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Sample records for atrophin interacting protein

  1. Atrophin-1, the DRPLA gene product, interacts with two families of WW domain-containing proteins.

    Science.gov (United States)

    Wood, J D; Yuan, J; Margolis, R L; Colomer, V; Duan, K; Kushi, J; Kaminsky, Z; Kleiderlein, J J; Sharp, A H; Ross, C A

    1998-06-01

    Atrophin-1 contains a polyglutamine repeat, expansion of which is responsible for dentatorubral and pallidoluysian atrophy (DRPLA). The normal function of atrophin-1 is unknown. We have identified five atrophin-1 interacting proteins (AIPs) which bind to atrophin-1 in the vicinity of the polyglutamine tract using the yeast two-hybrid system. Four of the interactions were confirmed using in vitro binding assays. All five interactors contained multiple WW domains. Two are novel. The AIPs can be divided into two distinct classes. AIP1 and AIP3/WWP3 are MAGUK-like multidomain proteins containing a number of protein-protein interaction modules, namely a guanylate kinase-like region, two WW domains, and multiple PDZ domains. AIP2/WWP2, AIP4, and AIP5/WWP1 are highly homologous, each having four WW domains and a HECT domain characteristic of ubiquitin ligases. These interactors are similar to recently isolated huntingtin-interacting proteins, suggesting possible commonality of function between two proteins responsible for very similar diseases.

  2. Atrophin Protein RERE Positively Regulates Notch Targets in the Developing Vertebrate Spinal Cord.

    Science.gov (United States)

    Wang, Hui; Gui, Hongxing; Rallo, Michael S; Xu, Zhiyan; Matise, Michael P

    2017-01-31

    The Notch signaling pathway controls cell fate decision, proliferation and other biological functions in both vertebrates and invertebrates. Precise regulation of the canonical Notch pathway ensures robustness of the signal throughout development and adult tissue homeostasis. Aberrant Notch signaling results in profound developmental defects and is linked to many human diseases. In this study, we identified the Atrophin family protein RERE (also called Atro2) as a positive regulator of Notch target Hes genes in the developing vertebrate spinal cord. Prior studies have shown that during early embryogenesis in mouse and zebrafish, deficit of RERE causes various patterning defects in multiple organs including the neural tube. Here, we detected the expression of RERE in the developing chick spinal cord, and found that normal RERE activity is needed for proper neural progenitor proliferation and neuronal differentiation possibly by affecting Notch mediated Hes expression. In mammalian cells, RERE co-immunoprecipitates with CBF1 and Notch intracellular domain (NICD), and is recruited to nuclear foci formed by overexpressed NICD1. RERE is also necessary for NICD to activate the expression of Notch target genes. Our findings suggest that RERE stimulates Notch target gene expression by preventing degradation of NICD protein, thereby facilitating the assembly of a transcriptional activating complex containing NICD, CSL and other coactivators. This article is protected by copyright. All rights reserved.

  3. Atrophin-1全长基因稳定转染和瞬时转染真核细胞系的表达分析%Analysis of the expression of normal and mutant full-length atrophin-1 gene in stable and transient transfected eukaryotic cell line

    Institute of Scientific and Technical Information of China (English)

    张鑫; 顾卫红; 王国相

    2011-01-01

    Objective To establish eukaryotic expression system of full -length Sene in normal [CAG repeats (Q19)] and abnormal [CAG repeats (Q82 and Q66)] atrophin-1 [the pathogenic gene of dentatorubral-pallidoluysian atrophy (DRPLA)]. To compare the expression system of stable and transient transfected cell line by observing the cellular appearance and atrophin-1 fusion protein expression and its distribution for further research on the establishment of a fine cell model. Methods Based on the 2 established stable transfected green-fluoresent protein (GFP)-atrophin- 1-Q19/Flp- In TREx293 and GFP-atrophin - 1 -Q82/Flp -In TREx293 cell lines, Tel-on system was used to induce the expression, which contained normal and ahnormal atrophin - 1 full - length gene and GFP fusion proteins, respectively.Meanwhile, GFP - atrophin - 1 - Q19 and GFP - atrophin - 1 - Q66 recombinant plasmids were transiently transfected into 293T cells and SH-SY5Y cells respectively with LipofectamineTM 2000. The expression and localization of normal and abnormal atrophin - 1 fusion proteins in the cells were detected by light microscope with HE staining and fluorescence microscope; the ultrastructure of the cells were observed by electron microscope; the expression of GFP - atrophin - 1 fusion proteins were detected by Western blotting. Results In stable transfected system, the green fluorescence signals were detected in the nucleus, and abnormal proteins were not uniformly distributed while some were aggregated. Electron microscope showed shrinkaged nuclear membrane and abnormal intranuclear structure in abnormal cells. In transjpnt transfection of 293T, Q66 protein aggregated in the nucleus which was detected as acidophilic bodies by HE staining-light microscope and atrophin-1 fusion protein as punctuate pattern by fluorescence microscope. Under the observation of electron microscope, great quanlity of electron-dense substance accumulation, distinct shrinkage nuclear membrane and nucleus disappearance

  4. Protein-protein interactions

    DEFF Research Database (Denmark)

    Byron, Olwyn; Vestergaard, Bente

    2015-01-01

    Responsive formation of protein:protein interaction (PPI) upon diverse stimuli is a fundament of cellular function. As a consequence, PPIs are complex, adaptive entities, and exist in structurally heterogeneous interplays defined by the energetic states of the free and complexed protomers......, are reported. The aim is to depict how the elucidation of the interplay of structures requires the interplay of methods....

  5. Trinucleotide repeat expansion and DRPLA (Smith`s disease): Molecular characterization of atrophin-1

    Energy Technology Data Exchange (ETDEWEB)

    Margolis, R.L.; Li, S.H.; Li, X.J.; Ross, C.A. [Johns Hopkins Univ., Balitmore, MD (United States)

    1994-09-01

    Smith`s disease (also known as dentatorubral pallidoluysian atrophy or DRPLA) is a rare, progressive, fatal neuropsychiatric disorder similar to Huntington`s disease (HD). Smith`s disease is characterized by ataxia, choreoathetosis, myoclonic epilepsy, dementia, and genetic anticipation. Neuropathological findings include prominent cell loss in the dentate nucleus of the cerebellum, the globus pallidus, the red nucleus, and the subthalamic nucleus. An expansion of a CAG trinucleotide repeat encoding polyglutamine in a gene originally identified in our laboratory as part of a program to clone candidate genes for disorders with anticipation has recently been found to cause this disorder. We have identified two families that demonstrate the pathological and genetic features (expanded CAG repeat and anticipation) of this disease. Northern analysis indicates that the gene, which we have termed atrophin-1, is widely expressed as a 5 kb mRNA in normal human brain and peripheral tissues. Brain expression is highest in the cerebellum. The developmental expression of the rat homologues of IT-15 (the gene in which a CAG expansion causes HD) and atrophin-1 were compared. Atrophin-1 was most highly expressed in early rat embryo brain (E16), whereas the greatest expression of IT-15 was in the adult rat brain. Cloning and sequencing of the open reading frame from inserts contained in brain cDNA libraries is in progress. In addition to the CAG repeat, the ORF contains an unusual region of alternating acidic and basic amino acids. Further characterization of atrophin-1, and comparison of it to other genes in which trinucleotide repeat expansion leads to neuropsychiatric disorders, should lead to a better understanding of the pathophysiology by which CAG repeat expansion causes human disease.

  6. PIC: Protein Interactions Calculator.

    Science.gov (United States)

    Tina, K G; Bhadra, R; Srinivasan, N

    2007-07-01

    Interactions within a protein structure and interactions between proteins in an assembly are essential considerations in understanding molecular basis of stability and functions of proteins and their complexes. There are several weak and strong interactions that render stability to a protein structure or an assembly. Protein Interactions Calculator (PIC) is a server which, given the coordinate set of 3D structure of a protein or an assembly, computes various interactions such as disulphide bonds, interactions between hydrophobic residues, ionic interactions, hydrogen bonds, aromatic-aromatic interactions, aromatic-sulphur interactions and cation-pi interactions within a protein or between proteins in a complex. Interactions are calculated on the basis of standard, published criteria. The identified interactions between residues can be visualized using a RasMol and Jmol interface. The advantage with PIC server is the easy availability of inter-residue interaction calculations in a single site. It also determines the accessible surface area and residue-depth, which is the distance of a residue from the surface of the protein. User can also recognize specific kind of interactions, such as apolar-apolar residue interactions or ionic interactions, that are formed between buried or exposed residues or near the surface or deep inside.

  7. Protein-Protein Interaction Databases

    DEFF Research Database (Denmark)

    Szklarczyk, Damian; Jensen, Lars Juhl

    2015-01-01

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

  8. 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.

  9. Cardiolipin Interactions with Proteins.

    Science.gov (United States)

    Planas-Iglesias, Joan; Dwarakanath, Himal; Mohammadyani, Dariush; Yanamala, Naveena; Kagan, Valerian E; Klein-Seetharaman, Judith

    2015-09-15

    Cardiolipins (CL) represent unique phospholipids of bacteria and eukaryotic mitochondria with four acyl chains and two phosphate groups that have been implicated in numerous functions from energy metabolism to apoptosis. Many proteins are known to interact with CL, and several cocrystal structures of protein-CL complexes exist. In this work, we describe the collection of the first systematic and, to the best of our knowledge, the comprehensive gold standard data set of all known CL-binding proteins. There are 62 proteins in this data set, 21 of which have nonredundant crystal structures with bound CL molecules available. Using binding patch analysis of amino acid frequencies, secondary structures and loop supersecondary structures considering phosphate and acyl chain binding regions together and separately, we gained a detailed understanding of the general structural and dynamic features involved in CL binding to proteins. Exhaustive docking of CL to all known structures of proteins experimentally shown to interact with CL demonstrated the validity of the docking approach, and provides a rich source of information for experimentalists who may wish to validate predictions.

  10. Scaffolds for blocking protein-protein interactions.

    Science.gov (United States)

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

    2007-01-01

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

  11. Controllability in protein interaction networks.

    Science.gov (United States)

    Wuchty, Stefan

    2014-05-13

    Recently, the focus of network research shifted to network controllability, prompting us to determine proteins that are important for the control of the underlying interaction webs. In particular, we determined minimum dominating sets of proteins (MDSets) in human and yeast protein interaction networks. Such groups of proteins were defined as optimized subsets where each non-MDSet protein can be reached by an interaction from an MDSet protein. Notably, we found that MDSet proteins were enriched with essential, cancer-related, and virus-targeted genes. Their central position allowed MDSet proteins to connect protein complexes and to have a higher impact on network resilience than hub proteins. As for their involvement in regulatory functions, MDSet proteins were enriched with transcription factors and protein kinases and were significantly involved in bottleneck interactions, regulatory links, phosphorylation events, and genetic interactions.

