to model all known missense mutations in the paired box domain of Pax6, a highly conserved transcription factor involved in eye development and in several...

  17. Uncovering Viral Protein-Protein Interactions and their Role in Arenavirus Life Cycle

    Directory of Open Access Journals (Sweden)

    Nora López

    2012-09-01

    Full Text Available The Arenaviridae family includes widely distributed pathogens that cause severe hemorrhagic fever in humans. Replication and packaging of their single-stranded RNA genome involve RNA recognition by viral proteins and a number of key protein-protein interactions. Viral RNA synthesis is directed by the virus-encoded RNA dependent-RNA polymerase (L protein and requires viral RNA encapsidation by the Nucleoprotein. In addition to the role that the interaction between L and the Nucleoprotein may have in the replication process, polymerase activity appears to be modulated by the association between L and the small multifunctional Z protein. Z is also a structural component of the virions that plays an essential role in viral morphogenesis. Indeed, interaction of the Z protein with the Nucleoprotein is critical for genome packaging. Furthermore, current evidence suggests that binding between Z and the viral envelope glycoprotein complex is required for virion infectivity, and that Z homo-oligomerization is an essential step for particle assembly and budding. Efforts to understand the molecular basis of arenavirus life cycle have revealed important details on these viral protein-protein interactions that will be reviewed in this article.

  18. Prediction of heterodimeric protein complexes from weighted protein-protein interaction networks using novel features and kernel functions.

    Directory of Open Access Journals (Sweden)

    Peiying Ruan

    Full Text Available Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.

  19. 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.

  20. Structural interface parameters are discriminatory in recognising near-native poses of protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Sony Malhotra

    Full Text Available Interactions at the molecular level in the cellular environment play a very crucial role in maintaining the physiological functioning of the cell. These molecular interactions exist at varied levels viz. protein-protein interactions, protein-nucleic acid interactions or protein-small molecules interactions. Presently in the field, these interactions and their mechanisms mark intensively studied areas. Molecular interactions can also be studied computationally using the approach named as Molecular Docking. Molecular docking employs search algorithms to predict the possible conformations for interacting partners and then calculates interaction energies. However, docking proposes number of solutions as different docked poses and hence offers a serious challenge to identify the native (or near native structures from the pool of these docked poses. Here, we propose a rigorous scoring scheme called DockScore which can be used to rank the docked poses and identify the best docked pose out of many as proposed by docking algorithm employed. The scoring identifies the optimal interactions between the two protein partners utilising various features of the putative interface like area, short contacts, conservation, spatial clustering and the presence of positively charged and hydrophobic residues. DockScore was first trained on a set of 30 protein-protein complexes to determine the weights for different parameters. Subsequently, we tested the scoring scheme on 30 different protein-protein complexes and native or near-native structure were assigned the top rank from a pool of docked poses in 26 of the tested cases. We tested the ability of DockScore to discriminate likely dimer interactions that differ substantially within a homologous family and also demonstrate that DOCKSCORE can distinguish correct pose for all 10 recent CAPRI targets.

  1. 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.

  2. 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

  3. A protein domain interaction interface database: InterPare

    Directory of Open Access Journals (Sweden)

    Lee Jungsul

    2005-08-01

    Full Text Available Abstract Background Most proteins function by interacting with other molecules. Their interaction interfaces are highly conserved throughout evolution to avoid undesirable interactions that lead to fatal disorders in cells. Rational drug discovery includes computational methods to identify the interaction sites of lead compounds to the target molecules. Identifying and classifying protein interaction interfaces on a large scale can help researchers discover drug targets more efficiently. Description We introduce a large-scale protein domain interaction interface database called InterPare http://interpare.net. It contains both inter-chain (between chains interfaces and intra-chain (within chain interfaces. InterPare uses three methods to detect interfaces: 1 the geometric distance method for checking the distance between atoms that belong to different domains, 2 Accessible Surface Area (ASA, a method for detecting the buried region of a protein that is detached from a solvent when forming multimers or complexes, and 3 the Voronoi diagram, a computational geometry method that uses a mathematical definition of interface regions. InterPare includes visualization tools to display protein interior, surface, and interaction interfaces. It also provides statistics such as the amino acid propensities of queried protein according to its interior, surface, and interface region. The atom coordinates that belong to interface, surface, and interior regions can be downloaded from the website. Conclusion InterPare is an open and public database server for protein interaction interface information. It contains the large-scale interface data for proteins whose 3D-structures are known. As of November 2004, there were 10,583 (Geometric distance, 10,431 (ASA, and 11,010 (Voronoi diagram entries in the Protein Data Bank (PDB containing interfaces, according to the above three methods. In the case of the geometric distance method, there are 31,620 inter-chain domain

  4. DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

    Science.gov (United States)

    Mistry, Divya; Wise, Roger P; Dickerson, Julie A

    2017-01-01

    Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be

  5. (S)Pinning down protein interactions by NMR

    DEFF Research Database (Denmark)

    Teilum, Kaare; Kunze, Micha Ben Achim; Erlendsson, Simon

    2017-01-01

    Protein molecules are highly diverse communication platforms and their interaction repertoire stretches from atoms over small molecules such as sugars and lipids to macromolecules. An important route to understanding molecular communication is to quantitatively describe their interactions...... all types of protein reactions, which can span orders of magnitudes in affinities, reaction rates and lifetimes of states. As the more versatile technique, solution NMR spectroscopy offers a remarkable catalogue of methods that can be successfully applied to the quantitative as well as qualitative...... descriptions of protein interactions. In this review we provide an easy-access approach to NMR for the non-NMR specialist and describe how and when solution state NMR spectroscopy is the method of choice for addressing protein ligand interaction. We describe very briefly the theoretical background...

  6. Specificity and evolvability in eukaryotic protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Pedro Beltrao

    2007-02-01

    Full Text Available Progress in uncovering the protein interaction networks of several species has led to questions of what underlying principles might govern their organization. Few studies have tried to determine the impact of protein interaction network evolution on the observed physiological differences between species. Using comparative genomics and structural information, we show here that eukaryotic species have rewired their interactomes at a fast rate of approximately 10(-5 interactions changed per protein pair, per million years of divergence. For Homo sapiens this corresponds to 10(3 interactions changed per million years. Additionally we find that the specificity of binding strongly determines the interaction turnover and that different biological processes show significantly different link dynamics. In particular, human proteins involved in immune response, transport, and establishment of localization show signs of positive selection for change of interactions. Our analysis suggests that a small degree of molecular divergence can give rise to important changes at the network level. We propose that the power law distribution observed in protein interaction networks could be partly explained by the cell's requirement for different degrees of protein binding specificity.

  7. Protein Annotation from Protein Interaction Networks and Gene Ontology

    OpenAIRE

    Nguyen, Cao D.; Gardiner, Katheleen J.; Cios, Krzysztof J.

    2011-01-01

    We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precis...

  8. 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/.

  9. Exploration of the dynamic properties of protein complexes predicted from spatially constrained protein-protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Eric A Yen

    2014-05-01

    Full Text Available Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20,480 output structures and identified conformational states using principle component analysis. We interrogated the conformation landscape and found evidence of symmetry breaking, a mixture of likely active and inactive conformational states and dynamic exchange of the core protein Arc15 between core and regulatory components. Our method provides a novel tool for prediction and

  10. Energetics of the protein-DNA-water interaction

    Directory of Open Access Journals (Sweden)

    Marabotti Anna

    2007-01-01

    Full Text Available Abstract Background To understand the energetics of the interaction between protein and DNA we analyzed 39 crystallographically characterized complexes with the HINT (Hydropathic INTeractions computational model. HINT is an empirical free energy force field based on solvent partitioning of small molecules between water and 1-octanol. Our previous studies on protein-ligand complexes demonstrated that free energy predictions were significantly improved by taking into account the energetic contribution of water molecules that form at least one hydrogen bond with each interacting species. Results An initial correlation between the calculated HINT scores and the experimentally determined binding free energies in the protein-DNA system exhibited a relatively poor r2 of 0.21 and standard error of ± 1.71 kcal mol-1. However, the inclusion of 261 waters that bridge protein and DNA improved the HINT score-free energy correlation to an r2 of 0.56 and standard error of ± 1.28 kcal mol-1. Analysis of the water role and energy contributions indicate that 46% of the bridging waters act as linkers between amino acids and nucleotide bases at the protein-DNA interface, while the remaining 54% are largely involved in screening unfavorable electrostatic contacts. Conclusion This study quantifies the key energetic role of bridging waters in protein-DNA associations. In addition, the relevant role of hydrophobic interactions and entropy in driving protein-DNA association is indicated by analyses of interaction character showing that, together, the favorable polar and unfavorable polar/hydrophobic-polar interactions (i.e., desolvation mostly cancel.

  11. In vivo interactions between the proteins of infectious bursal disease virus: capsid protein VP3 interacts with the RNA dependent polymerase VP1

    NARCIS (Netherlands)

    Tacken, M.G.J.; Rottier, P.J.M.; Gielkens, A.L.J.; Peeters, B.P.H.

    2000-01-01

    Little is known about the intermolecular interactions between the viral proteins of infectious bursal disease virus (IBDV). By using the yeast two-hybrid system, which allows the detection of protein-protein interactions in vivo, all possible interactions were tested by fusing the viral proteins to

  12. Interactions in vivo between the proteins of infectious bursal disease virus: capsid protein VP3 interacts with the RNA-dependent polymerase, VP1

    NARCIS (Netherlands)

    Tacken, M.G.J.; Rottier, P.J.M.; Gielkens, A.L.J.; Peeters, B.P.H.

    2000-01-01

    Little is known about the intermolecular interactions between the viral proteins of infectious bursal disease virus (IBDV). By using the yeast two-hybrid system, which allows the detection of protein-protein interactions in vivo, all possible interactions were tested by fusing the viral proteins to

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

    KAUST Repository

    Haidar, Malak; de Laté , Perle Latré ; Kennedy, Eileen J.; Langsley, Gordon

    2017-01-01

    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

  14. Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors.

    Science.gov (United States)

    Liu, Zhi-Ping; Chen, Luonan

    2016-01-01

    Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

  15. A Type-2 fuzzy data fusion approach for building reliable weighted protein interaction networks with application in protein complex detection.

    Science.gov (United States)

    Mehranfar, Adele; Ghadiri, Nasser; Kouhsar, Morteza; Golshani, Ashkan

    2017-09-01

    Detecting the protein complexes is an important task in analyzing the protein interaction networks. Although many algorithms predict protein complexes in different ways, surveys on the interaction networks indicate that about 50% of detected interactions are false positives. Consequently, the accuracy of existing methods needs to be improved. In this paper we propose a novel algorithm to detect the protein complexes in 'noisy' protein interaction data. First, we integrate several biological data sources to determine the reliability of each interaction and determine more accurate weights for the interactions. A data fusion component is used for this step, based on the interval type-2 fuzzy voter that provides an efficient combination of the information sources. This fusion component detects the errors and diminishes their effect on the detection protein complexes. So in the first step, the reliability scores have been assigned for every interaction in the network. In the second step, we have proposed a general protein complex detection algorithm by exploiting and adopting the strong points of other algorithms and existing hypotheses regarding real complexes. Finally, the proposed method has been applied for the yeast interaction datasets for predicting the interactions. The results show that our framework has a better performance regarding precision and F-measure than the existing approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Targeting protein-protein interaction between MLL1 and reciprocal proteins for leukemia therapy.

    Science.gov (United States)

    Wang, Zhi-Hui; Li, Dong-Dong; Chen, Wei-Lin; You, Qi-Dong; Guo, Xiao-Ke

    2018-01-15

    The mixed lineage leukemia protein-1 (MLL1), as a lysine methyltransferase, predominantly regulates the methylation of histone H3 lysine 4 (H3K4) and functions in hematopoietic stem cell (HSC) self-renewal. MLL1 gene fuses with partner genes that results in the generation of MLL1 fusion proteins (MLL1-FPs), which are frequently detected in acute leukemia. In the progress of leukemogenesis, a great deal of proteins cooperate with MLL1 to form multiprotein complexes serving for the dysregulation of H3K4 methylation, the overexpression of homeobox (HOX) cluster genes, and the consequent generation of leukemia. Hence, disrupting the interactions between MLL1 and the reciprocal proteins has been considered to be a new treatment strategy for leukemia. Here, we reviewed potential protein-protein interactions (PPIs) between MLL1 and its reciprocal proteins, and summarized the inhibitors to target MLL1 PPIs. The druggability of MLL1 PPIs for leukemia were also discussed. Copyright © 2017. Published by Elsevier Ltd.

  17. Drosophila Protein interaction Map (DPiM)

    OpenAIRE

    Guruharsha, K.G.; Obar, Robert A.; Mintseris, Julian; Aishwarya, K.; Krishnan, R.T.; VijayRaghavan, K.; Artavanis-Tsakonas, Spyros

    2012-01-01

    Proteins perform essential cellular functions as part of protein complexes, often in conjunction with RNA, DNA, metabolites and other small molecules. The genome encodes thousands of proteins but not all of them are expressed in every cell type; and expressed proteins are not active at all times. Such diversity of protein expression and function accounts for the level of biological intricacy seen in nature. Defining protein-protein interactions in protein complexes, and establishing the when,...

  18. A Library of Plasmodium vivax Recombinant Merozoite Proteins Reveals New Vaccine Candidates and Protein-Protein Interactions

    Science.gov (United States)

    Hostetler, Jessica B.; Sharma, Sumana; Bartholdson, S. Josefin; Wright, Gavin J.; Fairhurst, Rick M.; Rayner, Julian C.

    2015-01-01

    Background 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. Methodology/Principal Findings 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

  19. The genotype-environment interaction variance in rice-seed protein determination

    International Nuclear Information System (INIS)

    Ismachin, M.

    1976-01-01

    Many environmental factors influence the protein content of cereal seed. This fact procured difficulties in breeding for protein. Yield is another example on which so many environmental factors are of influence. The length of time required by the plant to reach maturity, is also affected by the environmental factors; even though its effect is not too decisive. In this investigation the genotypic variance and the genotype-environment interaction variance which contribute to the total variance or phenotypic variance was analysed, with purpose to give an idea to the breeder how selection should be made. It was found that genotype-environment interaction variance is larger than the genotypic variance in contribution to total variance of protein-seed determination or yield. In the analysis of the time required to reach maturity it was found that genotypic variance is larger than the genotype-environment interaction variance. It is therefore clear, why selection for time required to reach maturity is much easier than selection for protein or yield. Selected protein in one location may be different from that to other locations. (author)

  20. Effect of casein to whey protein ratios on the protein interactions and coagulation properties of low-fat yogurt.

    Science.gov (United States)

    Zhao, L L; Wang, X L; Tian, Q; Mao, X Y

    2016-10-01

    In this study, we investigated the effect of casein (CN) to whey protein (WP) ratios (4:1, 3:1, 2:1, and 1:1) on gelation properties and microstructure of low-fat yogurt made with reconstituted skim milk with or without addition of whey protein concentrate. The rheological properties (storage modulus, G'; yield stress; and yield strain) of the obtained low-fat yogurt were greatly enhanced, the fermentation period was shortened, and the microstructure became more compact with smaller pores as the CN:WP ratio decreased. When CN:WP was 2:1 or 1:1, the obtained yogurt coagulum showed higher G' and greater yield stress, with more compact crosslinking and smaller pores. In addition, the more of skim milk powder was replaced by whey protein concentrate, the more disulfide bonds were formed and the greater the occurrence of hydrophobic interactions during heat treatment, which can improve the rheological properties and microstructure of low-fat yogurt. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  1. Predicting the binding patterns of hub proteins: a study using yeast protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Carson M Andorf

    Full Text Available Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH with one or two binding sites, or multiple-interface hubs (MIH with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations or party hubs (i.e., simultaneously interact with multiple partners.Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques.Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions.We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.

  2. Characterization of host proteins interacting with the lymphocytic choriomeningitis virus L protein.

    Science.gov (United States)

    Khamina, Kseniya; Lercher, Alexander; Caldera, Michael; Schliehe, Christopher; Vilagos, Bojan; Sahin, Mehmet; Kosack, Lindsay; Bhattacharya, Anannya; Májek, Peter; Stukalov, Alexey; Sacco, Roberto; James, Leo C; Pinschewer, Daniel D; Bennett, Keiryn L; Menche, Jörg; Bergthaler, Andreas

    2017-12-01

    RNA-dependent RNA polymerases (RdRps) play a key role in the life cycle of RNA viruses and impact their immunobiology. The arenavirus lymphocytic choriomeningitis virus (LCMV) strain Clone 13 provides a benchmark model for studying chronic infection. A major genetic determinant for its ability to persist maps to a single amino acid exchange in the viral L protein, which exhibits RdRp activity, yet its functional consequences remain elusive. To unravel the L protein interactions with the host proteome, we engineered infectious L protein-tagged LCMV virions by reverse genetics. A subsequent mass-spectrometric analysis of L protein pulldowns from infected human cells revealed a comprehensive network of interacting host proteins. The obtained LCMV L protein interactome was bioinformatically integrated with known host protein interactors of RdRps from other RNA viruses, emphasizing interconnected modules of human proteins. Functional characterization of selected interactors highlighted proviral (DDX3X) as well as antiviral (NKRF, TRIM21) host factors. To corroborate these findings, we infected Trim21-/- mice with LCMV and found impaired virus control in chronic infection. These results provide insights into the complex interactions of the arenavirus LCMV and other viral RdRps with the host proteome and contribute to a better molecular understanding of how chronic viruses interact with their host.

  3. Protein-protein interactions within the ensemble, eukaryotic V-ATPase, and its concerted interactions with cellular machineries.

    Science.gov (United States)

    Balakrishna, Asha Manikkoth; Manimekalai, Malathy Sony Subramanian; Grüber, Gerhard

    2015-10-01

    The V1VO-ATPase (V-ATPase) is the important proton-pump in eukaryotic cells, responsible for pH-homeostasis, pH-sensing and amino acid sensing, and therefore essential for cell growths and metabolism. ATP-cleavage in the catalytic A3B3-hexamer of V1 has to be communicated via several so-called central and peripheral stalk units to the proton-pumping VO-part, which is membrane-embedded. A unique feature of V1VO-ATPase regulation is its reversible disassembly of the V1 and VO domain. Actin provides a network to hold the V1 in proximity to the VO, enabling effective V1VO-assembly to occur. Besides binding to actin, the 14-subunit V-ATPase interacts with multi-subunit machineries to form cellular sensors, which regulate the pH in cellular compartments or amino acid signaling in lysosomes. Here we describe a variety of subunit-subunit interactions within the V-ATPase enzyme during catalysis and its protein-protein assembling with key cellular machineries, essential for cellular function. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. The stability and formation of native proteins from unfolded monomers is increased through interactions with unrelated proteins.

    Directory of Open Access Journals (Sweden)

    Claudia Rodríguez-Almazán

    Full Text Available The intracellular concentration of protein may be as high as 400 mg per ml; thus it seems inevitable that within the cell, numerous protein-protein contacts are constantly occurring. A basic biochemical principle states that the equilibrium of an association reaction can be shifted by ligand binding. This indicates that if within the cell many protein-protein interactions are indeed taking place, some fundamental characteristics of proteins would necessarily differ from those observed in traditional biochemical systems. Accordingly, we measured the effect of eight different proteins on the formation of homodimeric triosephosphate isomerase from Trypanosoma brucei (TbTIM from guanidinium chloride unfolded monomers. The eight proteins at concentrations of micrograms per ml induced an important increase on active dimer formation. Studies on the mechanism of this phenomenon showed that the proteins stabilize the dimeric structure of TbTIM, and that this is the driving force that promotes the formation of active dimers. Similar data were obtained with TIM from three other species. The heat changes that occur when TbTIM is mixed with lysozyme were determined by isothermal titration calorimetry; the results provided direct evidence of the weak interaction between apparently unrelated proteins. The data, therefore, are strongly suggestive that the numerous protein-protein interactions that occur in the intracellular space are an additional control factor in the formation and stability of proteins.

  5. A Mesoscopic Model for Protein-Protein Interactions in Solution

    OpenAIRE

    Lund, Mikael; Jönsson, Bo

    2003-01-01

    Protein self-association may be detrimental in biological systems, but can be utilized in a controlled fashion for protein crystallization. It is hence of considerable interest to understand how factors like solution conditions prevent or promote aggregation. Here we present a computational model describing interactions between protein molecules in solution. The calculations are based on a molecular description capturing the detailed structure of the protein molecule using x-ray or nuclear ma...

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

    Directory of Open Access Journals (Sweden)

    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.

  7. Protein-Protein Interactions of Viroporins in Coronaviruses and Paramyxoviruses: New Targets for Antivirals?

    Directory of Open Access Journals (Sweden)

    Jaume Torres

    2015-06-01

    Full Text Available Viroporins are members of a rapidly growing family of channel-forming small polypeptides found in viruses. The present review will be focused on recent structural and protein-protein interaction information involving two viroporins found in enveloped viruses that target the respiratory tract; (i the envelope protein in coronaviruses and (ii the small hydrophobic protein in paramyxoviruses. Deletion of these two viroporins leads to viral attenuation in vivo, whereas data from cell culture shows involvement in the regulation of stress and inflammation. The channel activity and structure of some representative members of these viroporins have been recently characterized in some detail. In addition, searches for protein-protein interactions using yeast-two hybrid techniques have shed light on possible functional roles for their exposed cytoplasmic domains. A deeper analysis of these interactions should not only provide a more complete overview of the multiple functions of these viroporins, but also suggest novel strategies that target protein-protein interactions as much needed antivirals. These should complement current efforts to block viroporin channel activity.

  8. 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.

  9. Protein-surface interactions on stimuli-responsive polymeric biomaterials.

    Science.gov (United States)

    Cross, Michael C; Toomey, Ryan G; Gallant, Nathan D

    2016-03-04

    Responsive surfaces: a review of the dependence of protein adsorption on the reversible volume phase transition in stimuli-responsive polymers. Specifically addressed are a widely studied subset: thermoresponsive polymers. Findings are also generalizable to other materials which undergo a similarly reversible volume phase transition. As of 2015, over 100,000 articles have been published on stimuli-responsive polymers and many more on protein-biomaterial interactions. Significantly, fewer than 100 of these have focused specifically on protein interactions with stimuli-responsive polymers. These report a clear trend of increased protein adsorption in the collapsed state compared to the swollen state. This control over protein interactions makes stimuli-responsive polymers highly useful in biomedical applications such as wound repair scaffolds, on-demand drug delivery, and antifouling surfaces. Outstanding questions are whether the protein adsorption is reversible with the volume phase transition and whether there is a time-dependence. A clear understanding of protein interactions with stimuli-responsive polymers will advance theoretical models, experimental results, and biomedical applications.

  10. Probing hydrogen bonding interactions and proton transfer in proteins

    Science.gov (United States)

    Nie, Beining

    Scope and method of study. Hydrogen bonding is a fundamental element in protein structure and function. Breaking a single hydrogen bond may impair the stability of a protein. It is therefore important to probe dynamic changes in hydrogen bonding interactions during protein folding and function. Time-resolved Fourier transform infrared spectroscopy is highly sensitive to hydrogen bonding interactions. However, it lacks quantitative correlation between the vibrational frequencies and the number, type, and strength of hydrogen bonding interactions of ionizable and polar residues. We employ quantum physics theory based ab initio calculations to study the effects of hydrogen bonding interactions on vibrational frequencies of Asp, Glu, and Tyr residues and to develop vibrational spectral markers for probing hydrogen bonding interactions using infrared spectroscopy. In addition, proton transfer process plays a crucial role in a wide range of energy transduction, signal transduction, and enzymatic reactions. We study the structural basis for proton transfer using photoactive yellow protein as an excellent model system. Molecular dynamics simulation is employed to investigate the structures of early intermediate states. Quantum theory based ab initio calculations are used to study the impact of hydrogen bond interactions on proton affinity and proton transfer. Findings and conclusions. Our extensive density function theory based calculations provide rich structural, spectral, and energetic information on hydrogen bonding properties of protonated side chain groups of Asp/Glu and Tyr. We developed vibrational spectral markers and 2D FTIR spectroscopy for structural characterization on the number and the type of hydrogen bonding interactions of the COOH group of Asp/Glu and neutral phenolic group of Tyr. These developments greatly enhance the power of time-resolved FTIR spectroscopy as a major experimental tool for structural characterization of functionally important

  11. Mapping functional prion-prion protein interaction sites using prion protein based peptide-arrays

    NARCIS (Netherlands)

    Rigter, A.; Priem, J.; Timmers-Parohi, D.; Langeveld, J.; Bossers, A.

    2009-01-01

    Protein-protein interactions are at the basis of most if not all biological processes in living cells. Therefore, adapting existing techniques or developing new techniques to study interactions between proteins are of importance in elucidating which amino acid sequences contribute to these

  12. Gel properties and interactions of Mesona blumes polysaccharide-soy protein isolates mixed gel: The effect of salt addition.

    Science.gov (United States)

    Wang, Wenjie; Shen, Mingyue; Liu, Suchen; Jiang, Lian; Song, Qianqian; Xie, Jianhua

    2018-07-15

    Effect of different salt ions on the gel properties and microstructure of Mesona blumes polysaccharide (MBP)-soy protein isolates (SPI) mixed gels were investigated. Sodium and calcium ions were chosen to explore their effects on the rheological behavior and gel properties of MBP-SPI mixed gels were evaluated by using rheological, X-ray diffraction, protein solubility determination, and microstructure analysis. Results showed that the addition of salt ions change the crystalline state of gels system, the crystal of gel was enhanced at low ion concentrations (0.005-0.01 M). The two peaks of gel characteristic at 8.9° and 19.9° almost disappeared at high salt ions concentrations (0.015-0.02 M), and new crystallization peaks appeared at around 30° and 45°. The elasticity, viscosity, gel strength, water holding capacity, and thermal stability of gel were increased at low ion concentration. Results showed that the main interactions which promoted gel formation and maintain the three-dimensional structure of the gel were electrostatic interactions, hydrophobic interactions, and disulfide interactions. Copyright © 2018 Elsevier Ltd. All rights reserved.

  13. Yeast Interacting Proteins Database: YNL258C, YKR022C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YNL258C DSL1 Peripheral membrane protein required for Golgi-to-ER retrograde traffi...equired for Golgi-to-ER retrograde traffic; component of the ER target site that interacts with coatomer, th...it ORF YNL258C Bait gene name DSL1 Bait description Peripheral membrane protein r

  14. 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.