  12. Conductometric monitoring of protein-protein interactions.

    Science.gov (United States)

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

    2013-12-06

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

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

    Directory of Open Access Journals (Sweden)

    Can Tolga

    2008-06-01

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

  14. Protopia: a protein-protein interaction tool

    Science.gov (United States)

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

    2009-01-01

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

  15. 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.

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

    Directory of Open Access Journals (Sweden)

    Carmen Sánchez Claros

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

  17. 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...

  18. Protein mixtures: interactions and gelation

    OpenAIRE

    Ersch, C.

    2015-01-01

    Gelation is a ubiquitous process in the preparation of foods. As most foods are multi constituent mixtures, understanding gelation in mixtures is an important goal in food science. Here we presented a systematic investigation on the influence of molecular interactions on the gelation in protein mixtures. Gelatin gels with added globular protein and globular protein gels with added gelatin were analyzed for their gel microstructure and rheological properties. Mixed gels with altered microstruc...

  19. Yeast Interacting Proteins Database: YJL199C, YJL199C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available d in closely related Saccharomyces species; protein detected in large-scale protein-protein interaction studies...cies; protein detected in large-scale protein-protein interaction studies Rows with this prey as prey (4) Ro...n; not conserved in closely related Saccharomyces species; protein detected in large-scale protein-protein interaction studies... species; protein detected in large-scale protein-protein interaction studies Rows with this prey as prey Ro

  20. Automatic Extraction of Protein Interaction in Literature

    OpenAIRE

    Liu, Peilei; Wang, Ting

    2014-01-01

    Protein-protein interaction extraction is the key precondition of the construction of protein knowledge network, and it is very important for the research in the biomedicine. This paper extracted directional protein-protein interaction from the biological text, using the SVM-based method. Experiments were evaluated on the LLL05 corpus with good results. The results show that dependency features are import for the protein-protein interaction extraction and features related to the interaction w...

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

    Institute of Scientific and Technical Information of China (English)

    Mahmood A. Mahdavi; Yen-Han Lin

    2007-01-01

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

  2. Protein mixtures: interactions and gelation

    NARCIS (Netherlands)

    Ersch, C.

    2015-01-01

    Gelation is a ubiquitous process in the preparation of foods. As most foods are multi constituent mixtures, understanding gelation in mixtures is an important goal in food science. Here we presented a systematic investigation on the influence of molecular interactions on the gelation in protein mixt

  3. 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

  4. 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.

  5. Coevolution of gene expression among interacting proteins

    OpenAIRE

    2004-01-01

    Physically interacting proteins or parts of proteins are expected to evolve in a coordinated manner that preserves proper interactions. Such coevolution at the amino acid-sequence level is well documented and has been used to predict interacting proteins, domains, and amino acids. Interacting proteins are also often precisely coexpressed with one another, presumably to maintain proper stoichiometry among interacting components. Here, we show that the expression levels of physically inter...

  6. Yeast Interacting Proteins Database: YDL226C, YGL198W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available omputational analysis of large-scale protein-protein interaction data suggests a ... computational analysis of large-scale protein-protein interaction data suggests a possible role in vesicle-

  7. Yeast Interacting Proteins Database: YDR425W, YGL161C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available icles; computational analysis of large-scale protein-protein interaction data sug...olgi vesicles; computational analysis of large-scale protein-protein interaction data suggests a possible ro

  8. Yeast Interacting Proteins Database: YPL070W, YOR155C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available utational analysis of large-scale protein-protein interaction data suggests a possible role in transcription...9 domain; computational analysis of large-scale protein-protein interaction data suggests a possible role in

  9. Yeast Interacting Proteins Database: YPL070W, YLR245C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available utational analysis of large-scale protein-protein interaction data suggests a possible role in transcription...Vps9 domain; computational analysis of large-scale protein-protein interaction data suggests a possible role

  10. Yeast Interacting Proteins Database: YPL070W, YPR193C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available utational analysis of large-scale protein-protein interaction data suggests a possible role in transcription...in; computational analysis of large-scale protein-protein interaction data suggests a possible role in trans

  11. Yeast Interacting Proteins Database: YGL161C, YDR084C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available les; computational analysis of large-scale protein-protein interaction data suggests a possible role in vesi...GTPases, localized to late Golgi vesicles; computational analysis of large-scale protein-protein interaction

  12. Probing protein-sugar interactions.

    Science.gov (United States)

    Ebel, C; Eisenberg, H; Ghirlando, R

    2000-01-01

    We have investigated the partial specific volumes (2) (ml/g), hydration, and cosolvent interactions of rabbit muscle aldolase by equilibrium sedimentation in the analytical ultracentrifuge and by direct density increment (partial differential/partial differentialc(2))(mu) measurements over a range of sugar concentrations and temperature. In a series of sugars increasing in size, glucose, sucrose, raffinose, and alpha-cyclodextrin, (partial differential/ partial differentialc(2))(mu) decreases linearly with the solvent density rho(0). These sugar cosolvents do not interact with the protein; however, the interaction parameter B(1) (g water/g protein) mildly increases with increasing sugar size. The experimental B(1) values are smaller than values calculated by excluded volume (rolling ball) considerations. B(1) relates to hydration in this and in other instances studied. It decreases with increasing temperature, leading to an increase in (2) due to reduced water of hydration electrostriction. The density increments (partial differential/ partial differentialc(2))(mu), however, decrease in concave up form in the case of glycerol and in concave down form for trehalose, leading to more complex behavior in the case of carbohydrates playing a biological role as osmolytes and antifreeze agents. A critical discussion, based on the thermodynamics of multicomponent solutions, is presented.

  13. Inferring protein-protein interaction complexes from immunoprecipitation data

    NARCIS (Netherlands)

    Kutzera, J.; Hoefsloot, H.C.J.; Malovannaya, A.; Smit, A.B.; Van Mechelen, I.; Smilde, A.K.

    2013-01-01

    BACKGROUND: Protein inverted question markprotein interactions in cells are widely explored using small inverted question markscale experiments. However, the search for protein complexes and their interactions in data from high throughput experiments such as immunoprecipitation is still a challenge.

  14. New approach for predicting protein-protein interactions

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

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

  15. Yeast Interacting Proteins Database: YEL043W, YOR164C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available on quantitative analysis of protein-protein interaction maps; may interact with ribosomes, based on co-purification studies...ing based on quantitative analysis of protein-protein interaction maps; may interact with ribosomes, based on co-purification studies

  16. Coarse-grain modelling of protein-protein interactions

    NARCIS (Netherlands)

    Baaden, Marc; Marrink, Siewert J.

    2013-01-01

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

  17. Yeast Interacting Proteins Database: YPL095C, YGL198W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available d to late Golgi vesicles; computational analysis of large-scale protein-protein interaction data suggests a ...gene name YIP4 Prey description Protein that interacts with Rab GTPases, localized to late Golgi vesicles; computation

  18. Coevolution of gene expression among interacting proteins

    Energy Technology Data Exchange (ETDEWEB)

    Fraser, Hunter B.; Hirsh, Aaron E.; Wall, Dennis P.; Eisen,Michael B.

    2004-03-01

    Physically interacting proteins or parts of proteins are expected to evolve in a coordinated manner that preserves proper interactions. Such coevolution at the amino acid-sequence level is well documented and has been used to predict interacting proteins, domains, and amino acids. Interacting proteins are also often precisely coexpressed with one another, presumably to maintain proper stoichiometry among interacting components. Here, we show that the expression levels of physically interacting proteins coevolve. We estimate average expression levels of genes from four closely related fungi of the genus Saccharomyces using the codon adaptation index and show that expression levels of interacting proteins exhibit coordinated changes in these different species. We find that this coevolution of expression is a more powerful predictor of physical interaction than is coevolution of amino acid sequence. These results demonstrate previously uncharacterized coevolution of gene expression, adding a different dimension to the study of the coevolution of interacting proteins and underscoring the importance of maintaining coexpression of interacting proteins over evolutionary time. Our results also suggest that expression coevolution can be used for computational prediction of protein protein interactions.

  19. 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.

  20. 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.

  1. 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

    2015-01-01

    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.

  2. Yeast Interacting Proteins Database: YNL189W, YJL199C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available tein; not conserved in closely related Saccharomyces species; protein detected in large-scale protein-protein interaction studies...myces species; protein detected in large-scale protein-protein interaction studies Rows with this prey as pr

  3. Inferring interaction partners from protein sequences

    CERN Document Server

    Bitbol, Anne-Florence; Colwell, Lucy J; Wingreen, Ned S

    2016-01-01

    Specific protein-protein interactions are crucial in the cell, both to ensure the formation and stability of multi-protein complexes, and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners. Hence, the sequences of interacting partners are correlated. Here we exploit these correlations to accurately identify which proteins are specific interaction partners from sequence data alone. Our general approach, which employs a pairwise maximum entropy model to infer direct couplings between residues, has been successfully used to predict the three-dimensional structures of proteins from sequences. Building on this approach, we introduce an iterative algorithm to predict specific interaction partners from among the members of two protein families. We assess the algorithm's performance on histidine kinases and response regulators from bacterial two-component signaling systems. The algorithm proves successful without any a pri...

  4. In vivo bacterial morphogenetic protein interactions

    NARCIS (Netherlands)

    van der Ploeg, R.; den Blaauwen, T.; Meghea, A.

    2012-01-01

    This chapter will discuss none-invasive techniques that are widely used to study protein-protein interactions. As an example, their application in exploring interactions between proteins involved in bacterial cell division will be evaluated. First, bacterial morphology and cell division of the rod-s

  5. Ontology integration to identify protein complex in protein interaction networks

    Directory of Open Access Journals (Sweden)

    Yang Zhihao

    2011-10-01

    Full Text Available Abstract Background Protein complexes 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 protein complexes detection algorithms. Methods We have developed novel semantic similarity method, 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. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes. Results The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches.

  6. 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...... 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...... interactions between proteins and lipids. First, interactions of soluble proteins with membranes and specific lipids were studied, using two proteins: Annexin V and Tma1. The protein was first subjected to a lipid/protein overlay assay to identify candidate interaction partners in a fast and efficient way...

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

    Science.gov (United States)

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

    2015-01-01

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

  8. Yeast Interacting Proteins Database: YGL198W, YDR084C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available les; computational analysis of large-scale protein-protein interaction data suggests a possible role in vesi... GTPases, localized to late Golgi vesicles; computational analysis of large-scale protein-protein interactio

  9. Yeast Interacting Proteins Database: YNL189W, YOR284W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ait as prey (0) YOR284W HUA2 Cytoplasmic protein of unknown function; computational analysis of large-scal...protein of unknown function; computational analysis of large-scale protein-protein interaction data suggests

  10. 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.

  11. Hemoglobin interacting proteins and implications of spectrin hemoglobin interaction.

    Science.gov (United States)

    Basu, Avik; Chakrabarti, Abhijit

    2015-10-14

    In this report we have analyzed interacting partners of hemoglobin inside erythrocyte and sought possible implications of hemoglobin-spectrin interaction. Our list of identified cytosolic hemoglobin interacting proteins includes redox regulators like peroxiredoxin-2, Cu-Zn superoxide dismutase, catalase, aldehyde dehydrogenase-1, flavin reductase and chaperones like HSP70, α-hemoglobin stabilizing protein. Others include metabolic enzymes like carbonic anhydrase-1, selenium binding protein-1, purine nucleoside phosphorylase and nucleoside diphosphate kinase. Additionally, various membrane proteins like α and β spectrin, ankyrin, band3, protein4.1, actin and glyceraldehyde 3 phosphate dehydrogenase have been shown to interact with hemoglobin. Our result indicates that major membrane skeleton protein spectrin, that also has a chaperone like activity, helps to fold the unstable alpha-globin chains in vitro. Taken together our results could provide insight into a protein network evolved around hemoglobin molecule inside erythrocyte that may add a new perspective in understanding the hemoglobin function and homeostasis.

  12. Protein Synthesis--An Interactive Game.

    Science.gov (United States)

    Clements, Lee Ann J.; Jackson, Karen E.

    1998-01-01

    Describes an interactive game designed to help students see and understand the dynamic relationship between DNA, RNA, and proteins. Appropriate for either a class or laboratory setting, following a lecture session about protein synthesis. (DDR)

  13. 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 wi...

  14. Yeast Interacting Proteins Database: YLR291C, YJL199C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ved in closely related Saccharomyces species; protein detected in large-scale protein-protein interaction studies...in large-scale protein-protein interaction studies Rows with this prey as prey Rows with this prey as prey (

  15. Yeast Interacting Proteins Database: YML064C, YJL199C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available y related Saccharomyces species; protein detected in large-scale protein-protein interaction studies Rows wi...in-protein interaction studies Rows with this prey as prey (4) Rows with this prey as bait (1) 28 6 3 4 0 0 ...d in closely related Saccharomyces species; protein detected in large-scale prote

  16. Yeast Interacting Proteins Database: YOR124C, YGR268C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available that of Type I J-proteins; computational analysis of large-scale protein-protein interaction data suggests a...tational analysis of large-scale protein-protein interaction data suggests a possible role in actin patch as

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

    Directory of Open Access Journals (Sweden)

    Martin H Schaefer

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

  18. 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....

  19. Protein interaction networks from literature mining

    Science.gov (United States)

    Ihara, Sigeo

    2005-03-01

    The ability to accurately predict and understand physiological changes in the biological network system in response to disease or drug therapeutics is of crucial importance in life science. The extensive amount of gene expression data generated from even a single microarray experiment often proves difficult to fully interpret and comprehend the biological significance. An increasing knowledge of protein interactions stored in the PubMed database, as well as the advancement of natural language processing, however, makes it possible to construct protein interaction networks from the gene expression information that are essential for understanding the biological meaning. From the in house literature mining system we have developed, the protein interaction network for humans was constructed. By analysis based on the graph-theoretical characterization of the total interaction network in literature, we found that the network is scale-free and semantic long-ranged interactions (i.e. inhibit, induce) between proteins dominate in the total interaction network, reducing the degree exponent. Interaction networks generated based on scientific text in which the interaction event is ambiguously described result in disconnected networks. In contrast interaction networks based on text in which the interaction events are clearly stated result in strongly connected networks. The results of protein-protein interaction networks obtained in real applications from microarray experiments are discussed: For example, comparisons of the gene expression data indicative of either a good or a poor prognosis for acute lymphoblastic leukemia with MLL rearrangements, using our system, showed newly discovered signaling cross-talk.

  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. Yeast Interacting Proteins Database: YPR040W, YDL188C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YPR040W TIP41 Protein that interacts physically and genetically with Tap42p, which ...ait ORF YPR040W Bait gene name TIP41 Bait description Protein that interacts physically and genetically

  2. Yeast Interacting Proteins Database: YPR040W, YDL134C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YPR040W TIP41 Protein that interacts physically and genetically with Tap42p, which ...Bait ORF YPR040W Bait gene name TIP41 Bait description Protein that interacts physically and genetically

  3. 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

  4. Yeast Interacting Proteins Database: YGR268C, YER125W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available larity to that of Type I J-proteins; computational analysis of large-scale protein-protein interaction data ...equence similarity to that of Type I J-proteins; computational analysis of large-scale protein-protein inter

  5. Mapping interactions of Chikungunya virus nonstructural proteins.

    Science.gov (United States)

    Sreejith, R; Rana, Jyoti; Dudha, Namrata; Kumar, Kapila; Gabrani, Reema; Sharma, Sanjeev K; Gupta, Amita; Vrati, Sudhanshu; Chaudhary, Vijay K; Gupta, Sanjay

    2012-10-01

    The four nonstructural proteins (nsPs1-4) of Chikungunya virus (CHIKV) play important roles involving enzymatic activities and specific interactions with both viral and host components, during different stages of viral pathogenesis. Elucidation of the presence and/or absence of interactions among nsPs in a systematic manner is thus of scientific interest. In the current study, each pair-wise combination among the four nonstructural proteins of CHIKV was systematically analyzed for possible interactions. Six novel protein interactions were identified for CHIKV, using systems such as yeast two-hybrid, GST pull down and ELISA, three of which have not been previously reported for the genus Alphavirus. These interactions form a network of organized associations that suggest the spatial arrangement of nonstructural proteins in the late replicase complex. The study identified novel interactions as well as concurred with previously described associations in related alphaviruses.

  6. In vivo bacterial morphogenetic protein interactions

    OpenAIRE

    van der Ploeg, R.; den Blaauwen, T.; Meghea, A.

    2012-01-01

    This chapter will discuss none-invasive techniques that are widely used to study protein-protein interactions. As an example, their application in exploring interactions between proteins involved in bacterial cell division will be evaluated. First, bacterial morphology and cell division of the rod-shaped bacterium Escherichia coli will be introduced. Next, three bacterial two-hybrid methods and three Förster resonance energy transfer detection methods that are frequently applied to detect int...

  7. An Interactive Introduction to Protein Structure

    Science.gov (United States)

    Lee, W. Theodore

    2004-01-01

    To improve student understanding of protein structure and the significance of noncovalent interactions in protein structure and function, students are assigned a project to write a paper complemented with computer-generated images. The assignment provides an opportunity for students to select a protein structure that is of interest and detail…

  8. Yeast Interacting Proteins Database: YJR091C, YKL076C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...NA-binding proteins, interacts with mRNAs encoding membrane-associated proteins; involved in localizing the

  9. Yeast Interacting Proteins Database: YJR091C, YNR048W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...y of RNA-binding proteins, interacts with mRNAs encoding membrane-associated proteins; involved in localizing

  10. Yeast Interacting Proteins Database: YJR091C, YML015C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...y of RNA-binding proteins, interacts with mRNAs encoding membrane-associated proteins; involved in localizing

  11. Yeast Interacting Proteins Database: YML064C, YOR284W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available th this bait as prey (0) YOR284W HUA2 Cytoplasmic protein of unknown function; computational analysis of large-scale... unknown function; computational analysis of large-scale protein-protein interact

  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. Inferring interaction partners from protein sequences

    Science.gov (United States)

    Bitbol, Anne-Florence; Dwyer, Robert S.; Colwell, Lucy J.; Wingreen, Ned S.

    2016-01-01

    Specific protein−protein interactions are crucial in the cell, both to ensure the formation and stability of multiprotein complexes and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners, causing their sequences to be correlated. Here we exploit these correlations to accurately identify, from sequence data alone, which proteins are specific interaction partners. Our general approach, which employs a pairwise maximum entropy model to infer couplings between residues, has been successfully used to predict the 3D structures of proteins from sequences. Thus inspired, we introduce an iterative algorithm to predict specific interaction partners from two protein families whose members are known to interact. We first assess the algorithm’s performance on histidine kinases and response regulators from bacterial two-component signaling systems. We obtain a striking 0.93 true positive fraction on our complete dataset without any a priori knowledge of interaction partners, and we uncover the origin of this success. We then apply the algorithm to proteins from ATP-binding cassette (ABC) transporter complexes, and obtain accurate predictions in these systems as well. Finally, we present two metrics that accurately distinguish interacting protein families from noninteracting ones, using only sequence data. PMID:27663738

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

    Science.gov (United States)

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

    2002-05-01

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

  15. Protein-protein interaction based on pairwise similarity

    Directory of Open Access Journals (Sweden)

    Zaki Nazar

    2009-05-01

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

  16. 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

  17. Structural similarity of genetically interacting proteins

    Directory of Open Access Journals (Sweden)

    Nussinov Ruth

    2008-07-01

    Full Text Available Abstract Background The study of gene mutants and their interactions is fundamental to understanding gene function and backup mechanisms within the cell. The recent availability of large scale genetic interaction networks in yeast and worm allows the investigation of the biological mechanisms underlying these interactions at a global scale. To date, less than 2% of the known genetic interactions in yeast or worm can be accounted for by sequence similarity. Results Here, we perform a genome-scale structural comparison among protein pairs in the two species. We show that significant fractions of genetic interactions involve structurally similar proteins, spanning 7–10% and 14% of all known interactions in yeast and worm, respectively. We identify several structural features that are predictive of genetic interactions and show their superiority over sequence-based features. Conclusion Structural similarity is an important property that can explain and predict genetic interactions. According to the available data, the most abundant mechanism for genetic interactions among structurally similar proteins is a common interacting partner shared by two genetically interacting proteins.

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

    Science.gov (United States)

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

    2015-03-01

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

  19. Dynamic fluctuations of protein-carbohydrate interactions promote protein aggregation.

    Directory of Open Access Journals (Sweden)

    Vladimir Voynov

    Full Text Available Protein-carbohydrate interactions are important for glycoprotein structure and function. Antibodies of the IgG class, with increasing significance as therapeutics, are glycosylated at a conserved site in the constant Fc region. We hypothesized that disruption of protein-carbohydrate interactions in the glycosylated domain of antibodies leads to the exposure of aggregation-prone motifs. Aggregation is one of the main problems in protein-based therapeutics because of immunogenicity concerns and decreased efficacy. To explore the significance of intramolecular interactions between aromatic amino acids and carbohydrates in the IgG glycosylated domain, we utilized computer simulations, fluorescence analysis, and site-directed mutagenesis. We find that the surface exposure of one aromatic amino acid increases due to dynamic fluctuations. Moreover, protein-carbohydrate interactions decrease upon stress, while protein-protein and carbohydrate-carbohydrate interactions increase. Substitution of the carbohydrate-interacting aromatic amino acids with non-aromatic residues leads to a significantly lower stability than wild type, and to compromised binding to Fc receptors. Our results support a mechanism for antibody aggregation via decreased protein-carbohydrate interactions, leading to the exposure of aggregation-prone regions, and to aggregation.