  15. Prediction of localization and interactions of apoptotic proteins

    Directory of Open Access Journals (Sweden)

    Matula Pavel

    2009-07-01

    Full Text Available Abstract During apoptosis several mitochondrial proteins are released. Some of them participate in caspase-independent nuclear DNA degradation, especially apoptosis-inducing factor (AIF and endonuclease G (endoG. Another interesting protein, which was expected to act similarly as AIF due to the high sequence homology with AIF is AIF-homologous mitochondrion-associated inducer of death (AMID. We studied the structure, cellular localization, and interactions of several proteins in silico and also in cells using fluorescent microscopy. We found the AMID protein to be cytoplasmic, most probably incorporated into the cytoplasmic side of the lipid membranes. Bioinformatic predictions were conducted to analyze the interactions of the studied proteins with each other and with other possible partners. We conducted molecular modeling of proteins with unknown 3D structures. These models were then refined by MolProbity server and employed in molecular docking simulations of interactions. Our results show data acquired using a combination of modern in silico methods and image analysis to understand the localization, interactions and functions of proteins AMID, AIF, endonuclease G, and other apoptosis-related proteins.

  16. Affinity purification combined with mass spectrometry to identify herpes simplex virus protein-protein interactions.

    Science.gov (United States)

    Meckes, David G

    2014-01-01

    The identification and characterization of herpes simplex virus protein interaction complexes are fundamental to understanding the molecular mechanisms governing the replication and pathogenesis of the virus. Recent advances in affinity-based methods, mass spectrometry configurations, and bioinformatics tools have greatly increased the quantity and quality of protein-protein interaction datasets. In this chapter, detailed and reliable methods that can easily be implemented are presented for the identification of protein-protein interactions using cryogenic cell lysis, affinity purification, trypsin digestion, and mass spectrometry.

  17. Interactions between Surfactants in Solution and Electrospun Protein Fibers: Effects on Release Behavior and Fiber Properties

    DEFF Research Database (Denmark)

    Boutrup Stephansen, Karen; García-Díaz, María; Jessen, Flemming

    2016-01-01

    , and drug delivery. In the present study, we present a systematic investigation of how surfactants and proteins, as physiologically relevant components, interact with insulin-loaded fish sarcoplasmic protein (FSP) electrospun fibers (FSP-Ins fibers) in solution and thereby affect fiber properties...... such as accessible surface hydrophilicity, physical stability, and release characteristics of an encapsulated drug. Interactions between insulin-loaded protein fibers and five anionic surfactants (sodium taurocholate, sodium taurodeoxycholate, sodium glycocholate, sodium glycodeoxycholate, and sodium dodecyl sulfate......Intermolecular interaction phenomena occurring between endogenous compounds, such as proteins and bile salts, and electrospun compounds are so far unreported, despite the exposure of fibers to such biorelevant compounds when applied for biomedical purposes, e.g., tissue engineering, wound healing...

  18. In silico modeling of the yeast protein and protein family interaction network

    Science.gov (United States)

    Goh, K.-I.; Kahng, B.; Kim, D.

    2004-03-01

    Understanding of how protein interaction networks of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce an in silico ``coevolutionary'' model for the protein interaction network and the protein family network. The essential ingredient of the model includes the protein family identity and its robustness under evolution, as well as the three previously proposed: gene duplication, divergence, and mutation. This model produces a prototypical feature of complex networks in a wide range of parameter space, following the generalized Pareto distribution in connectivity. Moreover, we investigate other structural properties of our model in detail with some specific values of parameters relevant to the yeast Saccharomyces cerevisiae, showing excellent agreement with the empirical data. Our model indicates that the physical constraints encoded via the domain structure of proteins play a crucial role in protein interactions.

  19. [Interaction of protein with charged colloidal particles].

    Science.gov (United States)

    Durdenko, E V; Kuznetsova, S M; Basova, L V; Tikhonenko, S A; Saburova, E A

    2011-01-01

    The functional state of three proteins of different molecular weight (urease, lactate dehydrogenase, and hemoglobin) in the presence of the linear polyelectrolytes poly(allylamine hydrochloride) (PAA) and sodium poly(styrenesulfonate) (PSS) in the dissolved state and of the same polyelectrolytes bound to the surface of microspheres has been investigated. Microspheres were prepared by consecutive absorption of oppositely charged polyelectrolytes so that the outer layer of the shell was PAA for the acidic protein urease, and PSS for the alkaline proteins LDH and hemoglobin. It was shown that the dissolved polyelectrolyte completely inactivates all three proteins within one minute with a slight difference in the time constant. (By Hb inactivation are conventionally meant changes in the heme environment observed from the spectrum in the Soret band.) In the presence of microspheres, the proteins were adsorbed on their surface; in this case, more than 95% of the activity was retained within two hours. The proportion of the protein adsorbed on microspheres accounted for about 98% for urease, 72% for Hb, and 35% for LDH, as determined from the tryptophan fluorescence data. The interaction of hemoglobin with another type of charged colloidal particles, phospholipid vesicles, leads to the destruction of the tertiary structure of the protein, which made itself evident in the optical absorption spectra in the Soret band, as well as the spectra of tryptophan fluorescence and circular dichroism. In this case, according to circular dichroism, the percentage of alpha-helical structure of Hb was maintained. The differences in the physical and chemical mechanisms of interaction of proteins with these two types of charged colloidal particles that leads to differences in the degree of denaturing effects are discussed.

  20. Towards a better understanding of the specificity of protein-protein interaction

    Czech Academy of Sciences Publication Activity Database

    Kysilka, Jiří; Vondrášek, Jiří

    2012-01-01

    Roč. 25, č. 11 (2012), s. 604-615 ISSN 0952-3499 R&D Projects: GA ČR GAP208/10/0725; GA ČR GAP302/10/0427; GA MŠk(CZ) LH11020 Institutional research plan: CEZ:AV0Z40550506; CEZ:AV0Z50520701 Keywords : protein-protein interaction * molecular recognition * x-ray structure analysis * empirical potentials * side chain-side chain interaction * interaction energy * bioinformatics Subject RIV: CE - Biochemistry Impact factor: 3.006, year: 2012

  1. Effect of solvent on the structure of a protein (H3.1) with a coarse-grained model with knowledge-based interactions

    Science.gov (United States)

    Pandey, Ras; Farmer, Barry

    2013-03-01

    Quality of solvent plays a critical role in modulating the structure of a protein along with the temperature. Using a coarse-grained Monte Carlo simulation based on three knowledge-based contact potentials (MJ, BT, BFKV) we examine the structure and dynamics of a histone (H3.1). The empty lattice sites constitute the effective solvent medium in which the protein is embedded. Residue-solvent characteristic interaction is based on the hydropathy index while the residue-residue interaction is used from the knowledge-based contact matrices derived from ensembles of protein structures in the protein data bank. Large scale simulations are performed to analyze the structure of protein for a range of residue-solvent interaction strength, a measure of the solvent quality with each potential. Unlike the monotonic thermal response, the radius of gyration of the protein exhibits non-monotonic dependence of the solvent strength. Quantitative comparison of the structure and dynamics emerging from three knowledge-based potentials will be presented in this talk. This work is supported by Air Force Research Laboratory.

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

    Directory of Open Access Journals (Sweden)

    Meijing Li

    2015-01-01

    Full Text Available 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 model, and PPI discovery model. The Protein-NER model, which is named ProNER, identifies the protein names based on two methods: dictionary-based method and machine learning-based method. ProNER is capable of identifying more proteins than dictionary-based Protein-NER model in other existing systems. The final discovered PPIs extracted via PPI discovery model are represented in detail because we showed the protein interaction types and the occurrence frequency through two different methods. In the experiments, the result shows that the performances achieved by our ProNER and PPI discovery model are better than other existing tools. PPIMiner applied this protein-named entity recognition approach and parsing tree based PPI extraction method to improve the performance of PPI extraction. We also provide an easy-to-use interface to access PPIs database and an online system for PPIs extraction and Protein-NER.

  3. Identification of brain-specific angiogenesis inhibitor 2 as an interaction partner of glutaminase interacting protein

    International Nuclear Information System (INIS)

    Zencir, Sevil; Ovee, Mohiuddin; Dobson, Melanie J.; Banerjee, Monimoy; Topcu, Zeki; Mohanty, Smita

    2011-01-01

    Highlights: → Brain-specific angiogenesis inhibitor 2 (BAI2) is a new partner protein for GIP. → BAI2 interaction with GIP was revealed by yeast two-hybrid assay. → Binding of BAI2 to GIP was characterized by NMR, CD and fluorescence. → BAI2 and GIP binding was mediated through the C-terminus of BAI2. -- Abstract: The vast majority of physiological processes in living cells are mediated by protein-protein interactions often specified by particular protein sequence motifs. PDZ domains, composed of 80-100 amino acid residues, are an important class of interaction motif. Among the PDZ-containing proteins, glutaminase interacting protein (GIP), also known as Tax Interacting Protein TIP-1, is unique in being composed almost exclusively of a single PDZ domain. GIP has important roles in cellular signaling, protein scaffolding and modulation of tumor growth and interacts with a number of physiological partner proteins, including Glutaminase L, β-Catenin, FAS, HTLV-1 Tax, HPV16 E6, Rhotekin and Kir 2.3. To identify the network of proteins that interact with GIP, a human fetal brain cDNA library was screened using a yeast two-hybrid assay with GIP as bait. We identified brain-specific angiogenesis inhibitor 2 (BAI2), a member of the adhesion-G protein-coupled receptors (GPCRs), as a new partner of GIP. BAI2 is expressed primarily in neurons, further expanding GIP cellular functions. The interaction between GIP and the carboxy-terminus of BAI2 was characterized using fluorescence, circular dichroism (CD) and nuclear magnetic resonance (NMR) spectroscopy assays. These biophysical analyses support the interaction identified in the yeast two-hybrid assay. This is the first study reporting BAI2 as an interaction partner of GIP.

  4. Kinase Substrate Sensor (KISS), a mammalian in situ protein interaction sensor.

    Science.gov (United States)

    Lievens, Sam; Gerlo, Sarah; Lemmens, Irma; De Clercq, Dries J H; Risseeuw, Martijn D P; Vanderroost, Nele; De Smet, Anne-Sophie; Ruyssinck, Elien; Chevet, Eric; Van Calenbergh, Serge; Tavernier, Jan

    2014-12-01

    Probably every cellular process is governed by protein-protein interaction (PPIs), which are often highly dynamic in nature being modulated by in- or external stimuli. Here we present KISS, for KInase Substrate Sensor, a mammalian two-hybrid approach designed to map intracellular PPIs and some of the dynamic features they exhibit. Benchmarking experiments indicate that in terms of sensitivity and specificity KISS is on par with other binary protein interaction technologies while being complementary with regard to the subset of PPIs it is able to detect. We used KISS to evaluate interactions between different types of proteins, including transmembrane proteins, expressed at their native subcellular location. In situ analysis of endoplasmic reticulum stress-induced clustering of the endoplasmic reticulum stress sensor ERN1 and ligand-dependent β-arrestin recruitment to GPCRs illustrated the method's potential to study functional PPI modulation in complex cellular processes. Exploring its use as a tool for in cell evaluation of pharmacological interference with PPIs, we showed that reported effects of known GPCR antagonists and PPI inhibitors are properly recapitulated. In a three-hybrid setup, KISS was able to map interactions between small molecules and proteins. Taken together, we established KISS as a sensitive approach for in situ analysis of protein interactions and their modulation in a changing cellular context or in response to pharmacological challenges. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

  5. Identification of lipopolysaccharide-interacting plasma membrane-type proteins in Arabidopsis thaliana.

    Science.gov (United States)

    Vilakazi, Cornelius S; Dubery, Ian A; Piater, Lizelle A

    2017-02-01

    Lipopolysaccharide (LPS) is an amphiphatic bacterial glycoconjugate found on the external membrane of Gram-negative bacteria. This endotoxin is considered as a microbe-associated molecular pattern (MAMP) molecule and has been shown to elicit defense responses in plants. Here, LPS-interacting proteins from Arabidopsis thaliana plasma membrane (PM)-type fractions were captured and identified in order to investigate those involved in LPS perception and linked to triggering of innate immune responses. A novel proteomics-based affinity-capture strategy coupled to liquid chromatography-tandem mass spectrometry (LC-MS/MS) was employed for the enrichment and identification of LPS-interacting proteins. As such, LPS isolated from Burkholderia cepacia (LPS B.cep. ) was immobilized on three independent and distinct affinity-based matrices to serve as bait for interacting proteins from A. thaliana leaf and callus tissue. These were resolved by 1D electrophoresis and identified by mass spectrometry. Proteins specifically bound to LPS B.cep. have been implicated in membrane structure (e.g. COBRA-like and tubulin proteins), membrane trafficking and/or transport (e.g. soluble NSF attachment protein receptor (SNARE) proteins, patellin, aquaporin, PM instrinsic proteins (PIP) and H + -ATPase), signal transduction (receptor-like kinases and calcium-dependent protein kinases) as well as defense/stress responses (e.g. hypersensitive-induced response (HIR) proteins, jacalin-like lectin domain-containing protein and myrosinase-binding proteins). The novel affinity-capture strategy for the enrichment of LPS-interacting proteins proved to be effective, especially in the binding of proteins involved in plant defense responses, and can thus be used to elucidate LPS-mediated molecular recognition and disease mechanism(s). Copyright © 2016 Elsevier Masson SAS. All rights reserved.

  6. Single-well monitoring of protein-protein interaction and phosphorylation-dephosphorylation events.

    Science.gov (United States)

    Arcand, Mathieu; Roby, Philippe; Bossé, Roger; Lipari, Francesco; Padrós, Jaime; Beaudet, Lucille; Marcil, Alexandre; Dahan, Sophie

    2010-04-20

    We combined oxygen channeling assays with two distinct chemiluminescent beads to detect simultaneously protein phosphorylation and interaction events that are usually monitored separately. This novel method was tested in the ERK1/2 MAP kinase pathway. It was first used to directly monitor dissociation of MAP kinase ERK2 from MEK1 upon phosphorylation and to evaluate MAP kinase phosphatase (MKP) selectivity and mechanism of action. In addition, MEK1 and ERK2 were probed with an ATP competitor and an allosteric MEK1 inhibitor, which generated distinct phosphorylation-interaction patterns. Simultaneous monitoring of protein-protein interactions and substrate phosphorylation can provide significant mechanistic insight into enzyme activity and small molecule action.

  7. Probing the interaction of ferrocene containing hyperbranched poly-ester with model plasma protein: Effect on the interaction mechanism and conformational change

    Energy Technology Data Exchange (ETDEWEB)

    Xiao, Fengjuan, E-mail: xfj66@126.com; Gu, Muqing; Liang, Ye; Li, Lanlan; Yu, Xiaolei; Wu, Xiangfeng

    2014-05-01

    Interaction mechanism and conformational change of model plasma protein-bovine serum albumin (BSA) induced by ferrocenyl-functionalized hyperbranched polyester (HBPE-Fc) were investigated using cyclicvoltammetry (CV), differential pulsed voltammetry (DPV), fluorescence, UV–vis absorption spectrometry and circular dichroism (CD). Some complicated interactions occurred between BSA and HBPE-Fc and the new redox centers appeared in the BSA/HBPE-Fc complex that changed and hindered the electron transfer of Fe/Fe{sup 2+}. Fluorescence quenching data showed that the fluorescence of BSA was statically quenched by HBPE-Fc, which implied that ground state complex formed between BSA and HBPE-Fc. van der Waals force and hydrogen bond played major roles in the interaction of HBPE-Fc with BSA. The binding constant Ka for HBPE-Fc–protein interaction is in the order of 10{sup 6} at room temperature indicates that there is a strong interaction between HBPE-Fc and BSA. Synchronous, three-dimensional fluorescence and CD studies indicated that the interaction of BSA with HBPE-Fc induced conformational changes in BSA with overall decrease in the α-helical structure and increase in β-pleated sheet structure. The molecular model of the interaction between HBPE-Fc and BSA was also presented according to the results in this study. - Highlights: • A novel ferrocenyl-functionalized hyperbranched polymer (HBPE-Fc) with potential anticancer effects. • New redox centers appear in the BSA/HBPE-Fc complex that changed and hindered the electron transfer of Fe/Fe{sup 2+}. • BSA fluorescence was statically quenched by HBPE-Fc. • BSA/HBPE-Fc ground state complex was mainly formed by the hydrogen bonds and van der Waals force. • HBPE-Fc induced conformational changes in BSA with overall decrease in the α-helical structure and increase in β-pleated sheet structure. • The molecular model of the interaction was presented according to the results in this study.

  8. Predicting Protein-Protein Interactions Using BiGGER: Case Studies

    Directory of Open Access Journals (Sweden)

    Rui M. Almeida

    2016-08-01

    Full Text Available The importance of understanding interactomes makes preeminent the study of protein interactions and protein complexes. Traditionally, protein interactions have been elucidated by experimental methods or, with lower impact, by simulation with protein docking algorithms. This article describes features and applications of the BiGGER docking algorithm, which stands at the interface of these two approaches. BiGGER is a user-friendly docking algorithm that was specifically designed to incorporate experimental data at different stages of the simulation, to either guide the search for correct structures or help evaluate the results, in order to combine the reliability of hard data with the convenience of simulations. Herein, the applications of BiGGER are described by illustrative applications divided in three Case Studies: (Case Study A in which no specific contact data is available; (Case Study B when different experimental data (e.g., site-directed mutagenesis, properties of the complex, NMR chemical shift perturbation mapping, electron tunneling on one of the partners is available; and (Case Study C when experimental data are available for both interacting surfaces, which are used during the search and/or evaluation stage of the docking. This algorithm has been extensively used, evidencing its usefulness in a wide range of different biological research fields.

  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. Protein-gold nanoparticle interactions and their possible impact on biomedical applications

    DEFF Research Database (Denmark)

    Liu, Jingying; Peng, Qiang

    2017-01-01

    ) have critically affected physiological to therapeutic responses. The complexity and uncontrollability of AuNP-PC formation limited the clinical applications of AuNP, e.g. AuNP-based drug delivery systems or imaging agent. Thus, even intensive attempts have been made for in vitro characterizations of PC...... a detailed description of protein-AuNP interaction and launch an interesting discussion on how to use such interaction for smart and controlled AuNPs delivery, which would be a topic of widespread interest.......In the past few years, concerns of protein-gold nanoparticles (AuNP) interaction have been continuously growing in numerous potential biomedical applications. Despite the advances in tunable size, shape and excellent biocompatibility, unpredictable adverse effects related with protein corona (PC...

  11. HCVpro: Hepatitis C virus protein interaction database

    KAUST Repository

    Kwofie, Samuel K.

    2011-12-01

    It is essential to catalog characterized hepatitis C virus (HCV) protein-protein interaction (PPI) data and the associated plethora of vital functional information to augment the search for therapies, vaccines and diagnostic biomarkers. In furtherance of these goals, we have developed the hepatitis C virus protein interaction database (HCVpro) by integrating manually verified hepatitis C virus-virus and virus-human protein interactions curated from literature and databases. HCVpro is a comprehensive and integrated HCV-specific knowledgebase housing consolidated information on PPIs, functional genomics and molecular data obtained from a variety of virus databases (VirHostNet, VirusMint, HCVdb and euHCVdb), and from BIND and other relevant biology repositories. HCVpro is further populated with information on hepatocellular carcinoma (HCC) related genes that are mapped onto their encoded cellular proteins. Incorporated proteins have been mapped onto Gene Ontologies, canonical pathways, Online Mendelian Inheritance in Man (OMIM) and extensively cross-referenced to other essential annotations. The database is enriched with exhaustive reviews on structure and functions of HCV proteins, current state of drug and vaccine development and links to recommended journal articles. Users can query the database using specific protein identifiers (IDs), chromosomal locations of a gene, interaction detection methods, indexed PubMed sources as well as HCVpro, BIND and VirusMint IDs. The use of HCVpro is free and the resource can be accessed via http://apps.sanbi.ac.za/hcvpro/ or http://cbrc.kaust.edu.sa/hcvpro/. © 2011 Elsevier B.V.

  12. Structural study of surfactant-dependent interaction with protein

    Energy Technology Data Exchange (ETDEWEB)

    Mehan, Sumit; Aswal, Vinod K., E-mail: vkaswal@barc.gov.in [Solid State Physics Division, Bhabha Atomic Research Centre, Mumbai 400 085 (India); Kohlbrecher, Joachim [Laboratory for Neutron Scattering, Paul Scherrer Institut, CH-5232 PSI Villigen (Switzerland)

    2015-06-24

    Small-angle neutron scattering (SANS) has been used to study the complex structure of anionic BSA protein with three different (cationic DTAB, anionic SDS and non-ionic C12E10) surfactants. These systems form very different surfactant-dependent complexes. We show that the structure of protein-surfactant complex is initiated by the site-specific electrostatic interaction between the components, followed by the hydrophobic interaction at high surfactant concentrations. It is also found that hydrophobic interaction is preferred over the electrostatic interaction in deciding the resultant structure of protein-surfactant complexes.

  13. A lock-and-key model for protein–protein interactions

    OpenAIRE

    Morrison, Julie L.; Breitling, Rainer; Higham, Desmond J.; Gilbert, David R.

    2006-01-01

    Motivation: Protein–protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organism’s proteome, as well as detailed information on specific interactions. Here we suggest a physical model of protein interactions that can be used to extract additional information at an intermediate level: It enables us to identify proteins which share biological interaction motifs,...

  14. Protein-protein interaction site predictions with minimum covariance determinant and Mahalanobis distance.

    Science.gov (United States)

    Qiu, Zhijun; Zhou, Bo; Yuan, Jiangfeng

    2017-11-21

    Protein-protein interaction site (PPIS) prediction must deal with the diversity of interaction sites that limits their prediction accuracy. Use of proteins with unknown or unidentified interactions can also lead to missing interfaces. Such data errors are often brought into the training dataset. In response to these two problems, we used the minimum covariance determinant (MCD) method to refine the training data to build a predictor with better performance, utilizing its ability of removing outliers. In order to predict test data in practice, a method based on Mahalanobis distance was devised to select proper test data as input for the predictor. With leave-one-validation and independent test, after the Mahalanobis distance screening, our method achieved higher performance according to Matthews correlation coefficient (MCC), although only a part of test data could be predicted. These results indicate that data refinement is an efficient approach to improve protein-protein interaction site prediction. By further optimizing our method, it is hopeful to develop predictors of better performance and wide range of application. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Epitope mapping of imidazolium cations in ionic liquid-protein interactions unveils the balance between hydrophobicity and electrostatics towards protein destabilisation.

    Science.gov (United States)

    Silva, Micael; Figueiredo, Angelo Miguel; Cabrita, Eurico J

    2014-11-14

    We investigated imidazolium-based ionic liquid (IL) interactions with human serum albumin (HSA) to discern the level of cation interactions towards protein stability. STD-NMR spectroscopy was used to observe the imidazolium IL protons involved in direct binding and to identify the interactions responsible for changes in Tm as accessed by differential scanning calorimetry (DSC). Cations influence protein stability less than anions but still significantly. It was found that longer alkyl side chains of imidazolium-based ILs (more hydrophobic) are associated with a higher destabilisation effect on HSA than short-alkyl groups (less hydrophobic). The reason for such destabilisation lies on the increased surface contact area of the cation with the protein, particularly on the hydrophobic contacts promoted by the terminus of the alkyl chain. The relevance of the hydrophobic contacts is clearly demonstrated by the introduction of a polar moiety in the alkyl chain: a methoxy or alcohol group. Such structural modification reduces the degree of hydrophobic contacts with HSA explaining the lesser extent of protein destabilisation when compared to longer alkyl side chain groups: above [C2mim](+). Competition STD-NMR experiments using [C2mim](+), [C4mim](+) and [C2OHmim](+) also validate the importance of the hydrophobic interactions. The combined effect of cation and anion interactions was explored using (35)Cl NMR. Such experiments show that the nature of the cation has no influence on the anion-protein contacts, still the nature of the anion modulates the cation-protein interaction. Herein we propose that more destabilising anions are likely to be a result of a partial contribution from the cation as a direct consequence of the different levels of interaction (cation-anion pair and cation-protein).

  16. PerturbationAnalyzer: a tool for investigating the effects of concentration perturbation on protein interaction networks.

    Science.gov (United States)

    Li, Fei; Li, Peng; Xu, Wenjian; Peng, Yuxing; Bo, Xiaochen; Wang, Shengqi

    2010-01-15

    The propagation of perturbations in protein concentration through a protein interaction network (PIN) can shed light on network dynamics and function. In order to facilitate this type of study, PerturbationAnalyzer, which is an open source plugin for Cytoscape, has been developed. PerturbationAnalyzer can be used in manual mode for simulating user-defined perturbations, as well as in batch mode for evaluating network robustness and identifying significant proteins that cause large propagation effects in the PINs when their concentrations are perturbed. Results from PerturbationAnalyzer can be represented in an intuitive and customizable way and can also be exported for further exploration. PerturbationAnalyzer has great potential in mining the design principles of protein networks, and may be a useful tool for identifying drug targets. PerturbationAnalyzer can be accessed from the Cytoscape web site http://www.cytoscape.org/plugins/index.php or http://biotech.bmi.ac.cn/PerturbationAnalyzer. Supplementary data are available at Bioinformatics online.

  17. Quantitative analysis of protein-ligand interactions by NMR.

    Science.gov (United States)

    Furukawa, Ayako; Konuma, Tsuyoshi; Yanaka, Saeko; Sugase, Kenji

    2016-08-01

    Protein-ligand interactions have been commonly studied through static structures of the protein-ligand complex. Recently, however, there has been increasing interest in investigating the dynamics of protein-ligand interactions both for fundamental understanding of the underlying mechanisms and for drug development. NMR is a versatile and powerful tool, especially because it provides site-specific quantitative information. NMR has widely been used to determine the dissociation constant (KD), in particular, for relatively weak interactions. The simplest NMR method is a chemical-shift titration experiment, in which the chemical-shift changes of a protein in response to ligand titration are measured. There are other quantitative NMR methods, but they mostly apply only to interactions in the fast-exchange regime. These methods derive the dissociation constant from population-averaged NMR quantities of the free and bound states of a protein or ligand. In contrast, the recent advent of new relaxation-based experiments, including R2 relaxation dispersion and ZZ-exchange, has enabled us to obtain kinetic information on protein-ligand interactions in the intermediate- and slow-exchange regimes. Based on R2 dispersion or ZZ-exchange, methods that can determine the association rate, kon, dissociation rate, koff, and KD have been developed. In these approaches, R2 dispersion or ZZ-exchange curves are measured for multiple samples with different protein and/or ligand concentration ratios, and the relaxation data are fitted to theoretical kinetic models. It is critical to choose an appropriate kinetic model, such as the two- or three-state exchange model, to derive the correct kinetic information. The R2 dispersion and ZZ-exchange methods are suitable for the analysis of protein-ligand interactions with a micromolar or sub-micromolar dissociation constant but not for very weak interactions, which are typical in very fast exchange. This contrasts with the NMR methods that are used

  18. 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......) 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...