  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. Yeast Interacting Proteins Database: YGL198W, YGL161C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YGL198W YIP4 Protein that interacts with Rab GTPases, localized to late Golgi vesicles; comput...that interacts with Rab GTPases, localized to late Golgi vesicles; computational ...eracts with Rab GTPases, localized to late Golgi vesicles; computational analysis of large-scale protein-pro...ized to late Golgi vesicles; computational analysis of large-scale protein-protein interaction data suggests

  2. Yeast Interacting Proteins Database: YGL161C, YGL198W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YGL161C YIP5 Protein that interacts with Rab GTPases, localized to late Golgi vesicles; comput...that interacts with Rab GTPases, localized to late Golgi vesicles; computational ...eracts with Rab GTPases, localized to late Golgi vesicles; computational analysis of large-scale protein-pro...ized to late Golgi vesicles; computational analysis of large-scale protein-protein interaction data suggests

  3. 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

  4. Intraviral protein interactions of Chandipura virus.

    Science.gov (United States)

    Kumar, Kapila; Rana, Jyoti; Sreejith, R; Gabrani, Reema; Sharma, Sanjeev K; Gupta, Amita; Chaudhary, Vijay K; Gupta, Sanjay

    2012-10-01

    Chandipura virus (CHPV) is an emerging rhabdovirus responsible for several outbreaks of fatal encephalitis among children in India. The characteristic structure of the virus is a result of extensive and specific interplay among its five encoded proteins. The revelation of interactions among CHPV proteins can help in gaining insight into viral architecture and pathogenesis. In the current study, we carried out comprehensive yeast two-hybrid (Y2H) analysis to elucidate intraviral protein-protein interactions. All of the interactions identified by Y2H were assessed for reliability by GST pull-down and ELISA. A total of eight interactions were identified among four viral proteins. Five of these interactions are being reported for the first time for CHPV. Among these, the glycoprotein (G)-nucleocapsid (N) interaction could be considered novel, as this has not been reported for any members of the family Rhabdoviridae. This study provides a framework within which the roles of the identified protein interactions can be explored further for understanding the biology of this virus at the molecular level.

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

    Institute of Scientific and Technical Information of China (English)

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

    2003-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  7. Evolvability of yeast protein-protein interaction interfaces.

    Science.gov (United States)

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

    2012-06-22

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

  8. Treponema denticola interactions with host proteins

    Directory of Open Access Journals (Sweden)

    J. Christopher Fenno

    2012-02-01

    Full Text Available Oral Treponema species, most notably T. denticola, are implicated in the destructive effects of human periodontal disease. Progress in the molecular analysis of interactions between T. denticola and host proteins is reviewed here, with particular emphasis on the characterization of surface-expressed and secreted proteins of T. denticola involved in interactions with host cells, extracellular matrix components, and components of the innate immune system.

  9. Moonlighting proteins in sperm-egg interactions.

    Science.gov (United States)

    Petit, François M; Serres, Catherine; Auer, Jana

    2014-12-01

    Sperm-egg interaction is a highly species-specific step during the fertilization process. The first steps consist of recognition between proteins on the sperm head and zona pellucida (ZP) glycoproteins, the acellular coat that protects the oocyte. We aimed to determine which sperm head proteins interact with ZP2, ZP3 and ZP4 in humans. Two approaches were combined to identify these proteins: immunoblotting human spermatozoa targeted by antisperm antibodies (ASAs) from infertile men and far-Western blotting of human sperm proteins overlaid by each of the human recombinant ZP (hrZP) proteins. We used a proteomic approach with 2D electrophoretic separation of sperm protein revealed using either ASAs eluted from infertile patients or recombinant human ZP glycoproteins expressed in Chinese-hamster ovary (CHO) cells. Only spots highlighted by both methods were analysed by MALDI-MS/MS for identification. We identified proteins already described in human spermatozoa, but implicated in different metabolic pathways such as glycolytic enzymes [phosphokinase type 3 (PK3), enolase 1 (ENO1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), aldolase A (ALDOA) and triose phosphate isomerase (TPI)], detoxification enzymes [GST Mu (GSTM) and phospholipid hydroperoxide glutathione peroxidase (PHGPx) 4], ion channels [voltage-dependent anion channel 2 (VDAC2)] or structural proteins (outer dense fibre 2). Several proteins were localized on the sperm head by indirect immunofluorescence, and their interaction with ZP proteins was confirmed by co-precipitation experiments. These results confirm the complexity of the sperm-ZP recognition process in humans with the implication of different proteins interacting with the main three ZP glycoproteins. The multiple roles of these proteins suggest that they are multifaceted or moonlighting proteins.

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

    Directory of Open Access Journals (Sweden)

    Margaret E Johnson

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

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

    Directory of Open Access Journals (Sweden)

    Hirsh Aaron E

    2003-05-01

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

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

    NARCIS (Netherlands)

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

    2013-01-01

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

  13. 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

  14. 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

  15. Understanding Protein-Protein Interactions Using Local Structural Features

    DEFF Research Database (Denmark)

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

    2013-01-01

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

  16. 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.

  17. Length, protein–protein interactions, and complexity

    NARCIS (Netherlands)

    Tan, T.; Frenkel, D.; Gupta, V.; Deem, M.W.

    2005-01-01

    The evolutionary reason for the increase in gene length from archaea to prokaryotes to eukaryotes observed in large-scale genome sequencing efforts has been unclear. We propose here that the increasing complexity of protein–protein interactions has driven the selection of longer proteins, as they ar

  18. 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...

  19. Protein-protein interaction networks in the spinocerebellar ataxias

    OpenAIRE

    David C Rubinsztein

    2006-01-01

    A large yeast two-hybrid study investigating whether the proteins mutated in different forms of spinocerebellar ataxia have interacting protein partners in common suggests that some forms do share common pathways, and will provide a valuable resource for future work on these diseases.

  20. 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.

  1. 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

  2. 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...

  3. Website on Protein Interaction and Protein Structure Related Work

    Science.gov (United States)

    Samanta, Manoj; Liang, Shoudan; Biegel, Bryan (Technical Monitor)

    2003-01-01

    In today's world, three seemingly diverse fields - computer information technology, nanotechnology and biotechnology are joining forces to enlarge our scientific knowledge and solve complex technological problems. Our group is dedicated to conduct theoretical research exploring the challenges in this area. The major areas of research include: 1) Yeast Protein Interactions; 2) Protein Structures; and 3) Current Transport through Small Molecules.

  4. 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.

  5. Yeast Interacting Proteins Database: YPR148C, YDL237W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YPR148C - Protein of unknown function that may interact with ribosomes, based on co-purification experiments... with ribosomes, based on co-purification experiments; green fluorescent protein

  6. Yeast Interacting Proteins Database: YLR347C, YLR377C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ucleoporins to mediate nuclear import of NLS-containing cargo proteins via the nuclear pore complex; regulat...0p; interacts with nucleoporins to mediate nuclear import of NLS-containing cargo proteins via the nuclear p

  7. Yeast Interacting Proteins Database: YLR347C, YBR176W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ucleoporins to mediate nuclear import of NLS-containing cargo proteins via the nuclear pore complex; regulat...p; interacts with nucleoporins to mediate nuclear import of NLS-containing cargo proteins via the nuclear po

  8. Yeast Interacting Proteins Database: YPL070W, YBR176W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available utational analysis of large-scale protein-protein interaction data suggests a possible role in transcription...otein of unknown function containing a Vps9 domain; computational analysis of large-scale

  9. Yeast Interacting Proteins Database: YDR084C, YGL198W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available with Rab GTPases, localized to late Golgi vesicles; computational analysis of large-scale...omputational analysis of large-scale protein-protein interaction data suggests a possible role in vesicle-me

  10. Yeast Interacting Proteins Database: YDL226C, YJL151C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available s bait as prey (0) YJL151C SNA3 Integral membrane protein localized to vacuolar intralumenal vesicles, computation...intralumenal vesicles, computational analysis of large-scale protein-protein interaction data suggests a pos... gene name SNA3 Prey description Integral membrane protein localized to vacuolar

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

    Science.gov (United States)

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

    2006-04-01

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

  12. Yeast Interacting Proteins Database: YDR026C, YDL030W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YDR026C - Protein of unknown function that may interact with ribosomes, based on co-purification...ein of unknown function that may interact with ribosomes, based on co-purification

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

    Directory of Open Access Journals (Sweden)

    Oleksii Kuchaiev

    2009-08-01

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

  14. Proteomic analysis of SETD6 interacting proteins.

    Science.gov (United States)

    Cohn, Ofir; Chen, Ayelet; Feldman, Michal; Levy, Dan

    2016-03-01

    SETD6 (SET-domain-containing protein 6) is a mono-methyltransferase that has been shown to methylate RelA and H2AZ. Using a proteomic approach we recently identified several new SETD6 substrates. To identify novel SETD6 interacting proteins, SETD6 was immunoprecipitated (IP) from Human erythromyeloblastoid leukemia K562 cells. SETD6 binding proteins were subjected to mass-spectrometry analysis resulting in 115 new SETD6 binding candidates. STRING database was used to map the SETD6 interactome network. Network enrichment analysis of biological processes with Gene Ontology (GO) database, identified three major groups; metabolic processes, muscle contraction and protein folding.

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

    Institute of Scientific and Technical Information of China (English)

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

    2011-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  17. 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

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

    Science.gov (United States)

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

    2010-01-01

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

  19. Predicting protein-protein interactions in the post synaptic density.

    Science.gov (United States)

    Bar-shira, Ossnat; Chechik, Gal

    2013-09-01

    The post synaptic density (PSD) is a specialization of the cytoskeleton at the synaptic junction, composed of hundreds of different proteins. Characterizing the protein components of the PSD and their interactions can help elucidate the mechanism of long-term changes in synaptic plasticity, which underlie learning and memory. Unfortunately, our knowledge of the proteome and interactome of the PSD is still partial and noisy. In this study we describe a computational framework to improve the reconstruction of the PSD network. The approach is based on learning the characteristics of PSD protein interactions from a set of trusted interactions, expanding this set with data collected from large scale repositories, and then predicting novel interaction with proteins that are suspected to reside in the PSD. Using this method we obtained thirty predicted interactions, with more than half of which having supporting evidence in the literature. We discuss in details two of these new interactions, Lrrtm1 with PSD-95 and Src with Capg. The first may take part in a mechanism underlying glutamatergic dysfunction in schizophrenia. The second suggests an alternative mechanism to regulate dendritic spines maturation.

  20. Protein-protein interaction network of celiac disease

    Science.gov (United States)

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

    2016-01-01

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

  1. 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

  2. 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

  3. Annotation and retrieval in protein interaction databases

    Science.gov (United States)

    Cannataro, Mario; Hiram Guzzi, Pietro; Veltri, Pierangelo

    2014-06-01

    Biological databases have been developed with a special focus on the efficient retrieval of single records or the efficient computation of specialized bioinformatics algorithms against the overall database, such as in sequence alignment. The continuos production of biological knowledge spread on several biological databases and ontologies, such as Gene Ontology, and the availability of efficient techniques to handle such knowledge, such as annotation and semantic similarity measures, enable the development on novel bioinformatics applications that explicitly use and integrate such knowledge. After introducing the annotation process and the main semantic similarity measures, this paper shows how annotations and semantic similarity can be exploited to improve the extraction and analysis of biologically relevant data from protein interaction databases. As case studies, the paper presents two novel software tools, OntoPIN and CytoSeVis, both based on the use of Gene Ontology annotations, for the advanced querying of protein interaction databases and for the enhanced visualization of protein interaction networks.

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

    Directory of Open Access Journals (Sweden)

    Melody G Campbell

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

  5. Yeast Interacting Proteins Database: YHR114W, YDR422C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available substrate specificity; vacuolar protein containing KIS (Kinase-Interacting Sequence) and ASC (Association w...strate specificity; vacuolar protein containing KIS (Kinase-Interacting Sequence) and ASC (Association with ...e 4 CuraGen (0 or 1) 0 S. Fields (0 or 1) 0 Association (0 or 1,YPD) 0 Complex (0

  6. Yeast Interacting Proteins Database: YDR425W, YGL198W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available with this bait as prey (0) YGL198W YIP4 Protein that interacts with Rab GTPases, localized to late Golgi vesicles; computation...IP4 Prey description Protein that interacts with Rab GTPases, localized to late Golgi vesicles; computatio

  7. Yeast Interacting Proteins Database: YBR108W, YGR136W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YBR108W AIM3 Protein interacting with Rvs167p; null mutant is viable and displays e...w YBR108W Bait ORF YBR108W Bait gene name AIM3 Bait description Protein interacting with Rvs167p; null mutant is viable and display

  8. Yeast Interacting Proteins Database: YBR108W, YDR388W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YBR108W AIM3 Protein interacting with Rvs167p; null mutant is viable and displays e...l mutant is viable and displays elevated frequency of mitochondrial genome loss R...8 - Show YBR108W Bait ORF YBR108W Bait gene name AIM3 Bait description Protein interacting with Rvs167p; nul

  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. Yeast Interacting Proteins Database: YMR047C, YDR229W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available R229W IVY1 Phospholipid-binding protein that interacts with both Ypt7p and Vps33p, may partially...holipid-binding protein that interacts with both Ypt7p and Vps33p, may partially counteract the action of Vp

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

    Science.gov (United States)

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

    2016-06-14

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

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

    Science.gov (United States)

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

    2016-06-01

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

  13. 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

  14. Targeting protein-protein interactions for parasite control.

    Directory of Open Access Journals (Sweden)

    Christina M Taylor

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

  15. Yeast Interacting Proteins Database: YJR091C, YOR265W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...n Member of the Puf family of RNA-binding proteins, interacts with mRNAs encoding membrane-associated proteins; involved in localizin

  16. Yeast Interacting Proteins Database: YJR091C, YKL113C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...r of the Puf family of RNA-binding proteins, interacts with mRNAs encoding membrane-associated proteins; involved in localizing

  17. Yeast Interacting Proteins Database: YJR091C, YDR389W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...d proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpression causes increased sensi...scription Member of the Puf family of RNA-binding proteins, interacts with mRNAs encoding membrane-associate

  18. Yeast Interacting Proteins Database: YJR091C, YDL147W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...g proteins, interacts with mRNAs encoding membrane-associated proteins; involved in localizing the Arp2/3 co

  19. 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

  20. 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

  1. PCorral--interactive mining of protein interactions from MEDLINE.

    Science.gov (United States)

    Li, Chen; Jimeno-Yepes, Antonio; Arregui, Miguel; Kirsch, Harald; Rebholz-Schuhmann, Dietrich

    2013-01-01

    The extraction of information from the scientific literature is a complex task-for researchers doing manual curation and for automatic text processing solutions. The identification of protein-protein interactions (PPIs) requires the extraction of protein named entities and their relations. Semi-automatic interactive support is one approach to combine both solutions for efficient working processes to generate reliable database content. In principle, the extraction of PPIs can be achieved with different methods that can be combined to deliver high precision and/or high recall results in different combinations at the same time. Interactive use can be achieved, if the analytical methods are fast enough to process the retrieved documents. PCorral provides interactive mining of PPIs from the scientific literature allowing curators to skim MEDLINE for PPIs at low overheads. The keyword query to PCorral steers the selection of documents, and the subsequent text analysis generates high recall and high precision results for the curator. The underlying components of PCorral process the documents on-the-fly and are available, as well, as web service from the Whatizit infrastructure. The human interface summarizes the identified PPI results, and the involved entities are linked to relevant resources and databases. Altogether, PCorral serves curator at both the beginning and the end of the curation workflow for information retrieval and information extraction. Database URL: http://www.ebi.ac.uk/Rebholz-srv/pcorral.

  2. 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

  3. Studying protein-protein interactions using peptide arrays

    NARCIS (Netherlands)

    Katz, C.; Levy-Beladev, L.; Rotem-Bamberger, S.; Rito, T.; Rudiger, S.G.D.; Friedler, A.

    2010-01-01

    Screening of arrays and libraries of compounds is well-established as a high-throughput method for detecting and analyzing interactions in both biological and chemical systems. Arrays and libraries can be composed from various types of molecules, ranging from small organic compounds to DNA, proteins

  4. A framework for protein and membrane interactions

    CERN Document Server

    Bacci, Giorgio; Miculan, Marino; 10.4204/EPTCS.11.2

    2009-01-01

    We introduce the BioBeta Framework, a meta-model for both protein-level and membrane-level interactions of living cells. This formalism aims to provide a formal setting where to encode, compare and merge models at different abstraction levels; in particular, higher-level (e.g. membrane) activities can be given a formal biological justification in terms of low-level (i.e., protein) interactions. A BioBeta specification provides a protein signature together a set of protein reactions, in the spirit of the kappa-calculus. Moreover, the specification describes when a protein configuration triggers one of the only two membrane interaction allowed, that is "pinch" and "fuse". In this paper we define the syntax and semantics of BioBeta, analyse its properties, give it an interpretation as biobigraphical reactive systems, and discuss its expressivity by comparing with kappa-calculus and modelling significant examples. Notably, BioBeta has been designed after a bigraphical metamodel for the same purposes. Hence, each ...

  5. Detection of protein-protein interactions using tandem affinity purification.

    Science.gov (United States)

    Goodfellow, Ian; Bailey, Dalan

    2014-01-01

    Tandem affinity purification (TAP) is an invaluable technique for identifying interaction partners for an affinity tagged bait protein. The approach relies on the fusion of dual tags to the bait before separate rounds of affinity purification and precipitation. Frequently two specific elution steps are also performed to increase the specificity of the overall technique. In the method detailed here, the two tags used are protein G and a short streptavidin binding peptide; however, many variations can be employed. In our example the tags are separated by a cleavable tobacco etch virus protease target sequence, allowing for specific elution after the first round of affinity purification. Proteins isolated after the final elution step in this process are concentrated before being identified by mass spectrometry. The use of dual affinity tags and specific elution in this technique dramatically increases both the specificity and stringency of the pull-downs, ensuring a low level of background nonspecific interactions.

  6. 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.

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

    Science.gov (United States)

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

    2015-12-29

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

  8. Yeast Interacting Proteins Database: YEL017W, YEL017W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available 17W GTT3 Protein of unknown function with a possible role in glutathione metabolism, as suggested by compu...Bait description Protein of unknown function with a possible role in glutathione metabolism, as suggested by comput...ion Protein of unknown function with a possible role in glutathione metabolism, as suggested by computationa...YEL017W GTT3 Protein of unknown function with a possible role in glutathione metabolism, as suggested by com...putational analysis of large-scale protein-protein interaction data; GFP-fusion pro

  9. Sequence Motifs in MADS Transcription Factors Responsible for Specificity and Diversification of Protein-Protein Interaction

    NARCIS (Netherlands)

    Dijk, van A.D.J.; Morabito, G.; Fiers, M.A.; Ham, van R.C.H.J.; Angenent, G.C.; Immink, R.G.H.

    2010-01-01

    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 famil

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

    OpenAIRE

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

    2015-01-01

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

  11. The Development and Characterization of Protein-Based Stationary Phases for Studying Drug-Protein and Protein-Protein Interactions

    OpenAIRE

    Sanghvi, Mitesh; Moaddel, Ruin; Wainer, Irving W.

    2011-01-01

    Protein-based liquid chromatography stationary phases are used in bioaffinity chromatography for studying drug-protein interactions, the determination of binding affinities, competitive and allosteric interactions, as well as for studying protein-protein interactions. This review addresses the development and characterization of protein-based stationary phase, and the application of these phases using frontal and zonal chromatography techniques. The approach will be illustrated using immobili...

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

    Directory of Open Access Journals (Sweden)

    Craescu Constantin T

    2011-05-01

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

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

    Science.gov (United States)

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

    2013-10-01

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

  14. Sentence Simplification Aids Protein-Protein Interaction Extraction

    CERN Document Server

    Jonnalagadda, Siddhartha

    2010-01-01

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

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

    Science.gov (United States)

    Park, Byungkyu; Han, Kyungsook

    2010-02-01

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

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

    Science.gov (United States)

    Yang, Lei; Zhao, Xudong; Tang, Xianglong

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Pang Chi

    2008-11-01

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

  18. Yeast Interacting Proteins Database: YDL167C, YBR212W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available as bait (2) Rows with this bait as prey (0) YBR212W NGR1 RNA binding protein that negatively regulates grow...ption RNA binding protein that negatively regulates growth rate; interacts with the 3' UTR of the mitochondr

  19. 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

  20. 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.