  19. Interaction between La(III) and proteins on the plasma membrane of horseradish

    Science.gov (United States)

    Yang, Guang-Mei; Chu, Yun-Xia; Lv, Xiao-Fen; Zhou, Qing; Huang, Xiao-Hua

    2012-06-01

    Lanthanum (La) is an important rare earth element in the ecological environment of plant. The proteins on the plasma membrane control the transport of molecules into and out of cell. It is very important to investigate the effect of La(III) on the proteins on the plasma membrane in the plant cell. In the present work, the interaction between La(III) and proteins on the plasma membrane of horseradish was investigated using optimization of the fluorescence microscopy and fluorescence spectroscopy. It is found that the fluorescence of the complex system of protoplasts and 1-aniline Kenai-8-sulfonic acid in horseradish treated with the low concentration of La(III) is increased compared with that of the control horseradish. The opposite effect is observed in horseradish treated with the high concentration of La(III). These results indicated that the low concentration of La(III) can interact with the proteins on the plasma membrane of horseradish, causing the improvement in the structure of proteins on the plasma membrane. The high concentration of La(III) can also interact with the proteins on the plasma membrane of horseradish, leading to the destruction of the structure of proteins on the plasma membrane. We demonstrate that the proteins on the plasma membrane are the targets of La(III) action on plant cell.

  20. [Interaction of FABP4 with plasma membrane proteins of endothelial cells].

    Science.gov (United States)

    Saavedra, Paula; Girona, Josefa; Aragonès, Gemma; Cabré, Anna; Guaita, Sandra; Heras, Mercedes; Masana, Lluís

    2015-01-01

    Fatty acid binding protein (FABP4) is an adipose tissue-secreted adipokine implicated in the regulation of the energetic metabolism and inflammation. High levels of circulating FABP4 have been described in people with obesity, atherogenic dyslipidemia, diabetes and metabolic syndrome. Recent studies have demonstrated that FABP4 could have a direct effect on peripheral tissues and, specifically, on vascular function. It is still unknown how the interaction between FABP4 and the endothelial cells is produced to prompt these effects on vascular function. The objective of this work is studying the interaction between FABP4 and the plasma membrane proteins of endothelial cells. HUVEC cells were incubated with and without FABP4 (100 ng/ml) for 5 minutes. Immunolocalization of FABP4 was studied by confocal microscopy. The results showed that FABP4 colocalizates with CD31, a membrane protein marker. A strategy which combines 6XHistidine-tag FABP4 (FABP4-His), incubations with or without FABP4-His (100 ng/ml), formaldehyde cross-linking, cellular membrane protein extraction and western blot, was designed to study the FABP4 interactions with membrane proteins of HUVECs. The results showed different western blot profiles depending of the incubation with or without FABP4-His. The immunoblot revelead three covalent protein complexes of about 108, 77 and 33 kDa containing FAPB4 and its putative receptor. The existence of a specific binding protein complex able to bind FABP4 to endothelial cells is supported by these results. The obtained results will permit us advance in the molecular knowledge of FABP4 effects as well as use this protein and its receptor as therapeutic target to prevent cardiovascular. Copyright © 2014 Sociedad Española de Arteriosclerosis. Published by Elsevier España. All rights reserved.

  1. 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.

  2. Protein annotation from protein interaction networks and Gene Ontology.

    Science.gov (United States)

    Nguyen, Cao D; Gardiner, Katheleen J; Cios, Krzysztof J

    2011-10-01

    We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precision and 60% recall versus 45% and 26% for Majority and 24% and 61% for χ²-statistics, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.

  3. iview: an interactive WebGL visualizer for protein-ligand complex.

    Science.gov (United States)

    Li, Hongjian; Leung, Kwong-Sak; Nakane, Takanori; Wong, Man-Hon

    2014-02-25

    Visualization of protein-ligand complex plays an important role in elaborating protein-ligand interactions and aiding novel drug design. Most existing web visualizers either rely on slow software rendering, or lack virtual reality support. The vital feature of macromolecular surface construction is also unavailable. We have developed iview, an easy-to-use interactive WebGL visualizer of protein-ligand complex. It exploits hardware acceleration rather than software rendering. It features three special effects in virtual reality settings, namely anaglyph, parallax barrier and oculus rift, resulting in visually appealing identification of intermolecular interactions. It supports four surface representations including Van der Waals surface, solvent excluded surface, solvent accessible surface and molecular surface. Moreover, based on the feature-rich version of iview, we have also developed a neat and tailor-made version specifically for our istar web platform for protein-ligand docking purpose. This demonstrates the excellent portability of iview. Using innovative 3D techniques, we provide a user friendly visualizer that is not intended to compete with professional visualizers, but to enable easy accessibility and platform independence.

  4. Yeast Interacting Proteins Database: YDR176W, YDL239C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle pole...ining structure at the leading edge of the prospore membrane via interaction with spindle pole body componen...DY3 Prey description Protein required for spore wall formation, thought to mediate assembly of a Don1p-conta

  5. Coevolution of interacting fertilization proteins.

    Directory of Open Access Journals (Sweden)

    Nathaniel L Clark

    2009-07-01

    Full Text Available Reproductive proteins are among the fastest evolving in the proteome, often due to the consequences of positive selection, and their rapid evolution is frequently attributed to a coevolutionary process between interacting female and male proteins. Such a process could leave characteristic signatures at coevolving genes. One signature of coevolution, predicted by sexual selection theory, is an association of alleles between the two genes. Another predicted signature is a correlation of evolutionary rates during divergence due to compensatory evolution. We studied female-male coevolution in the abalone by resequencing sperm lysin and its interacting egg coat protein, VERL, in populations of two species. As predicted, we found intergenic linkage disequilibrium between lysin and VERL, despite our demonstration that they are not physically linked. This finding supports a central prediction of sexual selection using actual genotypes, that of an association between a male trait and its female preference locus. We also created a novel likelihood method to show that lysin and VERL have experienced correlated rates of evolution. These two signatures of coevolution can provide statistical rigor to hypotheses of coevolution and could be exploited for identifying coevolving proteins a priori. We also present polymorphism-based evidence for positive selection and implicate recent selective events at the specific structural regions of lysin and VERL responsible for their species-specific interaction. Finally, we observed deep subdivision between VERL alleles in one species, which matches a theoretical prediction of sexual conflict. Thus, abalone fertilization proteins illustrate how coevolution can lead to reproductive barriers and potentially drive speciation.

  6. The dynamic multisite interactions between two intrinsically disordered proteins

    KAUST Repository

    Wu, Shaowen

    2017-05-11

    Protein interactions involving intrinsically disordered proteins (IDPs) comprise a variety of binding modes, from the well characterized folding upon binding to dynamic fuzzy complex. To date, most studies concern the binding of an IDP to a structured protein, while the Interaction between two IDPs is poorly understood. In this study, we combined NMR, smFRET, and molecular dynamics (MD) simulation to characterize the interaction between two IDPs, the C-terminal domain (CTD) of protein 4.1G and the nuclear mitotic apparatus (NuMA) protein. It is revealed that CTD and NuMA form a fuzzy complex with remaining structural disorder. Multiple binding sites on both proteins were identified by MD and mutagenesis studies. Our study provides an atomic scenario in which two IDPs bearing multiple binding sites interact with each other in dynamic equilibrium. The combined approach employed here could be widely applicable for investigating IDPs and their dynamic interactions.

  7. Exact and Effective Pair-Wise Potential for Protein-Ligand Interactions Obtained from a Semiempirical Energy Partition

    Directory of Open Access Journals (Sweden)

    André Melo

    2008-09-01

    Full Text Available In this work, the partition method introduced by Carvalho and Melo was used to study the complex between Cucurbita maxima trypsin inhibitor (CMTI-I and glycerol at the AM1 level. An effective potential, combining non-bonding and polarization plus charge transfer (PLCT terms, was introduced to evaluate the magnitude of the interaction between each amino acid and the ligand. In this case study, the nonbonding–PLCT noncompensation characterizes the stabilization energy of the association process in study. The main residues (Gly29, Cys3 and Arg5 with net attractive effects and Arg1 (with a net repulsive effect, responsible by the stability of protein-ligand complex, are associated with large nonbonding energies non-compensated by PLCT effects. The results obtained enable us to conclude that the present decomposition scheme can be used for understanding the cohesive phenomena in proteins.

  8. Energy landscape of all-atom protein-protein interactions revealed by multiscale enhanced sampling.

    Directory of Open Access Journals (Sweden)

    Kei Moritsugu

    2014-10-01

    Full Text Available Protein-protein interactions are regulated by a subtle balance of complicated atomic interactions and solvation at the interface. To understand such an elusive phenomenon, it is necessary to thoroughly survey the large configurational space from the stable complex structure to the dissociated states using the all-atom model in explicit solvent and to delineate the energy landscape of protein-protein interactions. In this study, we carried out a multiscale enhanced sampling (MSES simulation of the formation of a barnase-barstar complex, which is a protein complex characterized by an extraordinary tight and fast binding, to determine the energy landscape of atomistic protein-protein interactions. The MSES adopts a multicopy and multiscale scheme to enable for the enhanced sampling of the all-atom model of large proteins including explicit solvent. During the 100-ns MSES simulation of the barnase-barstar system, we observed the association-dissociation processes of the atomistic protein complex in solution several times, which contained not only the native complex structure but also fully non-native configurations. The sampled distributions suggest that a large variety of non-native states went downhill to the stable complex structure, like a fast folding on a funnel-like potential. This funnel landscape is attributed to dominant configurations in the early stage of the association process characterized by near-native orientations, which will accelerate the native inter-molecular interactions. These configurations are guided mostly by the shape complementarity between barnase and barstar, and lead to the fast formation of the final complex structure along the downhill energy landscape.

  9. A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions

    Directory of Open Access Journals (Sweden)

    Mengqu Ge

    2016-02-01

    Full Text Available As one large class of non-coding RNAs (ncRNAs, long ncRNAs (lncRNAs have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI. LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR and protein-based collaborative filtering (ProCF. Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins.

  10. A lanthipeptide library used to identify a protein-protein interaction inhibitor.

    Science.gov (United States)

    Yang, Xiao; Lennard, Katherine R; He, Chang; Walker, Mark C; Ball, Andrew T; Doigneaux, Cyrielle; Tavassoli, Ali; van der Donk, Wilfred A

    2018-04-01

    In this article we describe the production and screening of a genetically encoded library of 10 6 lanthipeptides in Escherichia coli using the substrate-tolerant lanthipeptide synthetase ProcM. This plasmid-encoded library was combined with a bacterial reverse two-hybrid system for the interaction of the HIV p6 protein with the UEV domain of the human TSG101 protein, which is a critical protein-protein interaction for HIV budding from infected cells. Using this approach, we identified an inhibitor of this interaction from the lanthipeptide library, whose activity was verified in vitro and in cell-based virus-like particle-budding assays. Given the variety of lanthipeptide backbone scaffolds that may be produced with ProcM, this method may be used for the generation of genetically encoded libraries of natural product-like lanthipeptides containing substantial structural diversity. Such libraries may be combined with any cell-based assay to identify lanthipeptides with new biological activities.

  11. Feature generation and representations for protein-protein interaction classification.

    Science.gov (United States)

    Lan, Man; Tan, Chew Lim; Su, Jian

    2009-10-01

    Automatic detecting protein-protein interaction (PPI) relevant articles is a crucial step for large-scale biological database curation. The previous work adopted POS tagging, shallow parsing and sentence splitting techniques, but they achieved worse performance than the simple bag-of-words representation. In this paper, we generated and investigated multiple types of feature representations in order to further improve the performance of PPI text classification task. Besides the traditional domain-independent bag-of-words approach and the term weighting methods, we also explored other domain-dependent features, i.e. protein-protein interaction trigger keywords, protein named entities and the advanced ways of incorporating Natural Language Processing (NLP) output. The integration of these multiple features has been evaluated on the BioCreAtIvE II corpus. The experimental results showed that both the advanced way of using NLP output and the integration of bag-of-words and NLP output improved the performance of text classification. Specifically, in comparison with the best performance achieved in the BioCreAtIvE II IAS, the feature-level and classifier-level integration of multiple features improved the performance of classification 2.71% and 3.95%, respectively.

  12. Study of the interactions between riboflavin and protein

    International Nuclear Information System (INIS)

    Zhao Hongwei; Zhang Zhaoxia; Zhu Hongping; Ge Min; Miao Jinling; Wang Wenfeng; Yao Side

    2006-01-01

    Riboflavin (VB 2 ), a vitamin that is a natural constituent of living organisms, plays an important role in the process of metabolism and it is essential for normal cellular functions and growth. As an endogenous photosensitizer, riboflavin induces photooxidation damages to cells of skin and eye, causing inflammation, accelerated aging and mutation. The photodynamic actions of riboflavin on DNA have been studied extensively, however, its photodynamic actions on protein and enzyme are less studied due to the complex types and structures of proteins, and less attentions have been paid to photosensitive protein damages than DNA. In our lab, the interactions between riboflavin and serum albumin and lysozyme have been carried out by using transient absorption spectrometry, emission spectrometer analysis and electrophoresis techniques. It was found that stable state products and transient process of photosensitive damage to proteins were closely relative to concentration of riboflavin, time of irradiation and the atmosphere of the solutions. The results indicates that proteins can be photosensitive damaged by riboflavin irradiated by UV lights. Riboflavin can quench the intrinsic fluorescence of protein. Both dynamic and static quenching are simultaneous involved. The binding constants, the kinetics and spectroscopic properties of riboflavin interaction with albumin and lysozyme have also been investigated. Mechanisms of the photosensitive damages to proteins were discussed. The experiments also indicated that antioxidants such as melatonin and chlorogenic acid can reduce the damage of lysozyme effectively. (authors)

  13. Reciprocal carbonyl-carbonyl interactions in small molecules and proteins.

    Science.gov (United States)

    Rahim, Abdur; Saha, Pinaki; Jha, Kunal Kumar; Sukumar, Nagamani; Sarma, Bani Kanta

    2017-07-19

    Carbonyl-carbonyl n→π* interactions where a lone pair (n) of the oxygen atom of a carbonyl group is delocalized over the π* orbital of a nearby carbonyl group have attracted a lot of attention in recent years due to their ability to affect the 3D structure of small molecules, polyesters, peptides, and proteins. In this paper, we report the discovery of a "reciprocal" carbonyl-carbonyl interaction with substantial back and forth n→π* and π→π* electron delocalization between neighboring carbonyl groups. We have carried out experimental studies, analyses of crystallographic databases and theoretical calculations to show the presence of this interaction in both small molecules and proteins. In proteins, these interactions are primarily found in polyproline II (PPII) helices. As PPII are the most abundant secondary structures in unfolded proteins, we propose that these local interactions may have implications in protein folding.Carbonyl-carbonyl π* non covalent interactions affect the structure and stability of small molecules and proteins. Here, the authors carry out experimental studies, analyses of crystallographic databases and theoretical calculations to describe an additional type of carbonyl-carbonyl interaction.

  14. Mechanisms and Effects on HBV Replication of the Interaction between HBV Core Protein and Cellular Filamin B.

    Science.gov (United States)

    Li, Yilin; Sun, Yishuang; Sun, Fuyun; Hua, Rong; Li, Chenlin; Chen, Lang; Guo, Deyin; Mu, Jingfang

    2018-03-28

    Hepatitis B virus (HBV) infection is one of the major problems that threatens global health. There have been many studies on HBV, but the relationship between HBV and host factors is largely unexplored and more studies are needed to clarify these interactions. Filamin B is an actin-binding protein that acts as a cytoskeleton protein, and it is involved in cell development and several signaling pathways. In this study, we showed that filamin B interacted with HBV core protein, and the interaction promoted HBV replication. The interaction between filamin B and core protein was observed in HEK 293T, Huh7 and HepG2 cell lines by co-immunoprecipitation and co-localization immnofluoresence. Overexpression of filamin B increased the levels of HBV total RNAs and pre-genome RNA (pgRNA), and improved the secretion level of hepatitis B surface antigen (HBsAg) and hepatitis B e antigen (HBeAg). In contrast, filamin B knockdown inhibited HBV replication, decreased the level of HBV total RNAs and pgRNA, and reduced the secretion level of HBsAg and HBeAg. In addition, we found that filamin B and core protein may interact with each other via four blocks of argentine residues at the C-terminus of core protein. In conclusion, we identify filamin B as a novel host factor that can interact with core protein to promote HBV replication in hepatocytes. Our study provides new insights into the relationship between HBV and host factors and may provide new strategies for the treatment of HBV infection.

  15. Distinct Mechanism Evolved for Mycobacterial RNA Polymerase and Topoisomerase I Protein-Protein Interaction.

    Science.gov (United States)

    Banda, Srikanth; Cao, Nan; Tse-Dinh, Yuk-Ching

    2017-09-15

    We report here a distinct mechanism of interaction between topoisomerase I and RNA polymerase in Mycobacterium tuberculosis and Mycobacterium smegmatis that has evolved independently from the previously characterized interaction between bacterial topoisomerase I and RNA polymerase. Bacterial DNA topoisomerase I is responsible for preventing the hyper-negative supercoiling of genomic DNA. The association of topoisomerase I with RNA polymerase during transcription elongation could efficiently relieve transcription-driven negative supercoiling. Our results demonstrate a direct physical interaction between the C-terminal domains of topoisomerase I (TopoI-CTDs) and the β' subunit of RNA polymerase of M. smegmatis in the absence of DNA. The TopoI-CTDs in mycobacteria are evolutionarily unrelated in amino acid sequence and three-dimensional structure to the TopoI-CTD found in the majority of bacterial species outside Actinobacteria, including Escherichia coli. The functional interaction between topoisomerase I and RNA polymerase has evolved independently in mycobacteria and E. coli, with distinctively different structural elements of TopoI-CTD utilized for this protein-protein interaction. Zinc ribbon motifs in E. coli TopoI-CTD are involved in the interaction with RNA polymerase. For M. smegmatis TopoI-CTD, a 27-amino-acid tail that is rich in basic residues at the C-terminal end is responsible for the interaction with RNA polymerase. Overexpression of recombinant TopoI-CTD in M. smegmatis competed with the endogenous topoisomerase I for protein-protein interactions with RNA polymerase. The TopoI-CTD overexpression resulted in decreased survival following treatment with antibiotics and hydrogen peroxide, supporting the importance of the protein-protein interaction between topoisomerase I and RNA polymerase during stress response of mycobacteria. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. BRCA1 interacts directly with the Fanconi anemia protein FANCA.

    Science.gov (United States)

    Folias, Alexandra; Matkovic, Mara; Bruun, Donald; Reid, Sonja; Hejna, James; Grompe, Markus; D'Andrea, Alan; Moses, Robb

    2002-10-01

    Fanconi anemia (FA) is a rare autosomal recessive disease characterized by skeletal defects, anemia, chromosomal instability and increased risk of leukemia. At the cellular level FA is characterized by increased sensitivity to agents forming interstrand crosslinks (ICL) in DNA. Six FA genes have been cloned and interactions among individual FANC proteins have been found. The FANCD2 protein co-localizes in nuclear foci with the BRCA1 protein following DNA damage and during S-phase, requiring the FANCA, C, E and G proteins to do so. This finding may reflect a direct role for the BRCA1 protein in double strand break (DSB) repair and interaction with the FANC proteins. Therefore interactions between BRCA1 and the FANC proteins were investigated. Among the known FANC proteins, we find evidence for direct interaction only between the FANCA protein and BRCA1. The evidence rests on three different tests: yeast two-hybrid analysis, coimmunoprecipitation from in vitro synthesis, and coimmunoprecipitation from cell extracts. The amino terminal portion of FANCA and the central part (aa 740-1083) of BRCA1 contain the sites of interaction. The interaction does not depend on DNA damage, thus FANCA and BRCA1 are constitutively interacting. The demonstrated interaction directly connects BRCA1 to the FA pathway of DNA repair.

  17. Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information.

    Science.gov (United States)

    An, Ji-Yong; Zhang, Lei; Zhou, Yong; Zhao, Yu-Jun; Wang, Da-Fu

    2017-08-18

    Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM-LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM-LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .

  18. A Versatile Platform to Analyze Low-Affinity and Transient Protein-Protein Interactions in Living Cells in Real Time

    Directory of Open Access Journals (Sweden)

    Yao-Cheng Li

    2014-12-01

    Full Text Available Summary: Protein-protein interactions (PPIs play central roles in orchestrating biological processes. While some PPIs are stable, many important ones are transient and hard to detect with conventional approaches. We developed ReBiL, a recombinase enhanced bimolecular luciferase complementation platform, to enable detection of weak PPIs in living cells. ReBiL readily identified challenging transient interactions between an E3 ubiquitin ligase and an E2 ubiquitin-conjugating enzyme. ReBiL’s ability to rapidly interrogate PPIs in diverse conditions revealed that some stapled α-helical peptides, a class of PPI antagonists, induce target-independent cytosolic leakage and cytotoxicity that is antagonized by serum. These results explain the requirement for serum-free conditions to detect stapled peptide activity, and define a required parameter to evaluate for peptide antagonist approaches. ReBiL’s ability to expedite PPI analysis, assess target specificity and cell permeability, and reveal off-target effects of PPI modifiers should facilitate the development of effective, cell-permeable PPI therapeutics and the elaboration of diverse biological mechanisms. : Li et al. developed a recombinase-enhanced bimolecular luciferase complementation platform, termed ReBiL, to evaluate low-affinity protein-protein interactions (PPIs that are not detectable by other methods and to analyze PPI antagonists in living cells. ReBiL showed that small-molecule p53-Mdm2 antagonists disrupt their intended targets effectively in cells, whereas stapled peptides did not. Stapled peptides unexpectedly induced cell membrane disruption resulting in p53-independent death associated with cytoplasmic leakage. ReBiL is also valuable for high-throughput screening and for deciphering signaling mechanisms mediated by protein interactions.

  19. Analysis of Protein-RNA and Protein-Peptide Interactions in Equine Infectious Anemia

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jae-Hyung [Iowa State Univ., Ames, IA (United States)

    2007-01-01

    Macromolecular interactions are essential for virtually all cellular functions including signal transduction processes, metabolic processes, regulation of gene expression and immune responses. This dissertation focuses on the characterization of two important macromolecular interactions involved in the relationship between Equine Infectious Anemia Virus (EIAV) and its host cell in horse: (1) the interaction between the EIAV Rev protein and its binding site, the Rev-responsive element (RRE) and (2) interactions between equine MHC class I molecules and epitope peptides derived from EIAV proteins. EIAV, one of the most divergent members of the lentivirus family, has a single-stranded RNA genome and carries several regulatory and structural proteins within its viral particle. Rev is an essential EIAV regulatory encoded protein that interacts with the viral RRE, a specific binding site in the viral mRNA. Using a combination of experimental and computational methods, the interactions between EIAV Rev and RRE were characterized in detail. EIAV Rev was shown to have a bipartite RNA binding domain contain two arginine rich motifs (ARMs). The RRE secondary structure was determined and specific structural motifs that act as cis-regulatory elements for EIAV Rev-RRE interaction were identified. Interestingly, a structural motif located in the high affinity Rev binding site is well conserved in several diverse lentiviral genoes, including HIV-1. Macromolecular interactions involved in the immune response of the horse to EIAV infection were investigated by analyzing complexes between MHC class I proteins and epitope peptides derived from EIAV Rev, Env and Gag proteins. Computational modeling results provided a mechanistic explanation for the experimental finding that a single amino acid change in the peptide binding domain of the quine MHC class I molecule differentially affectes the recognitino of specific epitopes by EIAV-specific CTL. Together, the findings in this

  20. Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis.

    Science.gov (United States)

    Holland, David O; Johnson, Margaret E

    2018-03-01

    Stoichiometric balance, or dosage balance, implies that proteins that are subunits of obligate complexes (e.g. the ribosome) should have copy numbers expressed to match their stoichiometry in that complex. Establishing balance (or imbalance) is an important tool for inferring subunit function and assembly bottlenecks. We show here that these correlations in protein copy numbers can extend beyond complex subunits to larger protein-protein interactions networks (PPIN) involving a range of reversible binding interactions. We develop a simple method for quantifying balance in any interface-resolved PPINs based on network structure and experimentally observed protein copy numbers. By analyzing such a network for the clathrin-mediated endocytosis (CME) system in yeast, we found that the real protein copy numbers were significantly more balanced in relation to their binding partners compared to randomly sampled sets of yeast copy numbers. The observed balance is not perfect, highlighting both under and overexpressed proteins. We evaluate the potential cost and benefits of imbalance using two criteria. First, a potential cost to imbalance is that 'leftover' proteins without remaining functional partners are free to misinteract. We systematically quantify how this misinteraction cost is most dangerous for strong-binding protein interactions and for network topologies observed in biological PPINs. Second, a more direct consequence of imbalance is that the formation of specific functional complexes depends on relative copy numbers. We therefore construct simple kinetic models of two sub-networks in the CME network to assess multi-protein assembly of the ARP2/3 complex and a minimal, nine-protein clathrin-coated vesicle forming module. We find that the observed, imperfectly balanced copy numbers are less effective than balanced copy numbers in producing fast and complete multi-protein assemblies. However, we speculate that strategic imbalance in the vesicle forming module

  1. Identification of proteins that may directly interact with human RPA.

    Science.gov (United States)

    Nakaya, Ryou; Takaya, Junichiro; Onuki, Takeshi; Moritani, Mariko; Nozaki, Naohito; Ishimi, Yukio

    2010-11-01

    RPA, which consisted of three subunits (RPA1, 2 and 3), plays essential roles in DNA transactions. At the DNA replication forks, RPA binds to single-stranded DNA region to stabilize the structure and to assemble other replication proteins. Interactions between RPA and several replication proteins have been reported but the analysis is not comprehensive. We systematically performed the qualitative analysis to identify RPA interaction partners to understand the protein-protein interaction at the replication forks. We expressed in insect cells the three subunits of human RPA, together with one replication protein, which is present at the forks under normal conditions and/or under the replication stress conditions, to examine the interaction. Among 30 proteins examined in total, it was found that at least 14 proteins interacted with RPA. RPA interacted with MCM3-7, MCM-BP and CDC45 proteins among the proteins that play roles in the initiation and the elongation of the DNA replication. RPA bound with TIPIN, CLASPIN and RAD17, which are involved in the DNA replication checkpoint functions. RPA also bound with cyclin-dependent kinases and an amino-terminal fragment of Rb protein that negatively regulates DNA replication. These results suggest that RPA interacts with the specific proteins among those that play roles in the regulation of the replication fork progression.

  2. 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. 

  3. PIWI Proteins and PIWI-Interacting RNA

    DEFF Research Database (Denmark)

    Han, Yi Neng; Li, Yuan; Xia, Sheng Qiang

    2017-01-01

    tissue types as well and play important roles in transposon silencing, epigenetic regulation, gene and protein regulation, genome rearrangement, spermatogenesis and germ stem-cell maintenance. PIWI proteins were first discovered in Drosophila and they play roles in spermatogenesis, germline stem-cell......P-Element induced wimpy testis (PIWI)-interacting RNAs (piRNAs) are a type of noncoding RNAs (ncRNAs) and interact with PIWI proteins. piRNAs were primarily described in the germline, but emerging evidence revealed that piRNAs are expressed in a tissue-specific manner among multiple human somatic...... maintenance, self-renewal, retrotransposons silencing and the male germline mobility control. A growing number of studies have demonstrated that several piRNA and PIWI proteins are aberrantly expressed in various kinds of cancers and may probably serve as a novel biomarker and therapeutic target for cancer...

  4. The coat protein complex II, COPII, protein Sec13 directly interacts with presenilin-1

    International Nuclear Information System (INIS)

    Nielsen, Anders Lade

    2009-01-01

    Mutations in the human gene encoding presenilin-1, PS1, account for most cases of early-onset familial Alzheimer's disease. PS1 has nine transmembrane domains and a large loop orientated towards the cytoplasm. PS1 locates to cellular compartments as endoplasmic reticulum (ER), Golgi apparatus, vesicular structures, and plasma membrane, and is an integral member of γ-secretase, a protein protease complex with specificity for intra-membranous cleavage of substrates such as β-amyloid precursor protein. Here, an interaction between PS1 and the Sec13 protein is described. Sec13 takes part in coat protein complex II, COPII, vesicular trafficking, nuclear pore function, and ER directed protein sequestering and degradation control. The interaction maps to the N-terminal part of the large hydrophilic PS1 loop and the first of the six WD40-repeats present in Sec13. The identified Sec13 interaction to PS1 is a new candidate interaction for linking PS1 to secretory and protein degrading vesicular circuits.

  5. The coat protein complex II, COPII, protein Sec13 directly interacts with presenilin-1

    Energy Technology Data Exchange (ETDEWEB)

    Nielsen, Anders Lade, E-mail: aln@humgen.au.dk [Department of Human Genetics, The Bartholin Building, University of Aarhus, DK-8000 Aarhus C (Denmark)

    2009-10-23

    Mutations in the human gene encoding presenilin-1, PS1, account for most cases of early-onset familial Alzheimer's disease. PS1 has nine transmembrane domains and a large loop orientated towards the cytoplasm. PS1 locates to cellular compartments as endoplasmic reticulum (ER), Golgi apparatus, vesicular structures, and plasma membrane, and is an integral member of {gamma}-secretase, a protein protease complex with specificity for intra-membranous cleavage of substrates such as {beta}-amyloid precursor protein. Here, an interaction between PS1 and the Sec13 protein is described. Sec13 takes part in coat protein complex II, COPII, vesicular trafficking, nuclear pore function, and ER directed protein sequestering and degradation control. The interaction maps to the N-terminal part of the large hydrophilic PS1 loop and the first of the six WD40-repeats present in Sec13. The identified Sec13 interaction to PS1 is a new candidate interaction for linking PS1 to secretory and protein degrading vesicular circuits.

  6. Strong Ligand-Protein Interactions Derived from Diffuse Ligand Interactions with Loose Binding Sites.

    Science.gov (United States)

    Marsh, Lorraine

    2015-01-01

    Many systems in biology rely on binding of ligands to target proteins in a single high-affinity conformation with a favorable ΔG. Alternatively, interactions of ligands with protein regions that allow diffuse binding, distributed over multiple sites and conformations, can exhibit favorable ΔG because of their higher entropy. Diffuse binding may be biologically important for multidrug transporters and carrier proteins. A fine-grained computational method for numerical integration of total binding ΔG arising from diffuse regional interaction of a ligand in multiple conformations using a Markov Chain Monte Carlo (MCMC) approach is presented. This method yields a metric that quantifies the influence on overall ligand affinity of ligand binding to multiple, distinct sites within a protein binding region. This metric is essentially a measure of dispersion in equilibrium ligand binding and depends on both the number of potential sites of interaction and the distribution of their individual predicted affinities. Analysis of test cases indicates that, for some ligand/protein pairs involving transporters and carrier proteins, diffuse binding contributes greatly to total affinity, whereas in other cases the influence is modest. This approach may be useful for studying situations where "nonspecific" interactions contribute to biological function.

  7. Impact of hydrodynamic interactions on protein folding rates depends on temperature

    Science.gov (United States)

    Zegarra, Fabio C.; Homouz, Dirar; Eliaz, Yossi; Gasic, Andrei G.; Cheung, Margaret S.

    2018-03-01

    We investigated the impact of hydrodynamic interactions (HI) on protein folding using a coarse-grained model. The extent of the impact of hydrodynamic interactions, whether it accelerates, retards, or has no effect on protein folding, has been controversial. Together with a theoretical framework of the energy landscape theory (ELT) for protein folding that describes the dynamics of the collective motion with a single reaction coordinate across a folding barrier, we compared the kinetic effects of HI on the folding rates of two protein models that use a chain of single beads with distinctive topologies: a 64-residue α /β chymotrypsin inhibitor 2 (CI2) protein, and a 57-residue β -barrel α -spectrin Src-homology 3 domain (SH3) protein. When comparing the protein folding kinetics simulated with Brownian dynamics in the presence of HI to that in the absence of HI, we find that the effect of HI on protein folding appears to have a "crossover" behavior about the folding temperature. This means that at a temperature greater than the folding temperature, the enhanced friction from the hydrodynamic solvents between the beads in an unfolded configuration results in lowered folding rate; conversely, at a temperature lower than the folding temperature, HI accelerates folding by the backflow of solvent toward the folded configuration of a protein. Additionally, the extent of acceleration depends on the topology of a protein: for a protein like CI2, where its folding nucleus is rather diffuse in a transition state, HI channels the formation of contacts by favoring a major folding pathway in a complex free energy landscape, thus accelerating folding. For a protein like SH3, where its folding nucleus is already specific and less diffuse, HI matters less at a temperature lower than the folding temperature. Our findings provide further theoretical insight to protein folding kinetic experiments and simulations.

  8. Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.

    Science.gov (United States)

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

    2015-10-01

    Protein-protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.

  9. An antiviral disulfide compound blocks interaction between arenavirus Z protein and cellular promyelocytic leukemia protein

    International Nuclear Information System (INIS)

    Garcia, C.C.; Topisirovic, I.; Djavani, M.; Borden, K.L.B.; Damonte, E.B.; Salvato, M.S.

    2010-01-01

    The promyelocytic leukemia protein (PML) forms nuclear bodies (NB) that can be redistributed by virus infection. In particular, lymphocytic choriomeningitis virus (LCMV) influences disruption of PML NB through the interaction of PML with the arenaviral Z protein. In a previous report, we have shown that the disulfide compound NSC20625 has antiviral and virucidal properties against arenaviruses, inducing unfolding and oligomerization of Z without affecting cellular RING-containing proteins such as the PML. Here, we further studied the effect of the zinc-finger-reactive disulfide NSC20625 on PML-Z interaction. In HepG2 cells infected with LCMV or transiently transfected with Z protein constructs, treatment with NSC20625 restored PML distribution from a diffuse-cytoplasmic pattern to punctate, discrete NB which appeared identical to NB found in control, uninfected cells. Similar results were obtained in cells transfected with a construct expressing a Z mutant in zinc-binding site 2 of the RING domain, confirming that this Z-PML interaction requires the integrity of only one zinc-binding site. Altogether, these results show that the compound NSC20625 suppressed Z-mediated PML NB disruption and may be used as a tool for designing novel antiviral strategies against arenavirus infection.

  10. Super-resolution imaging and tracking of protein-protein interactions in sub-diffraction cellular space

    Science.gov (United States)

    Liu, Zhen; Xing, Dong; Su, Qian Peter; Zhu, Yun; Zhang, Jiamei; Kong, Xinyu; Xue, Boxin; Wang, Sheng; Sun, Hao; Tao, Yile; Sun, Yujie

    2014-07-01

    Imaging the location and dynamics of individual interacting protein pairs is essential but often difficult because of the fluorescent background from other paired and non-paired molecules, particularly in the sub-diffraction cellular space. Here we develop a new method combining bimolecular fluorescence complementation and photoactivated localization microscopy for super-resolution imaging and single-molecule tracking of specific protein-protein interactions. The method is used to study the interaction of two abundant proteins, MreB and EF-Tu, in Escherichia coli cells. The super-resolution imaging shows interesting distribution and domain sizes of interacting MreB-EF-Tu pairs as a subpopulation of total EF-Tu. The single-molecule tracking of MreB, EF-Tu and MreB-EF-Tu pairs reveals intriguing localization-dependent heterogonous dynamics and provides valuable insights to understanding the roles of MreB-EF-Tu interactions.

  11. 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.

  12. 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 em...

  13. Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data.

    Science.gov (United States)

    Fu, Changhe; Deng, Su; Jin, Guangxu; Wang, Xinxin; Yu, Zu-Guo

    2017-09-21

    Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously

  14. A Physical Interaction Network of Dengue Virus and Human Proteins*

    Science.gov (United States)

    Khadka, Sudip; Vangeloff, Abbey D.; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S.; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J.; Perera, Rushika; LaCount, Douglas J.

    2011-01-01

    Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection. PMID:21911577

  15. A physical interaction network of dengue virus and human proteins.

    Science.gov (United States)

    Khadka, Sudip; Vangeloff, Abbey D; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J; Perera, Rushika; LaCount, Douglas J

    2011-12-01

    Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection.

  16. 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.

  17. An ensemble self-training protein interaction article classifier.

    Science.gov (United States)

    Chen, Yifei; Hou, Ping; Manderick, Bernard

    2014-01-01

    Protein-protein interaction (PPI) is essential to understand the fundamental processes governing cell biology. The mining and curation of PPI knowledge are critical for analyzing proteomics data. Hence it is desired to classify articles PPI-related or not automatically. In order to build interaction article classification systems, an annotated corpus is needed. However, it is usually the case that only a small number of labeled articles can be obtained manually. Meanwhile, a large number of unlabeled articles are available. By combining ensemble learning and semi-supervised self-training, an ensemble self-training interaction classifier called EST_IACer is designed to classify PPI-related articles based on a small number of labeled articles and a large number of unlabeled articles. A biological background based feature weighting strategy is extended using the category information from both labeled and unlabeled data. Moreover, a heuristic constraint is put forward to select optimal instances from unlabeled data to improve the performance further. Experiment results show that the EST_IACer can classify the PPI related articles effectively and efficiently.

  18. Yeast Interacting Proteins Database: YDL239C, YDR273W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p...it as prey (1) YDR273W DON1 Meiosis-specific component of the spindle pole body, part of the leading... edge protein (LEP) coat, forms a ring-like structure at the leading edge of the prospore...ption Protein required for spore wall formation, thought to mediate assembly of a Don1p-containing structure at the leading...description Meiosis-specific component of the spindle pole body, part of the leading edge protein (LEP) coat

  19. Effect of protein-surfactant interactions on aggregation of β-lactoglobulin.

    Science.gov (United States)

    Hansted, Jon G; Wejse, Peter L; Bertelsen, Hans; Otzen, Daniel E

    2011-05-01

    The milk protein β-lactoglobulin (βLG) dominates the properties of whey aggregates in food products. Here we use spectroscopic and calorimetric techniques to elucidate how anionic, cationic and non-ionic surfactants interact with bovine βLG and modulate its heat-induced aggregation. Alkyl trimethyl ammonium chlorides (xTAC) strongly promote aggregation, while sodium alkyl sulfates (SxS) and alkyl maltopyranosides (xM) reduce aggregation. Sodium dodecyl sulfate (SDS) binds to non-aggregated βLG in several steps, but reduction of aggregation was associated with the first binding step, which occurs far below the critical micelle concentration. In contrast, micellar concentrations of xMs are required to reduce aggregation. The ranking order for reduction of aggregation (normalized to their tendency to self-associate) was C10-C12>C8>C14 for SxS and C8>C10>C12>C14>C16 for xM. xTAC promote aggregation in the same ranking order as xM reduce it. We conclude that SxS reduce aggregation by stabilizing the protein's ligand-bound state (the melting temperature t(m) increases by up to 10°C) and altering its charge potential. xM monomers also stabilize the protein's ligand-bound state (increasing t(m) up to 6°C) but in the absence of charged head groups this is not sufficient by itself to prevent aggregation. Although micelles of both anionic and non-ionic surfactants destabilize βLG, they also solubilize unfolded protein monomers, leaving them unavailable for protein-protein association and thus inhibiting aggregation. Cationic surfactants promote aggregation by a combination of destabilization and charge neutralization. The food compatible surfactant sodium dodecanoate also inhibited aggregation well below the cmc, suggesting that surfactants may be a practical way to modulate whey protein properties. Copyright © 2011 Elsevier B.V. All rights reserved.

  20. Interaction of milk proteins and Binder of Sperm (BSP) proteins from boar, stallion and ram semen.

    Science.gov (United States)

    Plante, Geneviève; Lusignan, Marie-France; Lafleur, Michel; Manjunath, Puttaswamy

    2015-08-15

    Mammalian semen contains a family of closely related proteins known as Binder of SPerm (BSP proteins) that are added to sperm at ejaculation. BSP proteins extract lipids from the sperm membrane thereby extensively modifying its composition. These changes can ultimately be detrimental to sperm storage. We have demonstrated that bovine BSP proteins interact with major milk proteins and proposed that this interaction could be the basis of sperm protection by milk extenders. In the present study, we investigated if homologous BSP proteins present in boar, stallion and ram seminal plasma display a similar affinity for the milk proteins in order to assess whether the mechanism of sperm protection by milk for these species could be general. Skim milk was incubated with seminal plasma proteins (boar, stallion and ram), chromatographed on a Sepharose CL-4B column and protein fractions were analyzed by immunoblotting. Boar, stallion and ram BSP proteins displayed affinity for a milk protein fraction (F1) mainly composed of α-lactalbumin, β-lactoglobulin, and κ-casein. They also had affinity for another milk protein fraction (F2) composed mostly of casein micelles. However, stallion BSP showed higher affinity for the fraction (F1). These results further extend our view that the association of BSP proteins with milk proteins could be a general feature of the mechanism of mammalian sperm protection by milk to prevent detrimental effect of prolonged exposure of sperm to seminal plasma.

  1. Identification of Redox and Glucose-Dependent Txnip Protein Interactions

    Directory of Open Access Journals (Sweden)

    Benjamin J. Forred

    2016-01-01

    Full Text Available Thioredoxin-interacting protein (Txnip acts as a negative regulator of thioredoxin function and is a critical modulator of several diseases including, but not limited to, diabetes, ischemia-reperfusion cardiac injury, and carcinogenesis. Therefore, Txnip has become an attractive therapeutic target to alleviate disease pathologies. Although Txnip has been implicated with numerous cellular processes such as proliferation, fatty acid and glucose metabolism, inflammation, and apoptosis, the molecular mechanisms underlying these processes are largely unknown. The objective of these studies was to identify Txnip interacting proteins using the proximity-based labeling method, BioID, to understand differential regulation of pleiotropic Txnip cellular functions. The BioID transgene fused to Txnip expressed in HEK293 identified 31 interacting proteins. Many protein interactions were redox-dependent and were disrupted through mutation of a previously described reactive cysteine (C247S. Furthermore, we demonstrate that this model can be used to identify dynamic Txnip interactions due to known physiological regulators such as hyperglycemia. These data identify novel Txnip protein interactions and demonstrate dynamic interactions dependent on redox and glucose perturbations, providing clarification to the pleiotropic cellular functions of Txnip.

  2. PCNA Structure and Interactions with Partner Proteins

    KAUST Repository

    Oke, Muse; Zaher, Manal S.; Hamdan, Samir

    2018-01-01

    Proliferating cell nuclear antigen (PCNA) consists of three identical monomers that topologically encircle double-stranded DNA. PCNA stimulates the processivity of DNA polymerase δ and, to a less extent, the intrinsically highly processive DNA polymerase ε. It also functions as a platform that recruits and coordinates the activities of a large number of DNA processing proteins. Emerging structural and biochemical studies suggest that the nature of PCNA-partner proteins interactions is complex. A hydrophobic groove at the front side of PCNA serves as a primary docking site for the consensus PIP box motifs present in many PCNA-binding partners. Sequences that immediately flank the PIP box motif or regions that are distant from it could also interact with the hydrophobic groove and other regions of PCNA. Posttranslational modifications on the backside of PCNA could add another dimension to its interaction with partner proteins. An encounter of PCNA with different DNA structures might also be involved in coordinating its interactions. Finally, the ability of PCNA to bind up to three proteins while topologically linked to DNA suggests that it would be a versatile toolbox in many different DNA processing reactions.

  3. PCNA Structure and Interactions with Partner Proteins

    KAUST Repository

    Oke, Muse

    2018-01-29

    Proliferating cell nuclear antigen (PCNA) consists of three identical monomers that topologically encircle double-stranded DNA. PCNA stimulates the processivity of DNA polymerase δ and, to a less extent, the intrinsically highly processive DNA polymerase ε. It also functions as a platform that recruits and coordinates the activities of a large number of DNA processing proteins. Emerging structural and biochemical studies suggest that the nature of PCNA-partner proteins interactions is complex. A hydrophobic groove at the front side of PCNA serves as a primary docking site for the consensus PIP box motifs present in many PCNA-binding partners. Sequences that immediately flank the PIP box motif or regions that are distant from it could also interact with the hydrophobic groove and other regions of PCNA. Posttranslational modifications on the backside of PCNA could add another dimension to its interaction with partner proteins. An encounter of PCNA with different DNA structures might also be involved in coordinating its interactions. Finally, the ability of PCNA to bind up to three proteins while topologically linked to DNA suggests that it would be a versatile toolbox in many different DNA processing reactions.

  4. HyCCAPP as a tool to characterize promoter DNA-protein interactions in Saccharomyces cerevisiae.

    Science.gov (United States)

    Guillen-Ahlers, Hector; Rao, Prahlad K; Levenstein, Mark E; Kennedy-Darling, Julia; Perumalla, Danu S; Jadhav, Avinash Y L; Glenn, Jeremy P; Ludwig-Kubinski, Amy; Drigalenko, Eugene; Montoya, Maria J; Göring, Harald H; Anderson, Corianna D; Scalf, Mark; Gildersleeve, Heidi I S; Cole, Regina; Greene, Alexandra M; Oduro, Akua K; Lazarova, Katarina; Cesnik, Anthony J; Barfknecht, Jared; Cirillo, Lisa A; Gasch, Audrey P; Shortreed, Michael R; Smith, Lloyd M; Olivier, Michael

    2016-06-01

    Currently available methods for interrogating DNA-protein interactions at individual genomic loci have significant limitations, and make it difficult to work with unmodified cells or examine single-copy regions without specific antibodies. In this study, we describe a physiological application of the Hybridization Capture of Chromatin-Associated Proteins for Proteomics (HyCCAPP) methodology we have developed. Both novel and known locus-specific DNA-protein interactions were identified at the ENO2 and GAL1 promoter regions of Saccharomyces cerevisiae, and revealed subgroups of proteins present in significantly different levels at the loci in cells grown on glucose versus galactose as the carbon source. Results were validated using chromatin immunoprecipitation. Overall, our analysis demonstrates that HyCCAPP is an effective and flexible technology that does not require specific antibodies nor prior knowledge of locally occurring DNA-protein interactions and can now be used to identify changes in protein interactions at target regions in the genome in response to physiological challenges. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Unveiling protein functions through the dynamics of the interaction network.

    Directory of Open Access Journals (Sweden)

    Irene Sendiña-Nadal

    Full Text Available Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.

  6. Towards a rigorous network of protein-protein interactions of the model sulfate reducer Desulfovibrio vulgaris Hildenborough.

    Directory of Open Access Journals (Sweden)

    Swapnil R Chhabra

    Full Text Available 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 Escherichia coli and other aerobes and more recently to study the stress response behavior of Desulfovibrio vulgaris Hildenborough, a model obligate anaerobe and sulfate reducer and the subject of this study. Here we carried out affinity purification followed by mass spectrometry to reconstruct an interaction network among 12 chromosomally encoded bait and 90 prey proteins based on 134 bait-prey interactions identified to be of high confidence. Protein-protein interaction data are often plagued by the lack of adequate controls and replication analyses necessary to assess confidence in the results, including identification of potential false positives. We addressed these issues through the use of biological replication, exponentially modified protein abundance indices, results from an experimental negative control, and a statistical test to assign confidence to each putative interacting pair applicable to small interaction data studies. We discuss the biological significance of metabolic features of D. vulgaris revealed by these protein-protein interaction data and the observed protein modifications. 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.

  7. Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection.

    Science.gov (United States)

    Chen, Yifei; Sun, Yuxing; Han, Bing-Qing

    2015-01-01

    Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

  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. A conserved NAD+ binding pocket that regulates protein-protein interactions during aging.

    Science.gov (United States)

    Li, Jun; Bonkowski, Michael S; Moniot, Sébastien; Zhang, Dapeng; Hubbard, Basil P; Ling, Alvin J Y; Rajman, Luis A; Qin, Bo; Lou, Zhenkun; Gorbunova, Vera; Aravind, L; Steegborn, Clemens; Sinclair, David A

    2017-03-24

    DNA repair is essential for life, yet its efficiency declines with age for reasons that are unclear. Numerous proteins possess Nudix homology domains (NHDs) that have no known function. We show that NHDs are NAD + (oxidized form of nicotinamide adenine dinucleotide) binding domains that regulate protein-protein interactions. The binding of NAD + to the NHD domain of DBC1 (deleted in breast cancer 1) prevents it from inhibiting PARP1 [poly(adenosine diphosphate-ribose) polymerase], a critical DNA repair protein. As mice age and NAD + concentrations decline, DBC1 is increasingly bound to PARP1, causing DNA damage to accumulate, a process rapidly reversed by restoring the abundance of NAD + Thus, NAD + directly regulates protein-protein interactions, the modulation of which may protect against cancer, radiation, and aging. Copyright © 2017, American Association for the Advancement of Science.

  10. InSilico Proteomics System: Integration and Application of Protein and Protein-Protein Interaction Data using Microsoft .NET

    Directory of Open Access Journals (Sweden)

    Straßer Wolfgang

    2006-12-01

    Full Text Available In the last decades, biological databases became the major knowledge resource for researchers in the field of molecular biology. The distribution of information among these databases is one of the major problems. An overview about the subject area of data access and representation of protein and protein-protein interaction data within public biological databases is described. For a comprehensive and consistent way of searching and analysing integrated protein and protein-protein interaction data, the InSilico Proteomics (ISP project has been initiated. Its three main objectives are (1 to provide an integrated knowledge pool for data investigation and global network analysis functions for a better understanding of a cell’s interactome, (2 employment of public data for plausibility analysis and validation of in-house experimental data and (3 testing the applicability of Microsoft’s .NET architecture for bioinformatics applications. Data integrated into the ISP database can be queried through the Web portal PRIMOS (PRotein Interaction and MOlecule Search which is freely available at http://biomis.fh-hagenberg.at/isp/primos.

  11. Interrogating the architecture of protein assemblies and protein interaction networks by cross-linking mass spectrometry

    NARCIS (Netherlands)

    Liu, Fan; Heck, Albert J R

    2015-01-01

    Proteins are involved in almost all processes of the living cell. They are organized through extensive networks of interaction, by tightly bound macromolecular assemblies or more transiently via signaling nodes. Therefore, revealing the architecture of protein complexes and protein interaction

  12. Network Compression as a Quality Measure for Protein Interaction Networks

    Science.gov (United States)

    Royer, Loic; Reimann, Matthias; Stewart, A. Francis; Schroeder, Michael

    2012-01-01

    With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients. PMID:22719828

  13. Interaction between Vaccinium bracteatum Thunb. leaf pigment and rice proteins.

    Science.gov (United States)

    Wang, Li; Xu, Yuan; Zhou, Sumei; Qian, Haifeng; Zhang, Hui; Qi, Xiguang; Fan, Meihua

    2016-03-01

    In this study, we investigated the interaction of Vaccinium bracteatum Thunb. leaf (VBTL) pigment and rice proteins. In the presence of rice protein, VBTL pigment antioxidant activity and free polyphenol content decreased by 67.19% and 68.11%, respectively, and L(∗) of the protein-pigment complex decreased significantly over time. L(∗) values of albumin, globulin and glutelin during 60-min pigment exposure decreased by 55.00, 57.14, and 54.30%, respectively, indicating that these proteins had bound to the pigment. A significant difference in protein surface hydrophobicity was observed between rice proteins and pigment-protein complexes, indicating that hydrophobic interaction is a major binding mechanism between VBTL pigment and rice proteins. A significant difference in secondary structures between proteins and protein-pigment complexes was also uncovered, indicating that hydrogen bonding may be another mode of interaction between VBTL pigment and rice proteins. Our results indicate that VBTL pigment can stain rice proteins with hydrophobic and hydrogen interactions. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Quantification of protein interaction kinetics in a micro droplet

    Energy Technology Data Exchange (ETDEWEB)

    Yin, L. L. [Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, Tempe, Arizona 85287 (United States); College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044 (China); Wang, S. P., E-mail: shaopeng.wang@asu.edu, E-mail: njtao@asu.edu; Shan, X. N.; Tao, N. J., E-mail: shaopeng.wang@asu.edu, E-mail: njtao@asu.edu [Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, Tempe, Arizona 85287 (United States); Zhang, S. T. [College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044 (China)

    2015-11-15

    Characterization of protein interactions is essential to the discovery of disease biomarkers, the development of diagnostic assays, and the screening for therapeutic drugs. Conventional flow-through kinetic measurements need relative large amount of sample that is not feasible for precious protein samples. We report a novel method to measure protein interaction kinetics in a single droplet with sub microliter or less volume. A droplet in a humidity-controlled environmental chamber is replacing the microfluidic channels as the reactor for the protein interaction. The binding process is monitored by a surface plasmon resonance imaging (SPRi) system. Association curves are obtained from the average SPR image intensity in the center area of the droplet. The washing step required by conventional flow-through SPR method is eliminated in the droplet method. The association and dissociation rate constants and binding affinity of an antigen-antibody interaction are obtained by global fitting of association curves at different concentrations. The result obtained by this method is accurate as validated by conventional flow-through SPR system. This droplet-based method not only allows kinetic studies for proteins with limited supply but also opens the door for high-throughput protein interaction study in a droplet-based microarray format that enables measurement of many to many interactions on a single chip.