  1. Yeast Interacting Proteins Database: YOR285W, YDR233C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available racts with exocyst subunit Sec6p and with Yip3p; also interacts with Sbh1p; null mutant has an altered (most...tion ER membrane protein that interacts with exocyst subunit Sec6p and with Yip3p; also interacts with Sbh1p; null mutant has an alte...red (mostly cisternal) ER morphology; member of the RTNL

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

    Institute of Scientific and Technical Information of China (English)

    代启国; 郭茂祖; 刘晓燕; 滕志霞; 王春宇

    2014-01-01

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

  3. Comprehensive peptidomimetic libraries targeting protein-protein interactions.

    Science.gov (United States)

    Whitby, Landon R; Boger, Dale L

    2012-10-16

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

  4. 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

  5. Support vector machine for predicting protein interactions using domain scores

    Institute of Scientific and Technical Information of China (English)

    PENG Xin-jun; WANG Yi-fei

    2009-01-01

    Protein-protein interactions play a crucial role in the cellular process such as metabolic pathways and immunological recognition. This paper presents a new domain score-based support vector machine (SVM) to infer protein interactions, which can be used not only to explore all possible domain interactions by the kernel method, but also to reflect the evolutionary conservation of domains in proteins by using the domain scores of proteins. The experimental result on the Saccharomyces cerevisiae dataset demonstrates that this approach can predict protein-protein interactions with higher performances compared to the existing approaches.

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

    Directory of Open Access Journals (Sweden)

    Ekaterina Kotelnikova

    2007-01-01

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

  7. Yeast Interacting Proteins Database: YDL239C, YPL255W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YDL239C ADY3 Protein required for spore wall formation, thought to mediate assembly... of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p...it as prey (1) YPL255W BBP1 Protein required for the spindle pole body (SPB) dupl...ows with this prey as bait (0) 4 8 3 4 0 0 0 0 0 - - - - - 0 0 7 - Show YDL239C Bait ORF YDL...ediate assembly of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindl

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

    Science.gov (United States)

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

    2010-03-01

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

  9. Interaction graph mining for protein complexes using local clique merging.

    Science.gov (United States)

    Li, Xiao-Li; Tan, Soon-Heng; Foo, Chuan-Sheng; Ng, See-Kiong

    2005-01-01

    While recent technological advances have made available large datasets of experimentally-detected pairwise protein-protein interactions, there is still a lack of experimentally-determined protein complex data. To make up for this lack of protein complex data, we explore the mining of existing protein interaction graphs for protein complexes. This paper proposes a novel graph mining algorithm to detect the dense neighborhoods (highly connected regions) in an interaction graph which may correspond to protein complexes. Our algorithm first locates local cliques for each graph vertex (protein) and then merge the detected local cliques according to their affinity to form maximal dense regions. We present experimental results with yeast protein interaction data to demonstrate the effectiveness of our proposed method. Compared with other existing techniques, our predicted complexes can match or overlap significantly better with the known protein complexes in the MIPS benchmark database. Novel protein complexes were also predicted to help biologists in their search for new protein complexes.

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

    Lifescience Database Archive (English)

    Full Text Available YDL239C ADY3 Protein required for spore wall formation, thought to mediate assembly... of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p...0 0 - - - - - 0 0 34 - Show YDL239C Bait ORF YDL239C Bait gene name ADY3 Bait des...ure at the leading edge of the prospore membrane via interaction with spindle pole body components; potentia

  11. Glycosphingolipid–Protein Interaction in Signal Transduction

    Directory of Open Access Journals (Sweden)

    Domenico Russo

    2016-10-01

    Full Text Available Glycosphingolipids (GSLs are a class of ceramide-based glycolipids essential for embryo development in mammals. The synthesis of specific GSLs depends on the expression of distinctive sets of GSL synthesizing enzymes that is tightly regulated during development. Several reports have described how cell surface receptors can be kept in a resting state or activate alternative signalling events as a consequence of their interaction with GSLs. Specific GSLs, indeed, interface with specific protein domains that are found in signalling molecules and which act as GSL sensors to modify signalling responses. The regulation exerted by GSLs on signal transduction is orthogonal to the ligand–receptor axis, as it usually does not directly interfere with the ligand binding to receptors. Due to their properties of adjustable production and orthogonal action on receptors, GSLs add a new dimension to the control of the signalling in development. GSLs can, indeed, dynamically influence progenitor cell response to morphogenetic stimuli, resulting in alternative differentiation fates. Here, we review the available literature on GSL–protein interactions and their effects on cell signalling and development.

  12. Glycosphingolipid–Protein Interaction in Signal Transduction

    Science.gov (United States)

    Russo, Domenico; Parashuraman, Seetharaman; D’Angelo, Giovanni

    2016-01-01

    Glycosphingolipids (GSLs) are a class of ceramide-based glycolipids essential for embryo development in mammals. The synthesis of specific GSLs depends on the expression of distinctive sets of GSL synthesizing enzymes that is tightly regulated during development. Several reports have described how cell surface receptors can be kept in a resting state or activate alternative signalling events as a consequence of their interaction with GSLs. Specific GSLs, indeed, interface with specific protein domains that are found in signalling molecules and which act as GSL sensors to modify signalling responses. The regulation exerted by GSLs on signal transduction is orthogonal to the ligand–receptor axis, as it usually does not directly interfere with the ligand binding to receptors. Due to their properties of adjustable production and orthogonal action on receptors, GSLs add a new dimension to the control of the signalling in development. GSLs can, indeed, dynamically influence progenitor cell response to morphogenetic stimuli, resulting in alternative differentiation fates. Here, we review the available literature on GSL–protein interactions and their effects on cell signalling and development. PMID:27754465

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2006-01-01

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

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

    Science.gov (United States)

    Kodama, Yutaka; Suetsugu, Noriyuki; Wada, Masamitsu

    2011-04-01

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

  15. 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.

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

    Institute of Scientific and Technical Information of China (English)

    SUN Jingchun; XU Jinlin; LI Yixue; SHI Tieliu

    2005-01-01

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

  17. Protein-lipid interactions at interfaces

    Directory of Open Access Journals (Sweden)

    Wilde, P.

    2000-04-01

    Full Text Available Foams and emulsions are both types of multiphase foods and are a dispersion of one immiscible phase (e.g. air or oil in another (e.g. water. Amphiphilic molecules (either proteins or chemical compounds are able to stabilise the interface between these phases and are termed emulsifiers. The ability of protein emulsifiers to bind lipid is reviewed, and the mechanisms underlying the behaviour of these and low molecular weight surfactants (LMWS at the interface are summarised. New research, exploiting atomic force microscopy, has given fresh insights into the mechanisms by which proteins and LMWS interact when both are present at the interface, compromising the stability of foams and emulsions stabilised by these mixtures. The understanding of component interactions at the interfacial level is essential if advances are to be made in the control and manipulation of multiphase foods during production and storage.Las espumas y las emulsiones son dispersiones de una fase inmiscible (ejemplo aire o aceite en otra (ejemplo agua. Las moléculas anfifílicas (bien proteínas o compuestos químicos pueden estabilizar la interfase y se denominan emulsionantes. En este artículo se revisa la habilidad de los emulsionantes proteínicos para enlazar lípidos y los mecanismos que subyacen en el comportamiento de estas moléculas así como de los tensioactivos de bajo peso molecular en la interfase. Recientes investigaciones que usan la microscopía han ofrecido visiones nuevas de los mecanismos mediante los cuales las proteínas y los tensioactivos de bajo peso molecular interaccionan cuando ambos están presentes en la interfase, comprometiendo la estabilidad de espumas y emulsiones estabilizadas por estas mezclas. El entendimiento de las interacciones entre componentes a nivel interfacial es esencial para lograr avances en el control y manipulación de alimentos multifases durante la producción y el almacenamiento.

  18. The Foundations of Protein-Ligand Interaction

    Science.gov (United States)

    Klebe, Gerhard

    For the specific design of a drug we must first answer the question: How does a drug achieve its activity? An active ingredient must, in order to develop its action, bind to a particular target molecule in the body. Usually this is a protein, but also nucleic acids in the form of RNA and DNA can be target structures for active agents. The most important condition for binding is at first that the active agent exhibits the correct size and shape in order to optimally fit into a cavity exposed to the surface of the protein, the "bindingpocket". It is further necessary for the surface properties of the ligand and protein to be mutually compatible to form specific interactions. In 1894 Emil Fischer compared the exact fit of a substrate for the catalytic centre of an enzyme with the picture of a "lock-and-key". Paul Ehrlich coined in 1913 "Corpora non agunt nisi fixata", literally "bodies do not work when they are not bound". He wanted to imply that active agents that are meant to kill bacteria or parasites must be "fixed" by them, i.e. linked to their structures. Both concepts form the starting point for any rational concept in the development of active pharmaceutical ingredients. In many respects they still apply today. A drug must, after being administered, reach its target and interact with a biological macromolecule. Specific agents have a large affinity and sufficient selectivity to bind to the macromolecule's active site. This is the only way they can develop the desired biological activity without side-effects.

  19. Yeast Interacting Proteins Database: YJR055W, YPL193W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available of 60S ribosomal subunits; functionally interacts with Dbp6p; functions in a late nucleoplasmic step of the...L193W Prey gene name RSA1 Prey description Protein involved in the assembly of 60S ribosomal subunits; functionally

  20. Yeast Interacting Proteins Database: YDL044C, YLR386W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available of mitochondrial RNA polymerase (Rpo41p) and couples RNA processing and translation to transcription Rows wi...protein that interacts with an N-terminal region of mitochondrial RNA polymerase (Rpo41p) and couples RNA pr

  1. Yeast Interacting Proteins Database: YIL007C, YOR117W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YIL007C NAS2 Proteasome-interacting protein involved in the assembly of the base su... - - - - - 0 0 3 4 Show YIL007C Bait ORF YIL007C Bait gene name NAS2 Bait description Proteasome-interacti

  2. Yeast Interacting Proteins Database: YNL041C, YDR229W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available pholipid-binding protein that interacts with both Ypt7p and Vps33p, may partially...teracts with both Ypt7p and Vps33p, may partially counteract the action of Vps33p and vice versa, localizes

  3. Yeast Interacting Proteins Database: YJR091C, YLR156W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ion with Jsn1p in a large-scale analysis Rows with this prey as prey (1) Rows with this prey as bait (0) 7 5...scription Putative protein of unknown function; exhibits a two-hybrid interaction with Jsn1p in a large-scale

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

    Science.gov (United States)

    2011-01-01

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

  5. 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.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  7. Elucidating the Interacting Domains of Chandipura Virus Nucleocapsid Protein

    Directory of Open Access Journals (Sweden)

    Kapila Kumar

    2013-01-01

    Full Text Available The nucleocapsid (N protein of Chandipura virus (CHPV plays a crucial role in viral life cycle, besides being an important structural component of the virion through proper organization of its interactions with other viral proteins. In a recent study, the authors had mapped the associations among CHPV proteins and shown that N protein interacts with four of the viral proteins: N, phosphoprotein (P, matrix protein (M, and glycoprotein (G. The present study aimed to distinguish the regions of CHPV N protein responsible for its interactions with other viral proteins. In this direction, we have generated the structure of CHPV N protein by homology modeling using SWISS-MODEL workspace and Accelrys Discovery Studio client 2.55 and mapped the domains of N protein using PiSQRD. The interactions of N protein fragments with other proteins were determined by ZDOCK rigid-body docking method and validated by yeast two-hybrid and ELISA. The study revealed a unique binding site, comprising of amino acids 1–30 at the N terminus of the nucleocapsid protein (N1 that is instrumental in its interactions with N, P, M, and G proteins. It was also observed that N2 associates with N and G proteins while N3 interacts with N, P, and M proteins.

  8. Mechanisms of peroxynitrite interactions with heme proteins.

    Science.gov (United States)

    Su, Jia; Groves, John T

    2010-07-19

    Oxygenated heme proteins are known to react rapidly with nitric oxide (NO) to produce peroxynitrite (PN) at the heme site. This process could lead either to attenuation of the effects of NO or to nitrosative protein damage. PN is a powerful nitrating and oxidizing agent that has been implicated in a variety of cell injuries. Accordingly, it is important to delineate the nature and variety of reaction mechanisms of PN interactions with heme proteins. In this Forum, we survey the range of reactions of PN with heme proteins, with particular attention to myoglobin and cytochrome c. While these two proteins are textbook paradigms for oxygen binding and electron transfer, respectively, both have recently been shown to have other important functions that involve NO and PN. We have recently described direct evidence that ferrylmyolgobin (ferrylMb) and nitrogen dioxide (NO(2)) are both produced during the reaction of PN and metmyolgobin (metMb) (Su, J.; Groves, J. T. J. Am. Chem. Soc. 2009, 131, 12979-12988). Kinetic evidence indicates that these products evolve from the initial formation of a caged radical intermediate [Fe(IV) horizontal lineO.NO(2)]. This caged pair reacts mainly via internal return with a rate constant k(r) to form metMb and nitrate in an oxygen-rebound scenario. Detectable amounts of ferrylMb are observed by stopped-flow spectrophotometry, appearing at a rate consistent with the rate, k(obs), of heme-mediated PN decomposition. Freely diffusing NO(2), which is liberated concomitantly from the radical pair (k(e)), preferentially nitrates myoglobin Tyr103 and added fluorescein. For cytochrome c, Raman spectroscopy has revealed that a substantial fraction of cytochrome c converts to a beta-sheet structure, at the expense of turns and helices at low pH (Balakrishnan, G.; Hu, Y.; Oyerinde, O. F.; Su, J.; Groves, J. T.; Spiro, T. G. J. Am. Chem. Soc., 2007, 129, 504-505). It is proposed that a short beta-sheet segment, comprising residues 37-39 and 58

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

    Science.gov (United States)

    Hall, Randy A

    2015-01-01

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

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

    Science.gov (United States)

    Gell, D; Jackson, S P

    1999-01-01

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

  11. Linguistic feature analysis for protein interaction extraction

    Directory of Open Access Journals (Sweden)

    Cornelis Chris

    2009-11-01

    Full Text Available Abstract Background The rapid growth of the amount of publicly available reports on biomedical experimental results has recently caused a boost of text mining approaches for protein interaction extraction. Most approaches rely implicitly or explicitly on linguistic, i.e., lexical and syntactic, data extracted from text. However, only few attempts have been made to evaluate the contribution of the different feature types. In this work, we contribute to this evaluation by studying the relative importance of deep syntactic features, i.e., grammatical relations, shallow syntactic features (part-of-speech information and lexical features. For this purpose, we use a recently proposed approach that uses support vector machines with structured kernels. Results Our results reveal that the contribution of the different feature types varies for the different data sets on which the experiments were conducted. The smaller the training corpus compared to the test data, the more important the role of grammatical relations becomes. Moreover, deep syntactic information based classifiers prove to be more robust on heterogeneous texts where no or only limited common vocabulary is shared. Conclusion Our findings suggest that grammatical relations play an important role in the interaction extraction task. Moreover, the net advantage of adding lexical and shallow syntactic features is small related to the number of added features. This implies that efficient classifiers can be built by using only a small fraction of the features that are typically being used in recent approaches.

  12. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    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.

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

    Lifescience Database Archive (English)

    Full Text Available YDL239C ADY3 Protein required for spore wall formation, thought to mediate assembly... 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, ...0 - - - - - 0 0 5 - Show YDL239C Bait ORF YDL239C Bait gene name ADY3 Bait descri... at the leading edge of the prospore membrane via interaction with spindle pole body components; potentially

  14. Yeast Interacting Proteins Database: YDL239C, YOR324C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YDL239C ADY3 Protein required for spore wall formation, thought to mediate assembly... of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p... as bait (0) 4 5 3 4 0 0 0 0 0 - - - - - 0 0 4 - Show YDL239C Bait ORF YDL239C Ba... a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle pole

  15. Yeast Interacting Proteins Database: YDL239C, YDR148C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YDL239C ADY3 Protein required for spore wall formation, thought to mediate assembly... of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p...s prey as bait (0) 4 15 2 5 0 0 0 0 0 - - - - - 0 0 3 - Show YDL239C Bait ORF YDL...mbly of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindl

  16. Yeast Interacting Proteins Database: YDL239C, YAL028W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YDL239C ADY3 Protein required for spore wall formation, thought to mediate assembly... of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p...(1) Rows with this prey as bait (0) 4 5 4 7 0 0 0 0 0 - - - - - 0 0 3 - Show YDL239C Bait ORF YDL... to mediate assembly of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindl

  17. Yeast Interacting Proteins Database: YDL239C, YBR072W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YDL239C ADY3 Protein required for spore wall formation, thought to mediate assembly... of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p... (3) Rows with this prey as bait (0) 4 52 1 1 0 0 0 0 0 - - - - - 0 0 3 - Show YDL239C Bait ORF YDL...ht to mediate assembly of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindl

  18. Yeast Interacting Proteins Database: YDL239C, YLR072W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YDL239C ADY3 Protein required for spore wall formation, thought to mediate assembly... of a Don1p-containing structure at the leading edge of the prospore membrane via interaction with spindle p... with this prey as prey (1) Rows with this prey as bait (0) 4 5 4 7 0 0 0 0 0 - - - - - 0 0 4 - Show YDL239C Bait ORF YDL...pore membrane via interaction with spindle pole body components; potentially phosphorylated by Cdc28p Rows w

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

    Institute of Scientific and Technical Information of China (English)

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

    2007-01-01

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

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

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

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

  1. Correlation of disorder between S. cerevisiae interacting proteins.

    Science.gov (United States)

    Rue-Albrecht, Kevin; Shields, Denis C; Khaldi, Nora

    2012-01-01

    Protein disorder has been frequently associated with protein-protein interaction. However, our knowledge of how protein disorder evolves within a network is limited. It is expected that physically interacting proteins evolve in a coordinated manner. This has so far been shown in their evolutionary rate, and in their gene expression levels. Here we examine the percentage of predicted disorder residues within binary and complex interacting proteins (physical and functional interactions respectively) to investigate how the disorder of a protein relates to that of its interacting partners. We show that the level of disorder of interacting proteins are correlated, with a greater correlation seen among proteins that are co-members of the same complex, and a lesser correlation between proteins that are documented as binary interactors of each other. There is a striking variation among complexes not only in their disorder, but in the extent to which the proteins within the complex differ in their levels of disorder, with RNA processes and protein binding complexes showing more variation in the disorder of their proteins, whilst other complexes show very little variation in the overall disorder of their constituent proteins. There is likely to be a stronger selection for complex subunits to have similar disorder, than is seen for proteins involved in binary interactions. Thus, binary interactions may be more resilient to changes in disorder than are complex interactions. These results add a new dimension to the role of disorder in protein networks, and highlight the potential importance of maintaining similar disorder in the members of a complex.

  2. Transcription factors do it together : the hows and whys of studying protein-protein interactions

    NARCIS (Netherlands)

    Immink, R.G.H.; Angenent, G.C.

    2002-01-01

    Protein–protein interactions are intrinsic to virtually every cellular process. Recent breakthroughs in techniques to study protein-interaction and the availability of fully sequenced plant genomes have attracted many plant scientists to undertake the first steps in the field of protein interactions

  3. Gap junctions and connexin-interacting proteins

    NARCIS (Netherlands)

    Giepmans, Ben N G

    2004-01-01

    Gap junctions form channels between adjacent cells. The core proteins of these channels are the connexins. Regulation of gap junction communication (GJC) can be modulated by connexin-associating proteins, such as regulatory protein phosphatases and protein kinases, of which c-Src is the best-studied

  4. AtPIN: Arabidopsis thaliana Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Silva-Filho Marcio C

    2009-12-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs constitute one of the most crucial conditions to sustain life in living organisms. To study PPI in Arabidopsis thaliana we have developed AtPIN, a database and web interface for searching and building interaction networks based on publicly available protein-protein interaction datasets. Description All interactions were divided into experimentally demonstrated or predicted. The PPIs in the AtPIN database present a cellular compartment classification (C3 which divides the PPI into 4 classes according to its interaction evidence and subcellular localization. It has been shown in the literature that a pair of genuine interacting proteins are generally expected to have a common cellular role and proteins that have common interaction partners have a high chance of sharing a common function. In AtPIN, due to its integrative profile, the reliability index for a reported PPI can be postulated in terms of the proportion of interaction partners that two proteins have in common. For this, we implement the Functional Similarity Weight (FSW calculation for all first level interactions present in AtPIN database. In order to identify target proteins of cytosolic glutamyl-tRNA synthetase (Cyt-gluRS (AT5G26710 we combined two approaches, AtPIN search and yeast two-hybrid screening. Interestingly, the proteins glutamine synthetase (AT5G35630, a disease resistance protein (AT3G50950 and a zinc finger protein (AT5G24930, which has been predicted as target proteins for Cyt-gluRS by AtPIN, were also detected in the experimental screening. Conclusions AtPIN is a friendly and easy-to-use tool that aggregates information on Arabidopsis thaliana PPIs, ontology, and sub-cellular localization, and might be a useful and reliable strategy to map protein-protein interactions in Arabidopsis. AtPIN can be accessed at http://bioinfo.esalq.usp.br/atpin.