  15. HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species.

    Science.gov (United States)

    López, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups. Database URL: http://hintdb.hgc.jp/htp. © The Author(s) 2015. Published by Oxford University Press.

  16. Studying Protein-Protein Interactions by Biotin AP-Tagged Pulldown and LTQ-Orbitrap Mass Spectrometry.

    Science.gov (United States)

    Xie, Zhongqiu; Jia, Yuemeng; Li, Hui

    2017-01-01

    The study of protein-protein interactions represents a key aspect of biological research. Identifying unknown protein binding partners using mass spectrometry (MS)-based proteomics has evolved into an indispensable strategy in drug discovery. The classic approach of immunoprecipitation with specific antibodies against the proteins of interest has limitations, such as the need for immunoprecipitation-qualified antibody. The biotin AP-tag pull-down system has the advantage of high specificity, ease of use, and no requirement for antibody. It is based on the high specificity, high affinity interaction between biotin and streptavidin. After pulldown, in-gel tryptic digestion and tandem mass spectrometry (MS/MS) analysis of sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) protein bands can be performed. In this work, we provide protocols that can be used for the identification of proteins that interact with FOXM1, a protein that has recently emerged as a potential biomarker and drug target in oncotherapy, as an example. We focus on the pull-down procedure and assess the efficacy of the pulldown with known FOXM1 interactors such as β-catenin. We use a high performance LTQ Orbitrap MSn system that combines rapid LTQ ion trap data acquisition with high mass accuracy Orbitrap analysis to identify the interacting proteins.

  17. Peptide folding in the presence of interacting protein crowders

    Energy Technology Data Exchange (ETDEWEB)

    Bille, Anna, E-mail: anna.bille@thep.lu.se; Irbäck, Anders, E-mail: anders@thep.lu.se [Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Sölvegatan 14A, SE-223 62 Lund (Sweden); Mohanty, Sandipan, E-mail: s.mohanty@fz-juelich.de [Jülich Supercomputing Centre, Institute for Advanced Simulation, Forschungszentrum Jülich, D-52425 Jülich (Germany)

    2016-05-07

    Using Monte Carlo methods, we explore and compare the effects of two protein crowders, BPTI and GB1, on the folding thermodynamics of two peptides, the compact helical trp-cage and the β-hairpin-forming GB1m3. The thermally highly stable crowder proteins are modeled using a fixed backbone and rotatable side-chains, whereas the peptides are free to fold and unfold. In the simulations, the crowder proteins tend to distort the trp-cage fold, while having a stabilizing effect on GB1m3. The extent of the effects on a given peptide depends on the crowder type. Due to a sticky patch on its surface, BPTI causes larger changes than GB1 in the melting properties of the peptides. The observed effects on the peptides stem largely from attractive and specific interactions with the crowder surfaces, and differ from those seen in reference simulations with purely steric crowder particles.

  18. Thioredoxin interacting protein and its association with clinical outcome in gastro-oesophageal adenocarcinoma

    Directory of Open Access Journals (Sweden)

    Caroline M. Woolston

    2013-01-01

    Full Text Available The overall prognosis for operable gastro-oesophageal adenocarcinoma remains poor and therefore neoadjuvant chemotherapy has become the standard of care, in addition to radical surgery. Certain anticancer agents (e.g. anthracyclines and cisplatin generate damaging reactive oxygen species as by-products of their mechanism of action. Drug effectiveness can therefore depend upon the presence of cellular redox buffering systems that are often deregulated in cancer. The expression of the redox protein, thioredoxin interacting protein, was assessed in gastro-oesophageal adenocarcinomas. Thioredoxin interacting protein expression was assessed using conventional immunohistochemistry on a tissue microarray of 140 adenocarcinoma patients treated by primary surgery alone and 88 operable cases treated with neoadjuvant chemotherapy. In the primary surgery cases, high thioredoxin interacting protein expression associated with a lack of lymph node involvement (p=0.005, no perineural invasion (p=0.030 and well/moderate tumour differentiation (p=0.033. In the neoadjuvant tumours, high thioredoxin interacting protein expression was an independent marker for improved disease specific survival (p=0.002 especially in cases with anthracycline-based regimes (p=0.008. This study highlights the potential of thioredoxin interacting protein as a biomarker for response in neoadjuvant treated gastro-oesophageal adenocarcinoma and may represent a useful therapeutic target due to its association with tumour progression.

  19. Database of Interacting Proteins (DIP)

    Data.gov (United States)

    U.S. Department of Health & Human Services — The DIP database catalogs experimentally determined interactions between proteins. It combines information from a variety of sources to create a single, consistent...

  20. Brain transcriptome-wide screen for HIV-1 Nef protein interaction partners reveals various membrane-associated proteins.

    Directory of Open Access Journals (Sweden)

    Ellen C Kammula

    Full Text Available HIV-1 Nef protein contributes essentially to the pathology of AIDS by a variety of protein-protein-interactions within the host cell. The versatile functionality of Nef is partially attributed to different conformational states and posttranslational modifications, such as myristoylation. Up to now, many interaction partners of Nef have been identified using classical yeast two-hybrid screens. Such screens rely on transcriptional activation of reporter genes in the nucleus to detect interactions. Thus, the identification of Nef interaction partners that are integral membrane proteins, membrane-associated proteins or other proteins that do not translocate into the nucleus is hampered. In the present study, a split-ubiquitin based yeast two-hybrid screen was used to identify novel membrane-localized interaction partners of Nef. More than 80% of the hereby identified interaction partners of Nef are transmembrane proteins. The identified hits are GPM6B, GPM6A, BAP31, TSPAN7, CYB5B, CD320/TCblR, VSIG4, PMEPA1, OCIAD1, ITGB1, CHN1, PH4, CLDN10, HSPA9, APR-3, PEBP1 and B3GNT, which are involved in diverse cellular processes like signaling, apoptosis, neurogenesis, cell adhesion and protein trafficking or quality control. For a subfraction of the hereby identified proteins we present data supporting their direct interaction with HIV-1 Nef. We discuss the results with respect to many phenotypes observed in HIV infected cells and patients. The identified Nef interaction partners may help to further elucidate the molecular basis of HIV-related diseases.

  1. Enhancing the Functional Content of Eukaryotic Protein Interaction Networks

    Science.gov (United States)

    Pandey, Gaurav; Arora, Sonali; Manocha, Sahil; Whalen, Sean

    2014-01-01

    Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks. PMID:25275489

  2. Wiki-pi: a web-server of annotated human protein-protein interactions to aid in discovery of protein function.

    Directory of Open Access Journals (Sweden)

    Naoki Orii

    Full Text Available Protein-protein interactions (PPIs are the basis of biological functions. Knowledge of the interactions of a protein can help understand its molecular function and its association with different biological processes and pathways. Several publicly available databases provide comprehensive information about individual proteins, such as their sequence, structure, and function. There also exist databases that are built exclusively to provide PPIs by curating them from published literature. The information provided in these web resources is protein-centric, and not PPI-centric. The PPIs are typically provided as lists of interactions of a given gene with links to interacting partners; they do not present a comprehensive view of the nature of both the proteins involved in the interactions. A web database that allows search and retrieval based on biomedical characteristics of PPIs is lacking, and is needed. We present Wiki-Pi (read Wiki-π, a web-based interface to a database of human PPIs, which allows users to retrieve interactions by their biomedical attributes such as their association to diseases, pathways, drugs and biological functions. Each retrieved PPI is shown with annotations of both of the participant proteins side-by-side, creating a basis to hypothesize the biological function facilitated by the interaction. Conceptually, it is a search engine for PPIs analogous to PubMed for scientific literature. Its usefulness in generating novel scientific hypotheses is demonstrated through the study of IGSF21, a little-known gene that was recently identified to be associated with diabetic retinopathy. Using Wiki-Pi, we infer that its association to diabetic retinopathy may be mediated through its interactions with the genes HSPB1, KRAS, TMSB4X and DGKD, and that it may be involved in cellular response to external stimuli, cytoskeletal organization and regulation of molecular activity. The website also provides a wiki-like capability allowing users

  3. Quantitative chemogenomics: machine-learning models of protein-ligand interaction.

    Science.gov (United States)

    Andersson, Claes R; Gustafsson, Mats G; Strömbergsson, Helena

    2011-01-01

    Chemogenomics is an emerging interdisciplinary field that lies in the interface of biology, chemistry, and informatics. Most of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand interaction is therefore central to drug discovery and design. In the subfield of chemogenomics known as proteochemometrics, protein-ligand-interaction models are induced from data matrices that consist of both protein and ligand information along with some experimentally measured variable. The two general aims of this quantitative multi-structure-property-relationship modeling (QMSPR) approach are to exploit sparse/incomplete information sources and to obtain more general models covering larger parts of the protein-ligand space, than traditional approaches that focuses mainly on specific targets or ligands. The data matrices, usually obtained from multiple sparse/incomplete sources, typically contain series of proteins and ligands together with quantitative information about their interactions. A useful model should ideally be easy to interpret and generalize well to new unseen protein-ligand combinations. Resolving this requires sophisticated machine-learning methods for model induction, combined with adequate validation. This review is intended to provide a guide to methods and data sources suitable for this kind of protein-ligand-interaction modeling. An overview of the modeling process is presented including data collection, protein and ligand descriptor computation, data preprocessing, machine-learning-model induction and validation. Concerns and issues specific for each step in this kind of data-driven modeling will be discussed. © 2011 Bentham Science Publishers

  4. Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces.

    Science.gov (United States)

    Xia, Zheng; Wu, Ling-Yun; Zhou, Xiaobo; Wong, Stephen T C

    2010-09-13

    Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG. We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

  5. A membrane protein / signaling protein interaction network for Arabidopsis version AMPv2

    Directory of Open Access Journals (Sweden)

    Sylvie Lalonde

    2010-09-01

    Full Text Available Interactions between membrane proteins and the soluble fraction are essential for signal transduction and for regulating nutrient transport. To gain insights into the membrane-based interactome, 3,852 open reading frames (ORFs out of a target list of 8,383 representing membrane and signaling proteins from Arabidopsis thaliana were cloned into a Gateway compatible vector. The mating-based split-ubiquitin system was used to screen for potential protein-protein interactions (pPPIs among 490 Arabidopsis ORFs. A binary robotic screen between 142 receptor-like kinases, 72 transporters, 57 soluble protein kinases and phosphatases, 40 glycosyltransferases, 95 proteins of various functions and 89 proteins with unknown function detected 387 out of 90,370 possible PPIs. A secondary screen confirmed 343 (of 387 pPPIs between 179 proteins, yielding a scale-free network (r2=0.863. Eighty of 142 transmembrane receptor-like kinases (RLK tested positive, identifying three homomers, 63 heteromers and 80 pPPIs with other proteins. Thirty-one out of 142 RLK interactors (including RLKs had previously been found to be phosphorylated; thus interactors may be substrates for respective RLKs. None of the pPPIs described here had been reported in the major interactome databases, including potential interactors of G protein-coupled receptors, phospholipase C, and AMT ammonium transporters. Two RLKs found as putative interactors of AMT1;1 were independently confirmed using a split luciferase assay in Arabidopsis protoplasts. These RLKs may be involved in ammonium-dependent phosphorylation of the C-terminus and regulation of ammonium uptake activity. The robotic screening method established here will enable a systematic analysis of membrane protein interactions in fungi, plants and metazoa.

  6. HitPredict version 4: comprehensive reliability scoring of physical protein?protein interactions from more than 100 species

    OpenAIRE

    L?pez, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein?protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein?protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of p...

  7. Hydrophobic Interaction Chromatography for Bottom-Up Proteomics Analysis of Single Proteins and Protein Complexes.

    Science.gov (United States)

    Rackiewicz, Michal; Große-Hovest, Ludger; Alpert, Andrew J; Zarei, Mostafa; Dengjel, Jörn

    2017-06-02

    Hydrophobic interaction chromatography (HIC) is a robust standard analytical method to purify proteins while preserving their biological activity. It is widely used to study post-translational modifications of proteins and drug-protein interactions. In the current manuscript we employed HIC to separate proteins, followed by bottom-up LC-MS/MS experiments. We used this approach to fractionate antibody species followed by comprehensive peptide mapping as well as to study protein complexes in human cells. HIC-reversed-phase chromatography (RPC)-mass spectrometry (MS) is a powerful alternative to fractionate proteins for bottom-up proteomics experiments making use of their distinct hydrophobic properties.

  8. Huntingtin interacting proteins are genetic modifiers of neurodegeneration.

    Directory of Open Access Journals (Sweden)

    Linda S Kaltenbach

    2007-05-01

    Full Text Available Huntington's disease (HD is a fatal neurodegenerative condition caused by expansion of the polyglutamine tract in the huntingtin (Htt protein. Neuronal toxicity in HD is thought to be, at least in part, a consequence of protein interactions involving mutant Htt. We therefore hypothesized that genetic modifiers of HD neurodegeneration should be enriched among Htt protein interactors. To test this idea, we identified a comprehensive set of Htt interactors using two complementary approaches: high-throughput yeast two-hybrid screening and affinity pull down followed by mass spectrometry. This effort led to the identification of 234 high-confidence Htt-associated proteins, 104 of which were found with the yeast method and 130 with the pull downs. We then tested an arbitrary set of 60 genes encoding interacting proteins for their ability to behave as genetic modifiers of neurodegeneration in a Drosophila model of HD. This high-content validation assay showed that 27 of 60 orthologs tested were high-confidence genetic modifiers, as modification was observed with more than one allele. The 45% hit rate for genetic modifiers seen among the interactors is an order of magnitude higher than the 1%-4% typically observed in unbiased genetic screens. Genetic modifiers were similarly represented among proteins discovered using yeast two-hybrid and pull-down/mass spectrometry methods, supporting the notion that these complementary technologies are equally useful in identifying biologically relevant proteins. Interacting proteins confirmed as modifiers of the neurodegeneration phenotype represent a diverse array of biological functions, including synaptic transmission, cytoskeletal organization, signal transduction, and transcription. Among the modifiers were 17 loss-of-function suppressors of neurodegeneration, which can be considered potential targets for therapeutic intervention. Finally, we show that seven interacting proteins from among 11 tested were able to

  9. The potential of protein-nanomaterial interaction for advanced drug delivery

    DEFF Research Database (Denmark)

    Peng, Qiang; Mu, Huiling

    2016-01-01

    Nanomaterials, like nanoparticles, micelles, nano-sheets, nanotubes and quantum dots, have great potentials in biomedical fields. However, their delivery is highly limited by the formation of protein corona upon interaction with endogenous proteins. This new identity, instead of nanomaterial itself...... of such interaction for advanced drug delivery are presented........ Therefore, protein-nanomaterial interaction is a great challenge for nanomaterial systems and should be inhibited. However, this interaction can also be used to functionalize nanomaterials by forming a selected protein corona. Unlike other decoration using exogenous molecules, nanomaterials functionalized...

  10. Visualization and targeted disruption of protein interactions in living cells

    Science.gov (United States)

    Herce, Henry D.; Deng, Wen; Helma, Jonas; Leonhardt, Heinrich; Cardoso, M. Cristina

    2013-01-01

    Protein–protein interactions are the basis of all processes in living cells, but most studies of these interactions rely on biochemical in vitro assays. Here we present a simple and versatile fluorescent-three-hybrid (F3H) strategy to visualize and target protein–protein interactions. A high-affinity nanobody anchors a GFP-fusion protein of interest at a defined cellular structure and the enrichment of red-labelled interacting proteins is measured at these sites. With this approach, we visualize the p53–HDM2 interaction in living cells and directly monitor the disruption of this interaction by Nutlin 3, a drug developed to boost p53 activity in cancer therapy. We further use this approach to develop a cell-permeable vector that releases a highly specific peptide disrupting the p53 and HDM2 interaction. The availability of multiple anchor sites and the simple optical readout of this nanobody-based capture assay enable systematic and versatile analyses of protein–protein interactions in practically any cell type and species. PMID:24154492

  11. Protein disulfide isomerase interacts with tau protein and inhibits its fibrillization.

    Directory of Open Access Journals (Sweden)

    Li-Rong Xu

    Full Text Available BACKGROUND: Tau protein is implicated in the pathogenesis of neurodegenerative disorders such as tauopathies including Alzheimer disease, and Tau fibrillization is thought to be related to neuronal toxicity. Physiological inhibitors of Tau fibrillization hold promise for developing new strategies for treatment of Alzheimer disease. Because protein disulfide isomerase (PDI is both an enzyme and a chaperone, and implicated in neuroprotection against Alzheimer disease, we want to know whether PDI can prevent Tau fibrillization. In this study, we have investigated the interaction between PDI and Tau protein and the effect of PDI on Tau fibrillization. METHODOLOGY/PRINCIPAL FINDINGS: As evidenced by co-immunoprecipitation and confocal laser scanning microscopy, human PDI interacts and co-locates with some endogenous human Tau on the endoplasmic reticulum of undifferentiated SH-SY5Y neuroblastoma cells. The results from isothermal titration calorimetry show that one full-length human PDI binds to one full-length human Tau (or human Tau fragment Tau244-372 monomer with moderate, micromolar affinity at physiological pH and near physiological ionic strength. As revealed by thioflavin T binding assays, Sarkosyl-insoluble SDS-PAGE, and transmission electron microscopy, full-length human PDI remarkably inhibits both steps of nucleation and elongation of Tau244-372 fibrillization in a concentration-dependent manner. Furthermore, we find that two molecules of the a-domain of human PDI interact with one Tau244-372 molecule with sub-micromolar affinity, and inhibit both steps of nucleation and elongation of Tau244-372 fibrillization more strongly than full-length human PDI. CONCLUSIONS/SIGNIFICANCE: We demonstrate for the first time that human PDI binds to Tau protein mainly through its thioredoxin-like catalytic domain a, forming a 1∶1 complex and preventing Tau misfolding. Our findings suggest that PDI could act as a physiological inhibitor of Tau

  12. Partial molar volume of proteins studied by the three-dimensional reference interaction site model theory.

    Science.gov (United States)

    Imai, Takashi; Kovalenko, Andriy; Hirata, Fumio

    2005-04-14

    The three-dimensional reference interaction site model (3D-RISM) theory is applied to the analysis of hydration effects on the partial molar volume of proteins. For the native structure of some proteins, the partial molar volume is decomposed into geometric and hydration contributions using the 3D-RISM theory combined with the geometric volume calculation. The hydration contributions are correlated with the surface properties of the protein. The thermal volume, which is the volume of voids around the protein induced by the thermal fluctuation of water molecules, is directly proportional to the accessible surface area of the protein. The interaction volume, which is the contribution of electrostatic interactions between the protein and water molecules, is apparently governed by the charged atomic groups on the protein surface. The polar atomic groups do not make any contribution to the interaction volume. The volume differences between low- and high-pressure structures of lysozyme are also analyzed by the present method.

  13. Interaction between holo transferrin and HSA-PPIX complex in the presence of lomefloxacin: An evaluation of PPIX aggregation in protein-protein interactions

    Science.gov (United States)

    Sattar, Zohreh; Iranfar, Hediye; Asoodeh, Ahmad; Saberi, Mohammad Reza; Mazhari, Mahboobeh; Chamani, Jamshidkhan

    2012-11-01

    Human serum albumin (HSA) and holo transferrin (TF) are two serum carrier proteins that are able to interact with each other, thereby altering their binding behavior toward their ligands. During the course of this study, the interaction between HSA-PPIX and TF, in the presence and absence of lomefloxacin (LMF), was for the first time investigated using different spectroscopic and molecular modeling techniques. Fluorescence spectroscopy experiments were performed in order to study conformational changes of proteins. The RLS technique was utilized to investigate the effect of LMF on J-aggregation of PPIX, which is the first report of its kind. Our findings present clear-cut evidence for the alteration of interactions between HSA and TF in the presence of PPIX and changes in drug-binding to HSA and HSA-PPIX complex upon interaction with TF. Moreover, molecular modeling studies suggested that the binding site for LMF became switched in the presence of PPIX, and that LMF bound to the site IIA of HSA. The obtained results should give new insight into research in this field and may cast some light on the dynamics of drugs in biological systems.

  14. Insights into the energetics and mechanism underlying the interaction of tetraethylammonium bromide with proteins

    International Nuclear Information System (INIS)

    Banerjee, Tuhina; Kishore, Nand

    2008-01-01

    Calorimetry has been employed to investigate the quantitative energetic aspects and mechanism underlying protein-tetraethylammonium bromide (TEAB) interactions. Differential scanning calorimetry and UV-Visible spectroscopy have been used to study the thermal unfolding of three proteins of different structure and function (bovine serum albumin, α-lactalbumin, and bovine pancreatic ribonuclease A). The mode of interaction has been studied by using isothermal titration calorimetry, which demonstrates the absence of appreciable specific binding of TEAB to the protein. This suggests the involvement of solvent mediated effects and, possibly weak non-specific binding. The thermal unfolding transitions were found to be calorimetrically reversible for α-lactalbumin and bovine pancreatic ribonuclease A and partially reversible in the case of bovine serum albumin. The results indicate protein destabilization promoted by the TEAB interaction. The preferential interaction parameters of TEAB with α-lactalbumin and ribonuclease A confirm that an increased interaction of the hydrophobic groups of the TEAB with that of the protein upon denaturation is responsible for the reduced thermal stability of the protein. The decrease in the thermal stability of proteins in the presence of TEAB is well supported by a red shift in the intrinsic fluorescence of these proteins leading to conformational change thereby shifting the native ↔ denatured equilibrium towards right. The forces responsible for the thermal denaturation of the proteins of different structure and function in the presence of TEAB are discussed

  15. Interactions among tobacco sieve element occlusion (SEO) proteins.

    Science.gov (United States)

    Jekat, Stephan B; Ernst, Antonia M; Zielonka, Sascia; Noll, Gundula A; Prüfer, Dirk

    2012-12-01

    Angiosperms transport their photoassimilates through sieve tubes, which comprise longitudinally-connected sieve elements. In dicots and also some monocots, the sieve elements contain parietal structural proteins known as phloem proteins or P-proteins. Following injury, P proteins disperse and accumulate as viscous plugs at the sieve plates to prevent the loss of valuable transport sugars. Tobacco (Nicotiana tabacum) P-proteins are multimeric complexes comprising subunits encoded by members of the SEO (sieve element occlusion) gene family. The existence of multiple subunits suggests that P-protein assembly involves interactions between SEO proteins, but this process is largely uncharacterized and it is unclear whether the different subunits perform unique roles or are redundant. We therefore extended our analysis of the tobacco P-proteins NtSEO1 and NtSEO2 to investigate potential interactions between them, and found that both proteins can form homomeric and heteromeric complexes in planta.

  16. ProteinShop: A tool for interactive protein manipulation and steering

    Energy Technology Data Exchange (ETDEWEB)

    Crivelli, Silvia; Kreylos, Oliver; Max, Nelson; Hamann, Bernd; Bethel, Wes

    2004-05-25

    We describe ProteinShop, a new visualization tool that streamlines and simplifies the process of determining optimal protein folds. ProteinShop may be used at different stages of a protein structure prediction process. First, it can create protein configurations containing secondary structures specified by the user. Second, it can interactively manipulate protein fragments to achieve desired folds by adjusting the dihedral angles of selected coil regions using an Inverse Kinematics method. Last, it serves as a visual framework to monitor and steer a protein structure prediction process that may be running on a remote machine. ProteinShop was used to create initial configurations for a protein structure prediction method developed by a team that competed in CASP5. ProteinShop's use accelerated the process of generating initial configurations, reducing the time required from days to hours. This paper describes the structure of ProteinShop and discusses its main features.

  17. Influence of wine pectic polysaccharides on the interactions between condensed tannins and salivary proteins.

    Science.gov (United States)

    Carvalho, Elisabete; Mateus, Nuno; Plet, Benoit; Pianet, Isabelle; Dufourc, Erick; De Freitas, Victor

    2006-11-15

    Alpha-amylase, a major human salivary protein, and IB8c, a representative of the proline-rich proteins, were obtained by isolation from saliva and by solid-phase synthesis, respectively. The interactions between these proteins and condensed tannins isolated from grape seeds were studied at different protein and tannin concentrations by measuring their aggregation. Pectic polysaccharides were isolated from wine, and their effect on protein tannin aggregation was assessed. The results presented in this study showed that the most acidic fractions of arabinogalactan proteins have the ability to inhibit the formation of aggregates between the grape seed tannins and the two different salivary proteins. Rhamnogalacturonan II has the same ability toward alpha-amylase but not IB8c under the conditions of the present study. Polysaccharides show effects at concentrations at which they are present in wine, which could mean an influence in wine astringency. The interaction between condensed tannins and alpha-amylase is differently affected by ionic strength when compared with IB8c.

  18. Incorporating information on predicted solvent accessibility to the co-evolution-based study of protein interactions.

    Science.gov (United States)

    Ochoa, David; García-Gutiérrez, Ponciano; Juan, David; Valencia, Alfonso; Pazos, Florencio

    2013-01-27

    A widespread family of methods for studying and predicting protein interactions using sequence information is based on co-evolution, quantified as similarity of phylogenetic trees. Part of the co-evolution observed between interacting proteins could be due to co-adaptation caused by inter-protein contacts. In this case, the co-evolution is expected to be more evident when evaluated on the surface of the proteins or the internal layers close to it. In this work we study the effect of incorporating information on predicted solvent accessibility to three methods for predicting protein interactions based on similarity of phylogenetic trees. We evaluate the performance of these methods in predicting different types of protein associations when trees based on positions with different characteristics of predicted accessibility are used as input. We found that predicted accessibility improves the results of two recent versions of the mirrortree methodology in predicting direct binary physical interactions, while it neither improves these methods, nor the original mirrortree method, in predicting other types of interactions. That improvement comes at no cost in terms of applicability since accessibility can be predicted for any sequence. We also found that predictions of protein-protein interactions are improved when multiple sequence alignments with a richer representation of sequences (including paralogs) are incorporated in the accessibility prediction.

  19. HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Xiaomin Wang

    2011-01-01

    Full Text Available With the availability of more and more genome-scale protein-protein interaction (PPI networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.

  20. Regulation of PCNA-protein interactions for genome stability

    DEFF Research Database (Denmark)

    Mailand, Niels; Gibbs-Seymour, Ian; Bekker-Jensen, Simon

    2013-01-01

    Proliferating cell nuclear antigen (PCNA) has a central role in promoting faithful DNA replication, providing a molecular platform that facilitates the myriad protein-protein and protein-DNA interactions that occur at the replication fork. Numerous PCNA-associated proteins compete for binding...

  1. Milk proteins interact with goat Binder of SPerm (BSP) proteins and decrease their binding to sperm.

    Science.gov (United States)

    de Menezes, Erika Bezerra; van Tilburg, Mauricio; Plante, Geneviève; de Oliveira, Rodrigo V; Moura, Arlindo A; Manjunath, Puttaswamy

    2016-11-01

    Seminal plasma Binder of SPerm (BSP) proteins bind to sperm at ejaculation and promote capacitation. When in excess, however, BSP proteins damage the sperm membrane. It has been suggested that milk components of semen extenders associate with BSP proteins, potentially protecting sperm. Thus, this study was conducted to investigate if milk proteins interact with BSP proteins and reduce BSP binding to goat sperm. Using gel filtration chromatography, milk was incubated with goat seminal plasma proteins and loaded onto columns with and without calcium. Milk was also fractionated into parts containing mostly whey proteins or mostly caseins, incubated with seminal plasma proteins and subjected to gel filtration. Eluted fractions were evaluated by immunoblot using anti-goat BSP antibodies, confirming milk protein-BSP protein interactions. As determined by ELISA, milk proteins coated on polystyrene wells bound to increasing of goat BSP proteins. Far-western dot blots confirmed that BSP proteins bound to caseins and β-lactoglobulin in a concentration-dependent manner. Then, cauda epididymal sperm from five goats was incubated with seminal plasma; seminal plasma followed by milk; and milk followed by seminal plasma. Sperm membrane proteins were extracted and evaluated by immunoblotting. The pattern of BSP binding to sperm membrane proteins was reduced by 59.3 % when epididymal sperm were incubated with seminal plasma and then with skimmed milk (p  0.05). In conclusion, goat BSP proteins have an affinity for caseins and whey proteins. Milk reduces BSP binding to goat sperm, depending whether or not sperm had been previously exposed to seminal plasma. Such events may explain the protective effect of milk during goat sperm preservation.

  2. Exploring hierarchical and overlapping modular structure in the yeast protein interaction network

    Directory of Open Access Journals (Sweden)

    Zhao Yi

    2010-12-01

    Full Text Available Abstract Background Developing effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC for clustering vertices of a protein interaction network using a novel subgraph density measurement. Results By statistically evaluating several independent criteria, we found that ADHOC could significantly improve the outcome as compared with five previously reported density-dependent methods. We further applied ADHOC to investigate the hierarchical and overlapping modular structure in the yeast PPI network. Our method could effectively detect both protein modules and the overlaps between them, and thus greatly promote the precise prediction of protein functions. Moreover, by further assaying the intermodule layer of the yeast PPI network, we classified hubs into two types, module hubs and inter-module hubs. Each type presents distinct characteristics both in network topology and biological functions, which could conduce to the better understanding of relationship between network architecture and biological implications. Conclusions Our proposed algorithm based on the novel subgraph density measurement makes it possible to more precisely detect hierarchical and overlapping modular structures in protein interaction networks. In addition, our method also shows a strong robustness against the noise in network, which is quite critical for analyzing such a high noise network.

  3. MPact: the MIPS protein interaction resource on yeast.

    Science.gov (United States)

    Güldener, Ulrich; Münsterkötter, Martin; Oesterheld, Matthias; Pagel, Philipp; Ruepp, Andreas; Mewes, Hans-Werner; Stümpflen, Volker

    2006-01-01

    In recent years, the Munich Information Center for Protein Sequences (MIPS) yeast protein-protein interaction (PPI) dataset has been used in numerous analyses of protein networks and has been called a gold standard because of its quality and comprehensiveness [H. Yu, N. M. Luscombe, H. X. Lu, X. Zhu, Y. Xia, J. D. Han, N. Bertin, S. Chung, M. Vidal and M. Gerstein (2004) Genome Res., 14, 1107-1118]. MPact and the yeast protein localization catalog provide information related to the proximity of proteins in yeast. Beside the integration of high-throughput data, information about experimental evidence for PPIs in the literature was compiled by experts adding up to 4300 distinct PPIs connecting 1500 proteins in yeast. As the interaction data is a complementary part of CYGD, interactive mapping of data on other integrated data types such as the functional classification catalog [A. Ruepp, A. Zollner, D. Maier, K. Albermann, J. Hani, M. Mokrejs, I. Tetko, U. Güldener, G. Mannhaupt, M. Münsterkötter and H. W. Mewes (2004) Nucleic Acids Res., 32, 5539-5545] is possible. A survey of signaling proteins and comparison with pathway data from KEGG demonstrates that based on these manually annotated data only an extensive overview of the complexity of this functional network can be obtained in yeast. The implementation of a web-based PPI-analysis tool allows analysis and visualization of protein interaction networks and facilitates integration of our curated data with high-throughput datasets. The complete dataset as well as user-defined sub-networks can be retrieved easily in the standardized PSI-MI format. The resource can be accessed through http://mips.gsf.de/genre/proj/mpact.

  4. Interaction Entropy: A New Paradigm for Highly Efficient and Reliable Computation of Protein-Ligand Binding Free Energy.

    Science.gov (United States)

    Duan, Lili; Liu, Xiao; Zhang, John Z H

    2016-05-04

    Efficient and reliable calculation of protein-ligand binding free energy is a grand challenge in computational biology and is of critical importance in drug design and many other molecular recognition problems. The main challenge lies in the calculation of entropic contribution to protein-ligand binding or interaction systems. In this report, we present a new interaction entropy method which is theoretically rigorous, computationally efficient, and numerically reliable for calculating entropic contribution to free energy in protein-ligand binding and other interaction processes. Drastically different from the widely employed but extremely expensive normal mode method for calculating entropy change in protein-ligand binding, the new method calculates the entropic component (interaction entropy or -TΔS) of the binding free energy directly from molecular dynamics simulation without any extra computational cost. Extensive study of over a dozen randomly selected protein-ligand binding systems demonstrated that this interaction entropy method is both computationally efficient and numerically reliable and is vastly superior to the standard normal mode approach. This interaction entropy paradigm introduces a novel and intuitive conceptual understanding of the entropic effect in protein-ligand binding and other general interaction systems as well as a practical method for highly efficient calculation of this effect.

  5. Interaction of Tenebrio Molitor Antifreeze Protein with Ice Crystal: Insights from Molecular Dynamics Simulations.

    Science.gov (United States)

    Ramya, L; Ramakrishnan, Vigneshwar

    2016-07-01

    Antifreeze proteins (AFP) observed in cold-adapting organisms bind to ice crystals and prevent further ice growth. However, the molecular mechanism of AFP-ice binding and AFP-inhibited ice growth remains unclear. Here we report the interaction of the insect antifreeze protein (Tenebrio molitor, TmAFP) with ice crystal by molecular dynamics simulation studies. Two sets of simulations were carried out at 263 K by placing the protein near the primary prism plane (PP) and basal plane (BL) of the ice crystal. To delineate the effect of temperatures, both the PP and BL simulations were carried out at 253 K as well. The analyses revealed that the protein interacts strongly with the ice crystal in BL simulation than in PP simulation both at 263 K and 253 K. Further, it was observed that the interactions are primarily mediated through the interface waters. We also observed that as the temperature decreases, the interaction between the protein and the ice increases which can be attributed to the decreased flexibility and the increased structuring of the protein at low temperature. In essence, our study has shed light on the interaction mechanism between the TmAFP antifreeze protein and the ice crystal. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. HCVpro: Hepatitis C virus protein interaction database

    KAUST Repository

    Kwofie, Samuel K.; Schaefer, Ulf; Sundararajan, Vijayaraghava Seshadri; Bajic, Vladimir B.; Christoffels, Alan G.

    2011-01-01

    It is essential to catalog characterized hepatitis C virus (HCV) protein-protein interaction (PPI) data and the associated plethora of vital functional information to augment the search for therapies, vaccines and diagnostic biomarkers

  7. Bioactive protein-based nanofibers interact with intestinal biological components resulting in transepithelial permeation of a therapeutic protein

    DEFF Research Database (Denmark)

    Boutrup Stephansen, Karen; García-Díaz, María; Jessen, Flemming

    2015-01-01

    Proteins originating from natural sources may constitute a novel type of material for use in drug delivery. However, thorough understanding of the behavior and effects of such a material when processed into a matrix together with a drug is crucial prior to further development into a drug product....... In the present study the potential of using bioactive electrospun fish sarcoplasmic proteins (FSP) as a carrier matrix for small therapeutic proteins was demonstrated in relation to the interactions with biological components of the intestinal tract. The inherent structural and chemical properties of FSP...... as a biomaterial facilitated interactions with cells and enzymes found in the gastrointestinal tract and displayed excellent biocompatibility. More specifically, insulin was efficiently encapsulated into FSP fibers maintaining its conformation, and subsequent controlled release was obtained in simulated intestinal...

  8. Galectin-1 Is an Interactive Protein of Selenoprotein M in the Brain

    Directory of Open Access Journals (Sweden)

    Qiong Liu

    2013-11-01

    Full Text Available Selenium, an essential trace element for human health, mainly exerts its biological function through selenoproteins. Selenoprotein M (SelM is one of the highly expressed selenoproteins in the brain, but its biological effect and molecular mechanism remain unclear. Thus, the interactive protein of SelM was investigated in this paper to guide further study. In order to avoid protein translational stop, the selenocysteine-encoding UGA inside the open reading frame of SelM was site-directly changed to the cysteine-encoding UGC to generate the SelM' mutant. Meanwhile, its N terminal transmembrane signal peptide was also cut off. This truncated SelM' was used to screen a human fetal brain cDNA library by the yeast two-hybrid system. A new interactive protein of SelM' was found to be galectin-1 (Gal-1. This protein-protein interaction was further verified by the results of fluorescence resonance energy transfer techniques, glutathione S-transferase pull-down and co-immunoprecipitation assays. As Gal-1 plays important roles in preventing neurodegeneration and promoting neuroprotection in the brain, the interaction between SelM' and Gal-1 displays a new direction for studying the biological function of SelM in the human brain.

  9. Comparative Genomics and Disorder Prediction Identify Biologically Relevant SH3 Protein Interactions.

    Directory of Open Access Journals (Sweden)

    2005-08-01

    Full Text Available Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from Saccharomyces cerevisiae(S. mikatae, S. bayanus, and S. paradoxus, or a long time ago (Neurospora crassa and Schizosaccharomyces pombe, contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.

  10. Comparative genomics and disorder prediction identify biologically relevant SH3 protein interactions.

    Directory of Open Access Journals (Sweden)

    Pedro Beltrao

    2005-08-01

    Full Text Available Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from Saccharomyces cerevisiae(S. mikatae, S. bayanus, and S. paradoxus, or a long time ago (Neurospora crassa and Schizosaccharomyces pombe, contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.

  11. Identification of novel direct protein-protein interactions by irradiating living cells with femtosecond UV laser pulses.

    Science.gov (United States)

    Itri, Francesco; Monti, Daria Maria; Chino, Marco; Vinciguerra, Roberto; Altucci, Carlo; Lombardi, Angela; Piccoli, Renata; Birolo, Leila; Arciello, Angela

    2017-10-07

    The identification of protein-protein interaction networks in living cells is becoming increasingly fundamental to elucidate main biological processes and to understand disease molecular bases on a system-wide level. We recently described a method (LUCK, Laser UV Cross-linKing) to cross-link interacting protein surfaces in living cells by UV laser irradiation. By using this innovative methodology, that does not require any protein modification or cell engineering, here we demonstrate that, upon UV laser irradiation of HeLa cells, a direct interaction between GAPDH and alpha-enolase was "frozen" by a cross-linking event. We validated the occurrence of this direct interaction by co-immunoprecipitation and Immuno-FRET analyses. This represents a proof of principle of the LUCK capability to reveal direct protein interactions in their physiological environment. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. SynechoNET: integrated protein-protein interaction database of a model cyanobacterium Synechocystis sp. PCC 6803

    OpenAIRE

    Kim, Woo-Yeon; Kang, Sungsoo; Kim, Byoung-Chul; Oh, Jeehyun; Cho, Seongwoong; Bhak, Jong; Choi, Jong-Soon

    2008-01-01

    Background Cyanobacteria are model organisms for studying photosynthesis, carbon and nitrogen assimilation, evolution of plant plastids, and adaptability to environmental stresses. Despite many studies on cyanobacteria, there is no web-based database of their regulatory and signaling protein-protein interaction networks to date. Description We report a database and website SynechoNET that provides predicted protein-protein interactions. SynechoNET shows cyanobacterial domain-domain interactio...

  13. A human protein interaction network shows conservation of aging processes between human and invertebrate species.

    Directory of Open Access Journals (Sweden)

    Russell Bell

    2009-03-01

    Full Text Available We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species. This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches. The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast, nematode, or fly, and 2,163 additional human proteins that interact with these homologs. Overall, the network consists of 3,271 binary interactions among 2,338 unique proteins. A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18.8 partners. Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance. To examine the relationship of this network to human aging phenotypes, we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle. In the case of both the longevity protein homologs and their interactors, we observed enrichments for differentially expressed genes in the network. To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates, homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi. Of 18 genes tested, 33% extended life span when knocked-down in Caenorhabditis elegans. These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging. They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins.

  14. Computational design of protein interactions: designing proteins that neutralize influenza by inhibiting its hemagglutinin surface protein

    Science.gov (United States)

    Fleishman, Sarel

    2012-02-01

    Molecular recognition underlies all life processes. Design of interactions not seen in nature is a test of our understanding of molecular recognition and could unlock the vast potential of subtle control over molecular interaction networks, allowing the design of novel diagnostics and therapeutics for basic and applied research. We developed the first general method for designing protein interactions. The method starts by computing a region of high affinity interactions between dismembered amino acid residues and the target surface and then identifying proteins that can harbor these residues. Designs are tested experimentally for binding the target surface and successful ones are affinity matured using yeast cell surface display. Applied to the conserved stem region of influenza hemagglutinin we designed two unrelated proteins that, following affinity maturation, bound hemagglutinin at subnanomolar dissociation constants. Co-crystal structures of hemagglutinin bound to the two designed binders were within 1Angstrom RMSd of their models, validating the accuracy of the design strategy. One of the designed proteins inhibits the conformational changes that underlie hemagglutinin's cell-invasion functions and blocks virus infectivity in cell culture, suggesting that such proteins may in future serve as diagnostics and antivirals against a wide range of pathogenic influenza strains. We have used this method to obtain experimentally validated binders of several other target proteins, demonstrating the generality of the approach. We discuss the combination of modeling and high-throughput characterization of design variants which has been key to the success of this approach, as well as how we have used the data obtained in this project to enhance our understanding of molecular recognition. References: Science 332:816 JMB, in press Protein Sci 20:753

  15. Novel fusion protein approach for efficient high-throughput screening of small molecule-mediating protein-protein interactions in cells and living animals.

    Science.gov (United States)

    Paulmurugan, Ramasamy; Gambhir, Sanjiv S

    2005-08-15

    Networks of protein interactions execute many different intracellular pathways. Small molecules either synthesized within the cell or obtained from the external environment mediate many of these protein-protein interactions. The study of these small molecule-mediated protein-protein interactions is important in understanding abnormal signal transduction pathways in a variety of disorders, as well as in optimizing the process of drug development and validation. In this study, we evaluated the rapamycin-mediated interaction of the human proteins FK506-binding protein (FKBP12) rapamycin-binding domain (FRB) and FKBP12 by constructing a fusion of these proteins with a split-Renilla luciferase or a split enhanced green fluorescent protein (split-EGFP) such that complementation of the reporter fragments occurs in the presence of rapamycin. Different linker peptides in the fusion protein were evaluated for the efficient maintenance of complemented reporter activity. This system was studied in both cell culture and xenografts in living animals. We found that peptide linkers with two or four EAAAR repeat showed higher protein-protein interaction-mediated signal with lower background signal compared with having no linker or linkers with amino acid sequences GGGGSGGGGS, ACGSLSCGSF, and ACGSLSCGSFACGSLSCGSF. A 9 +/- 2-fold increase in signal intensity both in cell culture and in living mice was seen compared with a system that expresses both reporter fragments and the interacting proteins separately. In this fusion system, rapamycin induced heterodimerization of the FRB and FKBP12 moieties occurred rapidly even at very lower concentrations (0.00001 nmol/L) of rapamycin. For a similar fusion system employing split-EGFP, flow cytometry analysis showed significant level of rapamycin-induced complementation.

  16. Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure.

    Science.gov (United States)

    Li, Tao; Li, Qian-Zhong

    2012-11-07

    RNA-protein interactions play important roles in various biological processes. The precise detection of RNA-protein interaction sites is very important for understanding essential biological processes and annotating the function of the proteins. In this study, based on various features from amino acid sequence and structure, including evolutionary information, solvent accessible surface area and torsion angles (φ, ψ) in the backbone structure of the polypeptide chain, a computational method for predicting RNA-binding sites in proteins is proposed. When the method is applied to predict RNA-binding sites in three datasets: RBP86 containing 86 protein chains, RBP107 containing 107 proteins chains and RBP109 containing 109 proteins chains, better sensitivities and specificities are obtained compared to previously published methods in five-fold cross-validation tests. In order to make further examination for the efficiency of our method, the RBP107 dataset is used as training set, RBP86 and RBP109 datasets are used as the independent test sets. In addition, as examples of our prediction, RNA-binding sites in a few proteins are presented. The annotated results are consistent with the PDB annotation. These results show that our method is useful for annotating RNA binding sites of novel proteins.

  17. 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.

  18. The drug-minded protein interaction database (DrumPID) for efficient target analysis and drug development.

    Science.gov (United States)

    Kunz, Meik; Liang, Chunguang; Nilla, Santosh; Cecil, Alexander; Dandekar, Thomas

    2016-01-01

    The drug-minded protein interaction database (DrumPID) has been designed to provide fast, tailored information on drugs and their protein networks including indications, protein targets and side-targets. Starting queries include compound, target and protein interactions and organism-specific protein families. Furthermore, drug name, chemical structures and their SMILES notation, affected proteins (potential drug targets), organisms as well as diseases can be queried including various combinations and refinement of searches. Drugs and protein interactions are analyzed in detail with reference to protein structures and catalytic domains, related compound structures as well as potential targets in other organisms. DrumPID considers drug functionality, compound similarity, target structure, interactome analysis and organismic range for a compound, useful for drug development, predicting drug side-effects and structure-activity relationships.Database URL:http://drumpid.bioapps.biozentrum.uni-wuerzburg.de. © The Author(s) 2016. Published by Oxford University Press.

  19. The Protein Interaction Network of Bacteriophage Lambda with Its Host, Escherichia coli

    Science.gov (United States)

    Blasche, Sonja; Wuchty, Stefan; Rajagopala, Seesandra V.

    2013-01-01

    Although most of the 73 open reading frames (ORFs) in bacteriophage λ have been investigated intensively, the function of many genes in host-phage interactions remains poorly understood. Using yeast two-hybrid screens of all lambda ORFs for interactions with its host Escherichia coli, we determined a raw data set of 631 host-phage interactions resulting in a set of 62 high-confidence interactions after multiple rounds of retesting. These links suggest novel regulatory interactions between the E. coli transcriptional network and lambda proteins. Targeted host proteins and genes required for lambda infection are enriched among highly connected proteins, suggesting that bacteriophages resemble interaction patterns of human viruses. Lambda tail proteins interact with both bacterial fimbrial proteins and E. coli proteins homologous to other phage proteins. Lambda appears to dramatically differ from other phages, such as T7, because of its unusually large number of modified and processed proteins, which reduces the number of host-virus interactions detectable by yeast two-hybrid screens. PMID:24049175

  20. Functional structural motifs for protein-ligand, protein-protein, and protein-nucleic acid interactions and their connection to supersecondary structures.

    Science.gov (United States)

    Kinjo, Akira R; Nakamura, Haruki

    2013-01-01

    Protein functions are mediated by interactions between proteins and other molecules. One useful approach to analyze protein functions is to compare and classify the structures of interaction interfaces of proteins. Here, we describe the procedures for compiling a database of interface structures and efficiently comparing the interface structures. To do so requires a good understanding of the data structures of the Protein Data Bank (PDB). Therefore, we also provide a detailed account of the PDB exchange dictionary necessary for extracting data that are relevant for analyzing interaction interfaces and secondary structures. We identify recurring structural motifs by classifying similar interface structures, and we define a coarse-grained representation of supersecondary structures (SSS) which represents a sequence of two or three secondary structure elements including their relative orientations as a string of four to seven letters. By examining the correspondence between structural motifs and SSS strings, we show that no SSS string has particularly high propensity to be found interaction interfaces in general, indicating any SSS can be used as a binding interface. When individual structural motifs are examined, there are some SSS strings that have high propensity for particular groups of structural motifs. In addition, it is shown that while the SSS strings found in particular structural motifs for nonpolymer and protein interfaces are as abundant as in other structural motifs that belong to the same subunit, structural motifs for nucleic acid interfaces exhibit somewhat stronger preference for SSS strings. In regard to protein folds, many motif-specific SSS strings were found across many folds, suggesting that SSS may be a useful description to investigate the universality of ligand binding modes.

  1. Orphan Nuclear Receptor NR4A1 Binds a Novel Protein Interaction Site on Anti-apoptotic B Cell Lymphoma Gene 2 Family Proteins.

    Science.gov (United States)

    Godoi, Paulo H C; Wilkie-Grantham, Rachel P; Hishiki, Asami; Sano, Renata; Matsuzawa, Yasuko; Yanagi, Hiroko; Munte, Claudia E; Chen, Ya; Yao, Yong; Marassi, Francesca M; Kalbitzer, Hans R; Matsuzawa, Shu-Ichi; Reed, John C

    2016-07-01

    B cell lymphoma gene 2 (Bcl-2) family proteins are key regulators of programmed cell death and important targets for drug discovery. Pro-apoptotic and anti-apoptotic Bcl-2 family proteins reciprocally modulate their activities in large part through protein interactions involving a motif known as BH3 (Bcl-2 homology 3). Nur77 is an orphan member of the nuclear receptor family that lacks a BH3 domain but nevertheless binds certain anti-apoptotic Bcl-2 family proteins (Bcl-2, Bfl-1, and Bcl-B), modulating their effects on apoptosis and autophagy. We used a combination of NMR spectroscopy-based methods, mutagenesis, and functional studies to define the interaction site of a Nur77 peptide on anti-apoptotic Bcl-2 family proteins and reveal a novel interaction surface. Nur77 binds adjacent to the BH3 peptide-binding crevice, suggesting the possibility of cross-talk between these discrete binding sites. Mutagenesis of residues lining the identified interaction site on Bcl-B negated the interaction with Nur77 protein in cells and prevented Nur77-mediated modulation of apoptosis and autophagy. The findings establish a new protein interaction site with the potential to modulate the apoptosis and autophagy mechanisms governed by Bcl-2 family proteins. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  2. Identification of in planta protein–protein interactions using IP-MS

    NARCIS (Netherlands)

    Jamge, Suraj; Angenent, Gerco; Bemer, Marian

    2018-01-01

    Gene regulation by transcription factors involves complex protein interaction networks, which include chromatin remodeling and modifying proteins as an integral part. Decoding these protein interactions is crucial for our understanding of chromatin-mediated gene regulation. Here, we describe a

  3. Computational analysis of RNA-protein interaction interfaces via the Voronoi diagram.

    Science.gov (United States)

    Mahdavi, Sedigheh; Mohades, Ali; Salehzadeh Yazdi, Ali; Jahandideh, Samad; Masoudi-Nejad, Ali

    2012-01-21

    Cellular functions are mediated by various biological processes including biomolecular interactions, such as protein-protein, DNA-protein and RNA-protein interactions in which RNA-Protein interactions are indispensable for many biological processes like cell development and viral replication. Unlike the protein-protein and protein-DNA interactions, accurate mechanisms and structures of the RNA-Protein complexes are not fully understood. A large amount of theoretical evidence have shown during the past several years that computational geometry is the first pace in understanding the binding profiles and plays a key role in the study of intricate biological structures, interactions and complexes. In this paper, RNA-Protein interaction interface surface is computed via the weighted Voronoi diagram of atoms. Using two filter operations provides a natural definition for interface atoms as classic methods. Unbounded parts of Voronoi facets that are far from the complex are trimmed using modified convex hull of atom centers. This algorithm is implemented to a database with different RNA-Protein complexes extracted from Protein Data Bank (PDB). Afterward, the features of interfaces have been computed and compared with classic method. The results show high correlation coefficients between interface size in the Voronoi model and the classical model based on solvent accessibility, as well as high accuracy and precision in comparison to classical model. Copyright © 2011 Elsevier Ltd. All rights reserved.

  4. Notable Aspects of Glycan-Protein Interactions

    Directory of Open Access Journals (Sweden)

    Miriam Cohen

    2015-09-01

    Full Text Available This mini review highlights several interesting aspects of glycan-mediated interactions that are common between cells, bacteria, and viruses. Glycans are ubiquitously found on all living cells, and in the extracellular milieu of multicellular organisms. They are known to mediate initial binding and recognition events of both immune cells and pathogens with their target cells or tissues. The host target tissues are hidden under a layer of secreted glycosylated decoy targets. In addition, pathogens can utilize and display host glycans to prevent identification as foreign by the host’s immune system (molecular mimicry. Both the host and pathogens continually evolve. The host evolves to prevent infection and the pathogens evolve to evade host defenses. Many pathogens express both glycan-binding proteins and glycosidases. Interestingly, these proteins are often located at the tip of elongated protrusions in bacteria, or in the leading edge of the cell. Glycan-protein interactions have low affinity and, as a result, multivalent interactions are often required to achieve biologically relevant binding. These enable dynamic forms of adhesion mechanisms, reviewed here, and include rolling (cells, stick and roll (bacteria or surfacing (viruses.

  5. Yeast Interacting Proteins Database: YDL239C, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p...cription Protein required for spore wall formation, thought to mediate assembly of a Don1p-containing structure at the leading

  6. Yeast Interacting Proteins Database: YDL239C, YPL070W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p...cription Protein required for spore wall formation, thought to mediate assembly of a Don1p-containing structure at the leading

  7. Yeast Interacting Proteins Database: YDL239C, YML042W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p...iption Protein required for spore wall formation, thought to mediate assembly of a Don1p-containing structure at the leading

  8. Yeast Interacting Proteins Database: YDL239C, YKL103C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p...ait description Protein required for spore wall formation, thought to mediate assembly of a Don1p-containing structure at the leading

  9. 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.

  10. Interactive effect of reduced pollen availability and Varroa destructor infestation limits growth and protein content of young honey bees

    NARCIS (Netherlands)

    Dooremalen, van C.; Stam, E.; Gerritsen, L.J.M.; Cornelissen, B.; Steen, van der J.J.M.; Langevelde, van F.; Blacquiere, T.

    2013-01-01

    Varroa destructor in combination with one or more stressors, such as low food availability or chemical exposure, is considered to be one of the main causes for honey bee colony losses. We examined the inter-active effect of pollen availability on the protein content and body weight of young bees

  11. Characterization of interactions between inclusion membrane proteins from Chlamydia trachomatis

    Directory of Open Access Journals (Sweden)

    Emilie eGauliard

    2015-02-01

    Full Text Available Chlamydiae are obligate intracellular pathogens of eukaryotes. The bacteria grow in an intracellular vesicle called an inclusion, the membrane of which is heavily modified by chlamydial proteins called Incs (Inclusion membrane proteins. Incs represent 7-10% of the genomes of Chlamydia and, given their localization at the interface between the host and the pathogen, likely play a key role in the development and pathogenesis of the bacterium. However, their functions remain largely unknown. Here, we characterized the interaction properties between various Inc proteins of C. trachomatis, using a bacterial two-hybrid (BACTH method suitable for detecting interactions between integral membrane proteins. To validate this approach, we first examined the oligomerization properties of the well-characterized IncA protein and showed that both the cytoplasmic domain and the transmembrane region independently contribute to IncA oligomerization. We then analyzed a set of Inc proteins and identified novel interactions between these components. Two small Incs, IncF and Ct222, were found here to interact with many other Inc proteins and may thus represent interaction nodes within the inclusion membrane. Our data suggest that the Inc proteins may assemble in the membrane of the inclusion to form specific multi-molecular complexes in an hierarchical and temporal manner. These studies will help to better define the putative functions of the Inc proteins in the infectious process of Chlamydia.

  12. The nonstructural protein 8 (nsp8) of the SARS coronavirus interacts with its ORF6 accessory protein

    International Nuclear Information System (INIS)

    Kumar, Purnima; Gunalan, Vithiagaran; Liu Boping; Chow, Vincent T.K.; Druce, Julian; Birch, Chris; Catton, Mike; Fielding, Burtram C.; Tan, Yee-Joo; Lal, Sunil K.

    2007-01-01

    Severe acute respiratory syndrome (SARS) coronavirus (SARS-CoV) caused a severe outbreak in several regions of the world in 2003. The SARS-CoV genome is predicted to contain 14 functional open reading frames (ORFs). The first ORF (1a and 1b) encodes a large polyprotein that is cleaved into nonstructural proteins (nsp). The other ORFs encode for four structural proteins (spike, membrane, nucleocapsid and envelope) as well as eight SARS-CoV-specific accessory proteins (3a, 3b, 6, 7a, 7b, 8a, 8b and 9b). In this report we have cloned the predicted nsp8 gene and the ORF6 gene of the SARS-CoV and studied their abilities to interact with each other. We expressed the two proteins as fusion proteins in the yeast two-hybrid system to demonstrate protein-protein interactions and tested the same using a yeast genetic cross. Further the strength of the interaction was measured by challenging growth of the positive interaction clones on increasing gradients of 2-amino trizole. The interaction was then verified by expressing both proteins separately in-vitro in a coupled-transcription translation system and by coimmunoprecipitation in mammalian cells. Finally, colocalization experiments were performed in SARS-CoV infected Vero E6 mammalian cells to confirm the nsp8-ORF6 interaction. To the best of our knowledge, this is the first report of the interaction between a SARS-CoV accessory protein and nsp8 and our findings suggest that ORF6 protein may play a role in virus replication

  13. Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols.

    Science.gov (United States)

    Li, Minghui; Goncearenco, Alexander; Panchenko, Anna R

    2017-01-01

    In this review we describe a protocol to annotate the effects of missense mutations on proteins, their functions, stability, and binding. For this purpose we present a collection of the most comprehensive databases which store different types of sequencing data on missense mutations, we discuss their relationships, possible intersections, and unique features. Next, we suggest an annotation workflow using the state-of-the art methods and highlight their usability, advantages, and limitations for different cases. Finally, we address a particularly difficult problem of deciphering the molecular mechanisms of mutations on proteins and protein complexes to understand the origins and mechanisms of diseases.

  14. Prediction of protein-protein interaction sites in sequences and 3D structures by random forests.

    Directory of Open Access Journals (Sweden)

    Mile Sikić

    2009-01-01

    Full Text Available Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i a combination of sequence- and structure-derived parameters and (ii sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras-Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.

  15. Interactions of Ras proteins with the plasma membrane and their roles in signaling.

    Science.gov (United States)

    Eisenberg, Sharon; Henis, Yoav I

    2008-01-01

    The complex dynamic structure of the plasma membrane plays critical roles in cellular signaling; interactions with the membrane lipid milieu, spatial segregation within and between cellular membranes and/or targeting to specific membrane-associated scaffolds are intimately involved in many signal transduction pathways. In this review, we focus on the membrane interactions of Ras proteins. These small GTPases play central roles in the regulation of cell growth and proliferation, and their excessive activation is commonly encountered in human tumors. Ras proteins associate with the membrane continuously via C-terminal lipidation and additional interactions in both their inactive and active forms; this association, as well as the targeting of specific Ras isoforms to plasma membrane microdomains and to intracellular organelles, have recently been implicated in Ras signaling and oncogenic potential. We discuss biochemical and biophysical evidence for the roles of specific domains of Ras proteins in mediating their association with the plasma membrane, and consider the potential effects of lateral segregation and interactions with membrane-associated protein assemblies on the signaling outcomes.

  16. A simple detection method for low-affinity membrane protein interactions by baculoviral display.

    Directory of Open Access Journals (Sweden)

    Toshiko Sakihama

    Full Text Available BACKGROUND: Membrane protein interactions play an important role in cell-to-cell recognition in various biological activities such as in the immune or neural system. Nevertheless, there has remained the major obstacle of expression of the membrane proteins in their active form. Recently, we and other investigators found that functional membrane proteins express on baculovirus particles (budded virus, BV. In this study, we applied this BV display system to detect interaction between membrane proteins important for cell-to-cell interaction in immune system. METHODOLOGY/PRINCIPAL FINDINGS: We infected Sf9 cells with recombinant baculovirus encoding the T cell membrane protein CD2 or its ligand CD58 and recovered the BV. We detected specific interaction between CD2-displaying BV and CD58-displaying BV by an enzyme-linked immunosorbent assay (ELISA. Using this system, we also detected specific interaction between two other membrane receptor-ligand pairs, CD40-CD40 ligand (CD40L, and glucocorticoid-induced TNFR family-related protein (GITR-GITR ligand (GITRL. Furthermore, we observed specific binding of BV displaying CD58, CD40L, or GITRL to cells naturally expressing their respective receptors by flowcytometric analysis using anti-baculoviral gp64 antibody. Finally we isolated CD2 cDNA from a cDNA expression library by magnetic separation using CD58-displaying BV and anti-gp64 antibody. CONCLUSIONS: We found the BV display system worked effectively in the detection of the interaction of membrane proteins. Since various membrane proteins and their oligomeric complexes can be displayed on BV in the native form, this BV display system should prove highly useful in the search for natural ligands or to develop screening systems for therapeutic antibodies and/or compounds.

  17. CellMap visualizes protein-protein interactions and subcellular localization

    Science.gov (United States)

    Dallago, Christian; Goldberg, Tatyana; Andrade-Navarro, Miguel Angel; Alanis-Lobato, Gregorio; Rost, Burkhard

    2018-01-01

    Many tools visualize protein-protein interaction (PPI) networks. The tool introduced here, CellMap, adds one crucial novelty by visualizing PPI networks in the context of subcellular localization, i.e. the location in the cell or cellular component in which a PPI happens. Users can upload images of cells and define areas of interest against which PPIs for selected proteins are displayed (by default on a cartoon of a cell). Annotations of localization are provided by the user or through our in-house database. The visualizer and server are written in JavaScript, making CellMap easy to customize and to extend by researchers and developers. PMID:29497493

  18. A scalable double-barcode sequencing platform for characterization of dynamic protein-protein interactions.

    Science.gov (United States)

    Schlecht, Ulrich; Liu, Zhimin; Blundell, Jamie R; St Onge, Robert P; Levy, Sasha F

    2017-05-25

    Several large-scale efforts have systematically catalogued protein-protein interactions (PPIs) of a cell in a single environment. However, little is known about how the protein interactome changes across environmental perturbations. Current technologies, which assay one PPI at a time, are too low throughput to make it practical to study protein interactome dynamics. Here, we develop a highly parallel protein-protein interaction sequencing (PPiSeq) platform that uses a novel double barcoding system in conjunction with the dihydrofolate reductase protein-fragment complementation assay in Saccharomyces cerevisiae. PPiSeq detects PPIs at a rate that is on par with current assays and, in contrast with current methods, quantitatively scores PPIs with enough accuracy and sensitivity to detect changes across environments. Both PPI scoring and the bulk of strain construction can be performed with cell pools, making the assay scalable and easily reproduced across environments. PPiSeq is therefore a powerful new tool for large-scale investigations of dynamic PPIs.

  19. Foot-printing of Protein Interactions by Tritium Labeling

    International Nuclear Information System (INIS)

    Mousseau, Guillaume; Thomas, Olivier P.; Agez, Morgane; Thai, Robert; Cintrat, Jean-Christophe; Rousseau, Bernard; Raffy, Quentin; Renault, Jean Philippe; Pin, Serge; Ochsenbein, Francoise

    2010-01-01

    A new foot-printing method for mapping protein interactions has been developed, using tritium as a radioactive label. As residues involved in an interaction are less labeled when the complex is formed, they can be identified via comparison of the tritium incorporation of each residue of the bound protein with that of the unbound one. Application of this foot-printing method to the complex formed by the histone H3 fragment H3 122-135 and the protein hAsflA 1-156 afforded data in good agreement with NMR results. (authors)

  20. KFC Server: interactive forecasting of protein interaction hot spots.

    Science.gov (United States)

    Darnell, Steven J; LeGault, Laura; Mitchell, Julie C

    2008-07-01

    The KFC Server is a web-based implementation of the KFC (Knowledge-based FADE and Contacts) model-a machine learning approach for the prediction of binding hot spots, or the subset of residues that account for most of a protein interface's; binding free energy. The server facilitates the automated analysis of a user submitted protein-protein or protein-DNA interface and the visualization of its hot spot predictions. For each residue in the interface, the KFC Server characterizes its local structural environment, compares that environment to the environments of experimentally determined hot spots and predicts if the interface residue is a hot spot. After the computational analysis, the user can visualize the results using an interactive job viewer able to quickly highlight predicted hot spots and surrounding structural features within the protein structure. The KFC Server is accessible at http://kfc.mitchell-lab.org.

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    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

  3. 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.

  4. Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold.

    Directory of Open Access Journals (Sweden)

    Stefan M Ivanov

    Full Text Available An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners.

  5. Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold.

    Science.gov (United States)

    Ivanov, Stefan M; Cawley, Andrew; Huber, Roland G; Bond, Peter J; Warwicker, Jim

    2017-01-01

    An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners.

  6. Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks.

    Science.gov (United States)

    Lin, Wen-Hsien; Liu, Wei-Chung; Hwang, Ming-Jing

    2009-03-11

    Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure. Compared to a random selection of proteins, house-keeping gene-encoded proteins tended to have a greater number of directly interacting neighbors and occupy network positions in several shortest paths of interaction between protein pairs, whereas tissue-specific gene-encoded proteins did not. In addition, house-keeping gene-encoded proteins tended to connect with other house-keeping gene-encoded proteins in all tissue types, whereas tissue-specific gene-encoded proteins also tended to connect with other tissue-specific gene-encoded proteins, but only in approximately half of the tissue types examined. Our analysis showed that house-keeping gene-encoded proteins tend to occupy important network positions, while those encoded by tissue-specific genes do not. The biological implications of our findings were discussed and we proposed a hypothesis regarding how cells organize their protein tools in protein-protein interaction networks. Our results led us to speculate that house-keeping gene-encoded proteins might form a core in human protein-protein interaction networks, while clusters of tissue-specific gene

  7. Femtosecond UV-laser pulses to unveil protein-protein interactions in living cells.

    Science.gov (United States)

    Itri, Francesco; Monti, Daria M; Della Ventura, Bartolomeo; Vinciguerra, Roberto; Chino, Marco; Gesuele, Felice; Lombardi, Angelina; Velotta, Raffaele; Altucci, Carlo; Birolo, Leila; Piccoli, Renata; Arciello, Angela

    2016-02-01

    A hallmark to decipher bioprocesses is to characterize protein-protein interactions in living cells. To do this, the development of innovative methodologies, which do not alter proteins and their natural environment, is particularly needed. Here, we report a method (LUCK, Laser UV Cross-linKing) to in vivo cross-link proteins by UV-laser irradiation of living cells. Upon irradiation of HeLa cells under controlled conditions, cross-linked products of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were detected, whose yield was found to be a linear function of the total irradiation energy. We demonstrated that stable dimers of GAPDH were formed through intersubunit cross-linking, as also observed when the pure protein was irradiated by UV-laser in vitro. We proposed a defined patch of aromatic residues located at the enzyme subunit interface as the cross-linking sites involved in dimer formation. Hence, by this technique, UV-laser is able to photofix protein surfaces that come in direct contact. Due to the ultra-short time scale of UV-laser-induced cross-linking, this technique could be extended to weld even transient protein interactions in their native context.

  8. 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.

  9. A two-hybrid assay to study protein interactions within the secretory pathway.

    Directory of Open Access Journals (Sweden)

    Danielle H Dube

    Full Text Available Interactions of transcriptional activators are difficult to study using transcription-based two-hybrid assays due to potent activation resulting in false positives. Here we report the development of the Golgi two-hybrid (G2H, a method that interrogates protein interactions within the Golgi, where transcriptional activators can be assayed with negligible background. The G2H relies on cell surface glycosylation to report extracellularly on protein-protein interactions occurring within the secretory pathway. In the G2H, protein pairs are fused to modular domains of the reporter glycosyltransferase, Och1p, and proper cell wall formation due to Och1p activity is observed only when a pair of proteins interacts. Cells containing interacting protein pairs are identified by selectable phenotypes associated with Och1p activity and proper cell wall formation: cells that have interacting proteins grow under selective conditions and display weak wheat germ agglutinin (WGA binding by flow cytometry, whereas cells that lack interacting proteins display stunted growth and strong WGA binding. Using this assay, we detected the interaction between transcription factor MyoD and its binding partner Id2. Interfering mutations along the MyoD:Id2 interaction interface ablated signal in the G2H assay. Furthermore, we used the G2H to detect interactions of the activation domain of Gal4p with a variety of binding partners. Finally, selective conditions were used to enrich for cells encoding interacting partners. The G2H detects protein-protein interactions that cannot be identified via traditional two-hybrid methods and should be broadly useful for probing previously inaccessible subsets of the interactome, including transcriptional activators and proteins that traffic through the secretory pathway.

  10. 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

  11. Evaluation of clustering algorithms for protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    van Helden Jacques

    2006-11-01

    Full Text Available Abstract Background Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism. In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies. High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL, Restricted Neighborhood Search Clustering (RNSC, Super Paramagnetic Clustering (SPC, and Molecular Complex Detection (MCODE. Results A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. Conclusion This

  12. Nanoparticle-assay marker interaction: effects on nanotoxicity assessment

    Science.gov (United States)

    Zhao, Xinxin; Xiong, Sijing; Huang, Liwen Charlotte; Ng, Kee Woei; Loo, Say Chye Joachim

    2015-01-01

    Protein-based cytotoxicity assays such as lactate dehydrogenase (LDH) and tumor necrosis factor-alpha (TNF-α) are commonly used in cytotoxic evaluation of nanoparticles (NPs) despite numerous reports on possible interactions with protein markers in these assays that can confound the results obtained. In this study, conventional cytotoxicity assays where assay markers may (LDH and TNF- α) or may not (PicoGreen and WST-8) come into contact with NPs were used to evaluate the cytotoxicity of NPs. The findings revealed selective interactions between negatively charged protein assay markers (LDH and TNF- α) and positively charged ZnO NPs under abiotic conditions. The adsorption and interaction with these protein assay markers were strongly influenced by surface charge, concentration, and specific surface area of the NPs, thereby resulting in less than accurate cytotoxic measurements, as observed from actual cell viability measurements. An improved protocol for LDH assay was, therefore, proposed and validated by eliminating any effects associated with protein-particle interactions. In view of this, additional measures and precautions should be taken when evaluating cytotoxicity of NPs with standard protein-based assays, particularly when they are of opposite charges.

  13. 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.

  14. Identification of NAD interacting residues in proteins

    Directory of Open Access Journals (Sweden)

    Raghava Gajendra PS

    2010-03-01

    Full Text Available Abstract Background Small molecular cofactors or ligands play a crucial role in the proper functioning of cells. Accurate annotation of their target proteins and binding sites is required for the complete understanding of reaction mechanisms. Nicotinamide adenine dinucleotide (NAD+ or NAD is one of the most commonly used organic cofactors in living cells, which plays a critical role in cellular metabolism, storage and regulatory processes. In the past, several NAD binding proteins (NADBP have been reported in the literature, which are responsible for a wide-range of activities in the cell. Attempts have been made to derive a rule for the binding of NAD+ to its target proteins. However, so far an efficient model could not be derived due to the time consuming process of structure determination, and limitations of similarity based approaches. Thus a sequence and non-similarity based method is needed to characterize the NAD binding sites to help in the annotation. In this study attempts have been made to predict NAD binding proteins and their interacting residues (NIRs from amino acid sequence using bioinformatics tools. Results We extracted 1556 proteins chains from 555 NAD binding proteins whose structure is available in Protein Data Bank. Then we removed all redundant protein chains and finally obtained 195 non-redundant NAD binding protein chains, where no two chains have more than 40% sequence identity. In this study all models were developed and evaluated using five-fold cross validation technique on the above dataset of 195 NAD binding proteins. While certain type of residues are preferred (e.g. Gly, Tyr, Thr, His in NAD interaction, residues like Ala, Glu, Leu, Lys are not preferred. A support vector machine (SVM based method has been developed using various window lengths of amino acid sequence for predicting NAD interacting residues and obtained maximum Matthew's correlation coefficient (MCC 0.47 with accuracy 74.13% at window length 17

  15. Interaction of a non-histone chromatin protein (high-mobility group protein 2) with DNA

    International Nuclear Information System (INIS)

    Goodwin, G.H.; Shooter, K.V.; Johns, E.W.

    1975-01-01

    The interaction with DNA of the calf thymus chromatin non-histone protein termed the high-mobility group protein 2 has been studied by sedimentation analysis in the ultracentrifuge and by measuring the binding of the 125 I-labelled protein to DNA. The results have been compared with those obtained previously by us [Eur. J. Biochem. (1974) 47, 263-270] for the interaction of high-mobility group protein 1 with DNA. Although the binding parameters are similar for these two proteins, high-mobility group protein 2 differs from high-mobility group protein 1 in that the former appears to change the shape of the DNA to a more compact form. The molecular weight of high-mobility group protein 2 has been determined by equilibrium sedimentation and a mean value of 26,000 was obtained. A low level of nuclease activity detected in one preparation of high-mobility group protein 2 has been investigated. (orig.) [de

  16. Biophysics of DNA-Protein Interactions From Single Molecules to Biological Systems

    CERN Document Server

    Williams, Mark C

    2011-01-01

    This book presents a concise overview of current research on the biophysics of DNA-protein interactions. A wide range of new and classical methods are presented by authors investigating physical mechanisms by which proteins interact with DNA. For example, several chapters address the mechanisms by which proteins search for and recognize specific binding sites on DNA, a process critical for cellular function. Single molecule methods such as force spectroscopy as well as fluorescence imaging and tracking are described in these chapters as well as other parts of the book that address the dynamics of protein-DNA interactions. Other important topics include the mechanisms by which proteins engage DNA sequences and/or alter DNA structure. These simple but important model interactions are then placed in the broader biological context with discussion of larger protein-DNA complexes . Topics include replication forks, recombination complexes, DNA repair interactions, and ultimately, methods to understand the chromatin...

  17. Protein solubility and folding enhancement by interaction with RNA.

    Directory of Open Access Journals (Sweden)

    Seong Il Choi

    Full Text Available While basic mechanisms of several major molecular chaperones are well understood, this machinery has been known to be involved in folding of only limited number of proteins inside the cells. Here, we report a chaperone type of protein folding facilitated by interaction with RNA. When an RNA-binding module is placed at the N-terminus of aggregation-prone target proteins, this module, upon binding with RNA, further promotes the solubility of passenger proteins, potentially leading to enhancement of proper protein folding. Studies on in vitro refolding in the presence of RNA, coexpression of RNA molecules in vivo and the mutants with impaired RNA binding ability suggests that RNA can exert chaperoning effect on their bound proteins. The results suggest that RNA binding could affect the overall kinetic network of protein folding pathway in favor of productive folding over off-pathway aggregation. In addition, the RNA binding-mediated solubility enhancement is extremely robust for increasing soluble yield of passenger proteins and could be usefully implemented for high-throughput protein expression for functional and structural genomic research initiatives. The RNA-mediated chaperone type presented here would give new insights into de novo folding in vivo.

  18. Dynamical analysis of yeast protein interaction network during the sake brewing process.

    Science.gov (United States)

    Mirzarezaee, Mitra; Sadeghi, Mehdi; Araabi, Babak N

    2011-12-01

    Proteins interact with each other for performing essential functions of an organism. They change partners to get involved in various processes at different times or locations. Studying variations of protein interactions within a specific process would help better understand the dynamic features of the protein interactions and their functions. We studied the protein interaction network of Saccharomyces cerevisiae (yeast) during the brewing of Japanese sake. In this process, yeast cells are exposed to several stresses. Analysis of protein interaction networks of yeast during this process helps to understand how protein interactions of yeast change during the sake brewing process. We used gene expression profiles of yeast cells for this purpose. Results of our experiments revealed some characteristics and behaviors of yeast hubs and non-hubs and their dynamical changes during the brewing process. We found that just a small portion of the proteins (12.8 to 21.6%) is responsible for the functional changes of the proteins in the sake brewing process. The changes in the number of edges and hubs of the yeast protein interaction networks increase in the first stages of the process and it then decreases at the final stages.

  19. Evidence for the additions of clustered interacting nodes during the evolution of protein interaction networks from network motifs

    Directory of Open Access Journals (Sweden)

    Guo Hao

    2011-05-01

    Full Text Available Abstract Background High-throughput screens have revealed large-scale protein interaction networks defining most cellular functions. How the proteins were added to the protein interaction network during its growth is a basic and important issue. Network motifs represent the simplest building blocks of cellular machines and are of biological significance. Results Here we study the evolution of protein interaction networks from the perspective of network motifs. We find that in current protein interaction networks, proteins of the same age class tend to form motifs and such co-origins of motif constituents are affected by their topologies and biological functions. Further, we find that the proteins within motifs whose constituents are of the same age class tend to be densely interconnected, co-evolve and share the same biological functions, and these motifs tend to be within protein complexes. Conclusions Our findings provide novel evidence for the hypothesis of the additions of clustered interacting nodes and point out network motifs, especially the motifs with the dense topology and specific function may play important roles during this process. Our results suggest functional constraints may be the underlying driving force for such additions of clustered interacting nodes.

  20. A Global Protein Kinase and Phosphatase Interaction Network in Yeast

    Science.gov (United States)

    Breitkreutz, Ashton; Choi, Hyungwon; Sharom, Jeffrey R.; Boucher, Lorrie; Neduva, Victor; Larsen, Brett; Lin, Zhen-Yuan; Breitkreutz, Bobby-Joe; Stark, Chris; Liu, Guomin; Ahn, Jessica; Dewar-Darch, Danielle; Reguly, Teresa; Tang, Xiaojing; Almeida, Ricardo; Qin, Zhaohui Steve; Pawson, Tony; Gingras, Anne-Claude; Nesvizhskii, Alexey I.; Tyers, Mike

    2011-01-01

    The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses. PMID:20489023

  1. Macrocyclic peptide inhibitors for the protein-protein interaction of Zaire Ebola virus protein 24 and karyopherin alpha 5.

    Science.gov (United States)

    Song, Xiao; Lu, Lu-Yi; Passioura, Toby; Suga, Hiroaki

    2017-06-21

    Ebola virus infection leads to severe hemorrhagic fever in human and non-human primates with an average case fatality rate of 50%. To date, numerous potential therapies are in development, but FDA-approved drugs or vaccines are yet unavailable. Ebola viral protein 24 (VP24) is a multifunctional protein that plays critical roles in the pathogenesis of Ebola virus infection, e.g. innate immune suppression by blocking the interaction between KPNA and PY-STAT1. Here we report macrocyclic peptide inhibitors of the VP24-KPNA5 protein-protein interaction (PPI) by means of the RaPID (Random non-standard Peptides Integrated Discovery) system. These macrocyclic peptides showed remarkably high affinity to recombinant Zaire Ebola virus VP24 (eVP24), with a dissociation constant in the single digit nanomolar range, and could also successfully disrupt the eVP24-KPNA interaction. This work provides for the first time a chemical probe capable of modulating this PPI interaction and is the starting point for the development of unique anti-viral drugs against the Ebola virus.

  2. Hepatitis C Virus Protein Interaction Network Analysis Based on Hepatocellular Carcinoma.

    Directory of Open Access Journals (Sweden)

    Yuewen Han

    Full Text Available Epidemiological studies have validated the association between hepatitis C virus (HCV infection and hepatocellular carcinoma (HCC. An increasing number of studies show that protein-protein interactions (PPIs between HCV proteins and host proteins play a vital role in infection and mediate HCC progression. In this work, we collected all published interaction between HCV and human proteins, which include 455 unique human proteins participating in 524 HCV-human interactions. Then, we construct the HCV-human and HCV-HCC protein interaction networks, which display the biological knowledge regarding the mechanism of HCV pathogenesis, particularly with respect to pathogenesis of HCC. Through in-depth analysis of the HCV-HCC interaction network, we found that interactors are enriched in the JAK/STAT, p53, MAPK, TNF, Wnt, and cell cycle pathways. Using a random walk with restart algorithm, we predicted the importance of each protein in the HCV-HCC network and found that AKT1 may play a key role in the HCC progression. Moreover, we found that NS5A promotes HCC cells proliferation and metastasis by activating AKT/GSK3β/β-catenin pathway. This work provides a basis for a detailed map tracking new cellular interactions of HCV and identifying potential targets for HCV-related hepatocellular carcinoma treatment.

  3. Interaction between a plasma membrane-localized ankyrin-repeat protein ITN1 and a nuclear protein RTV1

    Energy Technology Data Exchange (ETDEWEB)

    Sakamoto, Hikaru [Department of Bioproduction, Faculty of Bioindustry, Tokyo University of Agriculture, 196 Yasaka, Abashiri-shi, Hokkaido 093-2422 (Japan); Sakata, Keiko; Kusumi, Kensuke [Department of Biology, Faculty of Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581 (Japan); Kojima, Mikiko; Sakakibara, Hitoshi [RIKEN Plant Science Center, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 (Japan); Iba, Koh, E-mail: koibascb@kyushu-u.org [Department of Biology, Faculty of Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581 (Japan)

    2012-06-29

    Highlights: Black-Right-Pointing-Pointer ITN1, a plasma membrane ankyrin protein, interacts with a nuclear DNA-binding protein RTV1. Black-Right-Pointing-Pointer The nuclear transport of RTV1 is partially inhibited by interaction with ITN1. Black-Right-Pointing-Pointer RTV1 can promote the nuclear localization of ITN1. Black-Right-Pointing-Pointer Both overexpression of RTV1 and the lack of ITN1 increase salicylic acids sensitivity in plants. -- Abstract: The increased tolerance to NaCl 1 (ITN1) protein is a plasma membrane (PM)-localized protein involved in responses to NaCl stress in Arabidopsis. The predicted structure of ITN1 is composed of multiple transmembrane regions and an ankyrin-repeat domain that is known to mediate protein-protein interactions. To elucidate the molecular functions of ITN1, we searched for interacting partners using a yeast two-hybrid assay, and a nuclear-localized DNA-binding protein, RTV1, was identified as a candidate. Bimolecular fluorescence complementation analysis revealed that RTV1 interacted with ITN1 at the PM and nuclei in vivo. RTV1 tagged with red fluorescent protein localized to nuclei and ITN1 tagged with green fluorescent protein localized to PM; however, both proteins localized to both nuclei and the PM when co-expressed. These findings suggest that RTV1 and ITN1 regulate the subcellular localization of each other.

  4. Unravelling Protein-Protein Interaction Networks Linked to Aliphatic and Indole Glucosinolate Biosynthetic Pathways in Arabidopsis

    Directory of Open Access Journals (Sweden)

    Sebastian J. Nintemann

    2017-11-01

    Full Text Available Within the cell, biosynthetic pathways are embedded in protein-protein interaction networks. In Arabidopsis, the biosynthetic pathways of aliphatic and indole glucosinolate defense compounds are well-characterized. However, little is known about the spatial orchestration of these enzymes and their interplay with the cellular environment. To address these aspects, we applied two complementary, untargeted approaches—split-ubiquitin yeast 2-hybrid and co-immunoprecipitation screens—to identify proteins interacting with CYP83A1 and CYP83B1, two homologous enzymes specific for aliphatic and indole glucosinolate biosynthesis, respectively. Our analyses reveal distinct functional networks with substantial interconnection among the identified interactors for both pathway-specific markers, and add to our knowledge about how biochemical pathways are connected to cellular processes. Specifically, a group of protein interactors involved in cell death and the hypersensitive response provides a potential link between the glucosinolate defense compounds and defense against biotrophic pathogens, mediated by protein-protein interactions.

  5. An α-Helix-Mimicking 12,13-Helix: Designed α/β/γ-Foldamers as Selective Inhibitors of Protein-Protein Interactions.

    Science.gov (United States)

    Grison, Claire M; Miles, Jennifer A; Robin, Sylvie; Wilson, Andrew J; Aitken, David J

    2016-09-05

    A major current challenge in bioorganic chemistry is the identification of effective mimics of protein secondary structures that act as inhibitors of protein-protein interactions (PPIs). In this work, trans-2-aminocyclobutanecarboxylic acid (tACBC) was used as the key β-amino acid component in the design of α/β/γ-peptides to structurally mimic a native α-helix. Suitably functionalized α/β/γ-peptides assume an α-helix-mimicking 12,13-helix conformation in solution, exhibit enhanced proteolytic stability in comparison to the wild-type α-peptide parent sequence from which they are derived, and act as selective inhibitors of the p53/hDM2 interaction. © 2016 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

  6. The geometric framework for nutrition reveals interactions between protein and carbohydrate during larval growth in honey bees

    Directory of Open Access Journals (Sweden)

    Bryan R. Helm

    2017-06-01

    Full Text Available In holometabolous insects, larval nutrition affects adult body size, a life history trait with a profound influence on performance and fitness. Individual nutritional components of larval diets are often complex and may interact with one another, necessitating the use of a geometric framework for elucidating nutritional effects. In the honey bee, Apis mellifera, nurse bees provision food to developing larvae, directly moderating growth rates and caste development. However, the eusocial nature of honey bees makes nutritional studies challenging, because diet components cannot be systematically manipulated in the hive. Using in vitro rearing, we investigated the roles and interactions between carbohydrate and protein content on larval survival, growth, and development in A. mellifera. We applied a geometric framework to determine how these two nutritional components interact across nine artificial diets. Honey bees successfully completed larval development under a wide range of protein and carbohydrate contents, with the medium protein (∼5% diet having the highest survival. Protein and carbohydrate both had significant and non-linear effects on growth rate, with the highest growth rates observed on a medium-protein, low-carbohydrate diet. Diet composition did not have a statistically significant effect on development time. These results confirm previous findings that protein and carbohydrate content affect the growth of A. mellifera larvae. However, this study identified an interaction between carbohydrate and protein content that indicates a low-protein, high-carb diet has a negative effect on larval growth and survival. These results imply that worker recruitment in the hive would decline under low protein conditions, even when nectar abundance or honey stores are sufficient.

  7. Prediction of Cancer Proteins by Integrating Protein Interaction, Domain Frequency, and Domain Interaction Data Using Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Chien-Hung Huang

    2015-01-01

    Full Text Available Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of a combination of different aspects of proteomics data types. In this study, we extended Aragues’s method by employing the protein-protein interaction (PPI data, domain-domain interaction (DDI data, weighted domain frequency score (DFS, and cancer linker degree (CLD data to predict cancer proteins. Performances were benchmarked based on three kinds of experiments as follows: (I using individual algorithm, (II combining algorithms, and (III combining the same classification types of algorithms. When compared with Aragues’s method, our proposed methods, that is, machine learning algorithm and voting with the majority, are significantly superior in all seven performance measures. We demonstrated the accuracy of the proposed method on two independent datasets. The best algorithm can achieve a hit ratio of 89.4% and 72.8% for lung cancer dataset and lung cancer microarray study, respectively. It is anticipated that the current research could help understand disease mechanisms and diagnosis.

  8. Statistical Estimation of the Protein-Ligand Binding Free Energy Based On Direct Protein-Ligand Interaction Obtained by Molecular Dynamics Simulation

    Directory of Open Access Journals (Sweden)

    Haruki Nakamura

    2012-09-01

    Full Text Available We have developed a method for estimating protein-ligand binding free energy (DG based on the direct protein-ligand interaction obtained by a molecular dynamics simulation. Using this method, we estimated the DG value statistically by the average values of the van der Waals and electrostatic interactions between each amino acid of the target protein and the ligand molecule. In addition, we introduced fluctuations in the accessible surface area (ASA and dihedral angles of the protein-ligand complex system as the entropy terms of the DG estimation. The present method included the fluctuation term of structural change of the protein and the effective dielectric constant. We applied this method to 34 protein-ligand complex structures. As a result, the correlation coefficient between the experimental and calculated DG values was 0.81, and the average error of DG was 1.2 kcal/mol with the use of the fixed parameters. These results were obtained from a 2 nsec molecular dynamics simulation.

  9. Selection of organisms for the co-evolution-based study of protein interactions.

    Science.gov (United States)

    Herman, Dorota; Ochoa, David; Juan, David; Lopez, Daniel; Valencia, Alfonso; Pazos, Florencio

    2011-09-12

    The prediction and study of protein interactions and functional relationships based on similarity of phylogenetic trees, exemplified by the mirrortree and related methodologies, is being widely used. Although dependence between the performance of these methods and the set of organisms used to build the trees was suspected, so far nobody assessed it in an exhaustive way, and, in general, previous works used as many organisms as possible. In this work we asses the effect of using different sets of organism (chosen according with various phylogenetic criteria) on the performance of this methodology in detecting protein interactions of different nature. We show that the performance of three mirrortree-related methodologies depends on the set of organisms used for building the trees, and it is not always directly related to the number of organisms in a simple way. Certain subsets of organisms seem to be more suitable for the predictions of certain types of interactions. This relationship between type of interaction and optimal set of organism for detecting them makes sense in the light of the phylogenetic distribution of the organisms and the nature of the interactions. In order to obtain an optimal performance when predicting protein interactions, it is recommended to use different sets of organisms depending on the available computational resources and data, as well as the type of interactions of interest.

  10. The alpha-fetoprotein (AFP) third domain: a search for AFP interaction sites of cell cycle proteins.

    Science.gov (United States)

    Mizejewski, G J

    2016-09-01

    The carboxy-terminal third domain of alpha-fetoprotein (AFP-3D) is known to harbor binding and/or interaction sites for hydrophobic ligands, receptors, and binding proteins. Such reports have established that AFP-3D consists of amino acid (AA) sequence stretches on the AFP polypeptide that engages in protein-to-protein interactions with various ligands and receptors. Using a computer software program specifically designed for such interactions, the present report identified AA sequence fragments on AFP-3D that could potentially interact with a variety of cell cycle proteins. The cell cycle proteins identified were (1) cyclins, (2) cyclin-dependent kinases, (3) cell cycle-associated proteins (inhibitors, checkpoints, initiators), and (4) ubiquitin ligases. Following detection of the AFP-3D to cell cycle protein interaction sites, the computer-derived AFP localization AA sequences were compared and aligned with previously reported hydrophobic ligand and receptor interaction sites on AFP-3D. A literature survey of the association of cell cycle proteins with AFP showed both positive relationships and correlations. Previous reports of experimental AFP-derived peptides effects on various cell cycle proteins served to confirm and verify the present computer cell cycle protein identifications. Cell cycle protein interactions with AFP-CD peptides have been reported in cultured MCF-7 breast cancer cells subjected to mRNA microarray analysis. After 7 days in culture with MCF-7 cells, the AFP-derived peptides were shown to downregulate cyclin E, SKP2, checkpoint suppressors, cyclin-dependent kinases, and ubiquitin ligases that modulate cyclin E/CdK2 transition from the G1 to the S-phase of the cell cycle. Thus, the experimental data on AFP-CD interaction with cell cycle proteins were consistent with the "in silico" findings.

  11. The human interactome knowledge base (hint-kb): An integrative human protein interaction database enriched with predicted protein–protein interaction scores using a novel hybrid technique

    KAUST Repository

    Theofilatos, Konstantinos A.

    2013-07-12

    Proteins are the functional components of many cellular processes and the identification of their physical protein–protein interactions (PPIs) is an area of mature academic research. Various databases have been developed containing information about experimentally and computationally detected human PPIs as well as their corresponding annotation data. However, these databases contain many false positive interactions, are partial and only a few of them incorporate data from various sources. To overcome these limitations, we have developed HINT-KB (http://biotools.ceid.upatras.gr/hint-kb/), a knowledge base that integrates data from various sources, provides a user-friendly interface for their retrieval, cal-culatesasetoffeaturesofinterest and computesaconfidence score for every candidate protein interaction. This confidence score is essential for filtering the false positive interactions which are present in existing databases, predicting new protein interactions and measuring the frequency of each true protein interaction. For this reason, a novel machine learning hybrid methodology, called (Evolutionary Kalman Mathematical Modelling—EvoKalMaModel), was used to achieve an accurate and interpretable scoring methodology. The experimental results indicated that the proposed scoring scheme outperforms existing computational methods for the prediction of PPIs.

  12. Coarse-grained versus atomistic simulations : realistic interaction free energies for real proteins

    NARCIS (Netherlands)

    May, Ali; Pool, René; van Dijk, Erik; Bijlard, Jochem; Abeln, Sanne; Heringa, Jaap; Feenstra, K Anton

    2014-01-01

    MOTIVATION: To assess whether two proteins will interact under physiological conditions, information on the interaction free energy is needed. Statistical learning techniques and docking methods for predicting protein-protein interactions cannot quantitatively estimate binding free energies. Full

  13. Coarse-grained versus atomistic simulations: realistic interaction free energies for real proteins

    NARCIS (Netherlands)

    May, A.; Pool, R.; van Dijk, E.; Bijlard, J.; Abeln, S.; Heringa, J.; Feenstra, K.A.

    2014-01-01

    MOTIVATION: To assess whether two proteins will interact under physiological conditions, information on the interaction free energy is needed. Statistical learning techniques and docking methods for predicting protein-protein interactions cannot quantitatively estimate binding free energies. Full

  14. Analytical techniques for the study of polyphenol-protein interactions.

    Science.gov (United States)

    Poklar Ulrih, Nataša

    2017-07-03

    This mini review focuses on advances in biophysical techniques to study polyphenol interactions with proteins. Polyphenols have many beneficial pharmacological properties, as a result of which they have been the subject of intensive studies. The most conventional techniques described here can be divided into three groups: (i) methods used for screening (in-situ methods); (ii) methods used to gain insight into the mechanisms of polyphenol-protein interactions; and (iii) methods used to study protein aggregation and precipitation. All of these methods used to study polyphenol-protein interactions are based on modifications to the physicochemical properties of the polyphenols or proteins after binding/complex formation in solution. To date, numerous review articles have been published in the field of polyphenols. This review will give a brief insight in computational methods and biosensors and cell-based methods, spectroscopic methods including fluorescence emission, UV-vis adsorption, circular dichroism, Fourier transform infrared and mass spectrometry, nuclear magnetic resonance, X-ray diffraction, and light scattering techniques including small-angle X-ray scattering and small-angle neutron scattering, and calorimetric techniques (isothermal titration calorimetry and differential scanning calorimetry), microscopy, the techniques which have been successfully used for polyphenol-protein interactions. At the end the new methods based on single molecule detection with high potential to study polyphenol-protein interactions will be presented. The advantages and disadvantages of each technique will be discussed as well as the thermodynamic, kinetic or structural parameters, which can be obtained. The other relevant biophysical experimental techniques that have proven to be valuable, such electrochemical methods, hydrodynamic techniques and chromatographic techniques will not be described here.

  15. Molecular interactions and redox effects of carvacrol and thymol on myofibrillar proteins using a non-destructive and solvent-free methodological approach.

    Science.gov (United States)

    Lahmar, Aida; Akcan, Tolga; Chekir-Ghedira, Leila; Estévez, Mario

    2018-04-01

    The present study provides molecular insight into the effect of thymol and carvacrol on the oxidative damage caused to myofibrillar proteins by a hydroxyl-radical generating system (HRGS). An innovative model system was designed, in which gels, prepared with increasing levels of myofibrillar proteins, were oxidized by a HRGS (Fe 3+ /H 2 O 2 , 60 °C and 7 days) in the presence of lipids. The molecular affinity between myofibrillar proteins and both terpenes, as well as their effect on the oxidative stability of the gel systems, were studied using a non-destructive and solvent-free procedure based on fluorescence spectroscopy. Carvacrol displayed more affinity than thymol for establishing chemical interactions with protein residues. Both terpenes exhibited a significant antioxidant potential against the generation of lipid-derived volatile carbonyls and against the formation of protein crosslinking. This procedure may be applied to meat products to assess the effectiveness of a given antioxidant additive without size reduction or sample processing. Copyright © 2018 Elsevier Ltd. All rights reserved.

  16. Determination and Quantification of Molecular Interactions in Protein Films: A Review

    Directory of Open Access Journals (Sweden)

    Felicia Hammann

    2014-12-01

    Full Text Available Protein based films are nowadays also prepared with the aim of replacing expensive, crude oil-based polymers as environmentally friendly and renewable alternatives. The protein structure determines the ability of protein chains to form intra- and intermolecular bonds, whereas the degree of cross-linking depends on the amino acid composition and molecular weight of the protein, besides the conditions used in film preparation and processing. The functionality varies significantly depending on the type of protein and affects the resulting film quality and properties. This paper reviews the methods used in examination of molecular interactions in protein films and discusses how these intermolecular interactions can be quantified. The qualitative determination methods can be distinguished by structural analysis of solutions (electrophoretic analysis, size exclusion chromatography and analysis of solid films (spectroscopy techniques, X-ray scattering methods. To quantify molecular interactions involved, two methods were found to be the most suitable: protein film swelling and solubility. The importance of non-covalent and covalent interactions in protein films can be investigated using different solvents. The research was focused on whey protein, whereas soy protein and wheat gluten were included as further examples of proteins.

  17. Determination and Quantification of Molecular Interactions in Protein Films: A Review.

    Science.gov (United States)

    Hammann, Felicia; Schmid, Markus

    2014-12-10

    Protein based films are nowadays also prepared with the aim of replacing expensive, crude oil-based polymers as environmentally friendly and renewable alternatives. The protein structure determines the ability of protein chains to form intra- and intermolecular bonds, whereas the degree of cross-linking depends on the amino acid composition and molecular weight of the protein, besides the conditions used in film preparation and processing. The functionality varies significantly depending on the type of protein and affects the resulting film quality and properties. This paper reviews the methods used in examination of molecular interactions in protein films and discusses how these intermolecular interactions can be quantified. The qualitative determination methods can be distinguished by structural analysis of solutions (electrophoretic analysis, size exclusion chromatography) and analysis of solid films (spectroscopy techniques, X-ray scattering methods). To quantify molecular interactions involved, two methods were found to be the most suitable: protein film swelling and solubility. The importance of non-covalent and covalent interactions in protein films can be investigated using different solvents. The research was focused on whey protein, whereas soy protein and wheat gluten were included as further examples of proteins.

  18. Atom depth analysis delineates mechanisms of protein intermolecular interactions

    International Nuclear Information System (INIS)

    Alocci, Davide; Bernini, Andrea; Niccolai, Neri

    2013-01-01

    Highlights: •3D atom depth analysis is proposed to identify different layers in protein structures. •Amino acid contents for each layers have been analyzed for a large protein dataset. •Charged amino acids in the most external layer are present at very different extents. •Atom depth indexes of K residues reflect their side chains flexibility. •Mobile surface charges can be responsible for long range protein–protein recognition. -- Abstract: The systematic analysis of amino acid distribution, performed inside a large set of resolved protein structures, sheds light on possible mechanisms driving non random protein–protein approaches. Protein Data Bank entries have been selected using as filters a series of restrictions ensuring that the shape of protein surface is not modified by interactions with large or small ligands. 3D atom depth has been evaluated for all the atoms of the 2,410 selected structures. The amino acid relative population in each of the structural layers formed by grouping atoms on the basis of their calculated depths, has been evaluated. We have identified seven structural layers, the inner ones reproducing the core of proteins and the outer one incorporating their most protruding moieties. Quantitative analysis of amino acid contents of structural layers identified, as expected, different behaviors. Atoms of Q, R, K, N, D residues are increasingly more abundant in going from core to surfaces. An opposite trend is observed for V, I, L, A, C, and G. An intermediate behavior is exhibited by P, S, T, M, W, H, F and Y. The outer structural layer hosts predominantly E and K residues whose charged moieties, protruding from outer regions of the protein surface, reorient free from steric hindrances, determining specific electrodynamics maps. This feature may represent a protein signature for long distance effects, driving the formation of encounter complexes and the eventual short distance approaches that are required for protein–protein

  19. 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.

  20. Research Techniques Made Simple: Emerging Methods to Elucidate Protein Interactions through Spatial Proximity.

    Science.gov (United States)

    Che, Yonglu; Khavari, Paul A

    2017-12-01

    Interactions between proteins are essential for fundamental cellular processes, and the diversity of such interactions enables the vast variety of functions essential for life. A persistent goal in biological research is to develop assays that can faithfully capture different types of protein interactions to allow their study. A major step forward in this direction came with a family of methods that delineates spatial proximity of proteins as an indirect measure of protein-protein interaction. A variety of enzyme- and DNA ligation-based methods measure protein co-localization in space, capturing novel interactions that were previously too transient or low affinity to be identified. Here we review some of the methods that have been successfully used to measure spatially proximal protein-protein interactions. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  1. Using Förster-Resonance Energy Transfer to Measure Protein Interactions Between Bcl-2 Family Proteins on Mitochondrial Membranes.

    Science.gov (United States)

    Pogmore, Justin P; Pemberton, James M; Chi, Xiaoke; Andrews, David W

    2016-01-01

    The Bcl-2 family of proteins regulates the process of mitochondrial outer membrane permeabilization, causing the release of cytochrome c and committing a cell to apoptosis. The majority of the functional interactions between these proteins occur at, on, or within the mitochondrial outer membrane, complicating structural studies of the proteins and complexes. As a result most in vitro studies of these protein-protein interactions use truncated proteins and/or detergents which can cause artificial interactions. Herein, we describe a detergent-free, fluorescence-based, in vitro technique to study binding between full-length recombinant Bcl-2 family proteins, particularly cleaved BID (cBID) and BCL-XL, on the membranes of purified mitochondria.

  2. High pressure as a tool for investigating protein-ligand interactions

    International Nuclear Information System (INIS)

    Marchal, S; Lange, R; Tortora, P; Balny, C

    2004-01-01

    In recent years, the application of pressure on biological systems has gained increasing interest. Pressure-induced destabilization of electrostatic and hydrophobic interactions is currently exploited to study conformational protein stability and macromolecular assemblies of proteins. Due to links between severe human pathologies and ordered protein oligomerization into aggregates, which have become apparent, a better knowledge of the molecular and structural determinants that ensure the packing efficiency and stability of such complexes has taken on special importance. Here, we report the effect of pressure on the property of human ataxin-3 of aggregation. The results indicate the importance of its polyglutamine chain length in the stability and the tendency of the protein to form spheroids. Partial unfolding of the protein leading to solvent exposure of hydrophobic domains appears to be a prerequisite in the aggregation process of ataxin-3

  3. Identification of a novel receptor-like protein kinase that interacts with a geminivirus nuclear shuttle protein

    International Nuclear Information System (INIS)

    Mariano, Andrea C.; Andrade, Maxuel O.; Santos, Anesia A.; Carolino, Sonia M.B.; Oliveira, Marli L.; Baracat-Pereira, Maria Cristina; Brommonshenkel, Sergio H.; Fontes, Elizabeth P.B.

    2004-01-01

    Despite extensive studies in plant virus-host interactions, the molecular mechanisms of geminivirus movement and interactions with host components remain largely unknown. A tomato kinase protein and its soybean homolog were found to interact specifically with the nuclear shuttle protein (NSP) of Tomato golden mosaic virus (TGMV) and Tomato crinkle leaf yellows virus (TCrLYV) through yeast two-hybrid screening and in vitro protein binding assays. These proteins, designated LeNIK (Lycopersicon esculentum NSP-Interacting Kinase) and GmNIK (Glycine max NIK), belong to the LRR-RLK (leucine rich-repeat receptor-like kinase) family that is involved in plant developmental processes and/or resistance response. As such, NIK is structurally organized into characteristic domains, including a serine/threonine kinase domain with a nucleotide binding site at the C-terminal region, an internal transmembrane segment and leucine-rich repeats (LRR) at the N-terminal portion. The potential significance of the NSP-NIK interaction is discussed

  4. Density functional study of molecular interactions in secondary structures of proteins.

    Science.gov (United States)

    Takano, Yu; Kusaka, Ayumi; Nakamura, Haruki

    2016-01-01

    Proteins play diverse and vital roles in biology, which are dominated by their three-dimensional structures. The three-dimensional structure of a protein determines its functions and chemical properties. Protein secondary structures, including α-helices and β-sheets, are key components of the protein architecture. Molecular interactions, in particular hydrogen bonds, play significant roles in the formation of protein secondary structures. Precise and quantitative estimations of these interactions are required to understand the principles underlying the formation of three-dimensional protein structures. In the present study, we have investigated the molecular interactions in α-helices and β-sheets, using ab initio wave function-based methods, the Hartree-Fock method (HF) and the second-order Møller-Plesset perturbation theory (MP2), density functional theory, and molecular mechanics. The characteristic interactions essential for forming the secondary structures are discussed quantitatively.

  5. Incorporating water-release and lateral protein interactions in modeling equilibrium adsorption for ion-exchange chromatography.

    Science.gov (United States)

    Thrash, Marvin E; Pinto, Neville G

    2006-09-08

    The equilibrium adsorption of two albumin proteins on a commercial ion exchanger has been studied using a colloidal model. The model accounts for electrostatic and van der Waals forces between proteins and the ion exchanger surface, the energy of interaction between adsorbed proteins, and the contribution of entropy from water-release accompanying protein adsorption. Protein-surface interactions were calculated using methods previously reported in the literature. Lateral interactions between adsorbed proteins wer