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

    NARCIS (Netherlands)

    Somireddy Venkata, Bharat Kumar Reddy

    2012-01-01

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

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

    DEFF Research Database (Denmark)

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

    2017-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  8. Computational approaches for detecting protein complexes from protein interaction networks: a survey

    Directory of Open Access Journals (Sweden)

    Kwoh Chee-Keong

    2010-02-01

    Full Text Available Abstract Background Most proteins form macromolecular complexes to perform their biological functions. However, experimentally determined protein complex data, especially of those involving more than two protein partners, are relatively limited in the current state-of-the-art high-throughput experimental techniques. Nevertheless, many techniques (such as yeast-two-hybrid have enabled systematic screening of pairwise protein-protein interactions en masse. Thus computational approaches for detecting protein complexes from protein interaction data are useful complements to the limited experimental methods. They can be used together with the experimental methods for mapping the interactions of proteins to understand how different proteins are organized into higher-level substructures to perform various cellular functions. Results Given the abundance of pairwise protein interaction data from high-throughput genome-wide experimental screenings, a protein interaction network can be constructed from protein interaction data by considering individual proteins as the nodes, and the existence of a physical interaction between a pair of proteins as a link. This binary protein interaction graph can then be used for detecting protein complexes using graph clustering techniques. In this paper, we review and evaluate the state-of-the-art techniques for computational detection of protein complexes, and discuss some promising research directions in this field. Conclusions Experimental results with yeast protein interaction data show that the interaction subgraphs discovered by various computational methods matched well with actual protein complexes. In addition, the computational approaches have also improved in performance over the years. Further improvements could be achieved if the quality of the underlying protein interaction data can be considered adequately to minimize the undesirable effects from the irrelevant and noisy sources, and the various biological

  9. Phage display library screening for identification of interacting protein partners.

    Science.gov (United States)

    Addepalli, Balasubrahmanyam; Rao, Suryadevara; Hunt, Arthur G

    2015-01-01

    Phage display is a versatile high-throughput screening method employed to understand and improve the chemical biology, be it production of human monoclonal antibodies or identification of interacting protein partners. A majority of cell proteins operate in a concerted fashion either by stable or transient interactions. Such interactions can be mediated by recognition of small amino acid sequence motifs on the protein surface. Phage display can play a crucial role in identification of such motifs. This report describes the use of phage display for the identification of high affinity sequence motifs that could be responsible for interactions with a target (bait) protein.

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

    Science.gov (United States)

    Kandel, Sylvie E; Lampe, Jed N

    2014-09-15

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

  11. Effects of ethanol on the proteasome interacting proteins

    Institute of Scientific and Technical Information of China (English)

    Fawzia; Bardag-Gorce

    2010-01-01

    Proteasome dysfunction has been repeatedly reported in alcoholic liver disease. Ethanol metabolism endproducts affect the structure of the proteasome, and, therefore, change the proteasome interaction with its regulatory complexes 19S and PA28, as well as its interacting proteins. Chronic ethanol feeding alters the ubiquitin-proteasome activity by altering the interaction between the 19S and the 20S proteasome interaction. The degradation of oxidized and damaged proteins is thus decreased and leads to accum...

  12. Measuring protein-protein and protein-nucleic Acid interactions by biolayer interferometry.

    Science.gov (United States)

    Sultana, Azmiri; Lee, Jeffrey E

    2015-01-01

    Biolayer interferometry (BLI) is a simple, optical dip-and-read system useful for measuring interactions between proteins, peptides, nucleic acids, small molecules, and/or lipids in real time. In BLI, a biomolecular bait is immobilized on a matrix at the tip of a fiber-optic sensor. The binding between the immobilized ligand and another molecule in an analyte solution produces a change in optical thickness at the tip and results in a wavelength shift proportional to binding. BLI provides direct binding affinities and rates of association and dissociation. This unit describes an efficient approach using streptavidin-based BLI to analyze DNA-protein and protein-protein interactions. A quantitative set of equilibrium binding affinities (K(d)) and rates of association and dissociation (k(a)/k(d)) can be measured in minutes using nanomole quantities of sample.

  13. Proteins interacting with the 26S proteasome

    DEFF Research Database (Denmark)

    Hartmann-Petersen, R; Gordon, C

    2004-01-01

    The 26S proteasome is the multi-protein protease that recognizes and degrades ubiquitinylated substrates targeted for destruction by the ubiquitin pathway. In addition to the well-documented subunit organization of the 26S holoenzyme, it is clear that a number of other proteins transiently...... associate with the 26S complex. These transiently associated proteins confer a number of different roles such as substrate presentation, cleavage of the multi-ubiquitin chain from the protein substrate and turnover of misfolded proteins. Such activities are essential for the 26S proteasome to efficiently...... fulfill its intracellular function in protein degradation....

  14. Protein interaction network related to Helicobacter pylori infection response

    Institute of Scientific and Technical Information of China (English)

    Kyu Kwang Kim; Han Bok Kim

    2009-01-01

    AIM: To understand the complex reaction of gastric inflammation induced by Helicobacter pylori (H pylori ) in a systematic manner using a protein interaction network. METHODS: The expression of genes significantly changed on microarray during H pylori infection was scanned from the web literary database and translated into proteins. A network of protein interactions was constructed by searching the primary interactions of selected proteins. The constructed network was mathematically analyzed and its biological function was examined. In addition, the nodes on the network were checked to determine if they had any further functional importance or relation to other proteins by extending them.RESULTS: The scale-free network showing the relationship between inflammation and carcinogenesis was constructed. Mathematical analysis showed hub and bottleneck proteins, and these proteins were mostly related to immune response. The network contained pathways and proteins related to H pylori infection, such as the JAK-STAT pathway triggered by interleukins. Activation of nuclear factor (NF)-kB, TLR4, and other proteins known to function as core proteins of immune response were also found.These immune-related proteins interacted on the network with pathways and proteins related to the cell cycle, cell maintenance and proliferation, and transcription regulators such as BRCA1, FOS, REL, and zinc finger proteins. The extension of nodes showed interactions of the immune proteins with cancerrelated proteins. One extended network, the core network, a summarized form of the extended network, and cell pathway model were constructed. CONCLUSION: Immune-related proteins activated by H pylori infection interact with proto-oncogene proteins. The hub and bottleneck proteins are potential drug targets for gastric inflammation and cancer.

  15. Folding superfunnel to describe cooperative folding of interacting proteins.

    Science.gov (United States)

    Smeller, László

    2016-07-01

    This paper proposes a generalization of the well-known folding funnel concept of proteins. In the funnel model the polypeptide chain is treated as an individual object not interacting with other proteins. Since biological systems are considerably crowded, protein-protein interaction is a fundamental feature during the life cycle of proteins. The folding superfunnel proposed here describes the folding process of interacting proteins in various situations. The first example discussed is the folding of the freshly synthesized protein with the aid of chaperones. Another important aspect of protein-protein interactions is the folding of the recently characterized intrinsically disordered proteins, where binding to target proteins plays a crucial role in the completion of the folding process. The third scenario where the folding superfunnel is used is the formation of aggregates from destabilized proteins, which is an important factor in case of several conformational diseases. The folding superfunnel constructed here with the minimal assumption about the interaction potential explains all three cases mentioned above. Proteins 2016; 84:1009-1016. © 2016 Wiley Periodicals, Inc.

  16. Human cytomegalovirus IE2 protein interacts with transcription activating factors

    Institute of Scientific and Technical Information of China (English)

    XU; Jinping(徐进平); YE; Linbai(叶林柏)

    2002-01-01

    The human cytomegalovirus (HCMV) IE86 Cdna was cloned into Pgex-2T and fusion protein GST-IE86 was expressed in E. Coli. SDS-PAGE and Western blot assay indicated that fusion protein GST-IE86 with molecular weight of 92 ku is soluble in the supernatant of cell lysate. Protein GST and fusion protein GST-IE86 were purified by affinity chromatography. The technology of co-separation and specific affinity chromatography was used to study the interactions of HCMV IE86 protein with some transcriptional regulatory proteins and transcriptional factors. The results indicated that IE86 interacts separately with transcriptional factor TFIIB and promoter DNA binding transcription trans-activating factors SP1, AP1 and AP2 to form a heterogenous protein complex. These transcriptional trans-activating factors, transcriptional factor and IE86 protein were adsorbed and retained in the affinity chromatography simultaneously. But IE86 protein could not interact with NF-Кb, suggesting that the function of IE86 protein that can interact with transcriptional factor and transcriptional trans-activating factors has no relevance to protein glycosylation. IE86 protein probably has two domains responsible for binding transcriptional trans-activating regulatory proteins and transcriptional factors respectively, thus activating the transcription of many genes. The interactions accelerated the assembly of the transcriptional initiation complexes.

  17. Reconstituting Protein Interaction Networks Using Parameter-Dependent Domain-Domain Interactions

    Science.gov (United States)

    2013-05-07

    that approximately 80% of eukaryotic proteins and 67% of prokaryotic proteins have multiple domains [13,14]. Most annotation databases characterize...domain annotations, Domain-domain interactions, Protein-protein interaction networks Background The living cell is a dynamic, interconnected system...detailed in Methods. Here, we illustrate its application on a well- annotated single- cell organism. We created a merged set of protein-domain annotations

  18. Preferential interactions and the effect of protein PEGylation

    DEFF Research Database (Denmark)

    Holm, Louise Stenstrup; Thulstrup, Peter Waaben; Kasimova, Marina Robertovna;

    2015-01-01

    BACKGROUND: PEGylation is a strategy used by the pharmaceutical industry to prolong systemic circulation of protein drugs, whereas formulation excipients are used for stabilization of proteins during storage. Here we investigate the role of PEGylation in protein stabilization by formulation...... excipients that preferentially interact with the protein. METHODOLOGY/PRINCIPAL FINDINGS: The model protein hen egg white lysozyme was doubly PEGylated on two lysines with 5 kDa linear PEGs (mPEG-succinimidyl valerate, MW 5000) and studied in the absence and presence of preferentially excluded sucrose...... excipients. This suggests that formulation principles using preferentially interacting excipients are similar for PEGylated and non-PEGylated proteins....

  19. Membrane-mediated interaction between strongly anisotropic protein scaffolds.

    Directory of Open Access Journals (Sweden)

    Yonatan Schweitzer

    2015-02-01

    Full Text Available Specialized proteins serve as scaffolds sculpting strongly curved membranes of intracellular organelles. Effective membrane shaping requires segregation of these proteins into domains and is, therefore, critically dependent on the protein-protein interaction. Interactions mediated by membrane elastic deformations have been extensively analyzed within approximations of large inter-protein distances, small extents of the protein-mediated membrane bending and small deviations of the protein shapes from isotropic spherical segments. At the same time, important classes of the realistic membrane-shaping proteins have strongly elongated shapes with large and highly anisotropic curvature. Here we investigated, computationally, the membrane mediated interaction between proteins or protein oligomers representing membrane scaffolds with strongly anisotropic curvature, and addressed, quantitatively, a specific case of the scaffold geometrical parameters characterizing BAR domains, which are crucial for membrane shaping in endocytosis. In addition to the previously analyzed contributions to the interaction, we considered a repulsive force stemming from the entropy of the scaffold orientation. We computed this interaction to be of the same order of magnitude as the well-known attractive force related to the entropy of membrane undulations. We demonstrated the scaffold shape anisotropy to cause a mutual aligning of the scaffolds and to generate a strong attractive interaction bringing the scaffolds close to each other to equilibrium distances much smaller than the scaffold size. We computed the energy of interaction between scaffolds of a realistic geometry to constitute tens of kBT, which guarantees a robust segregation of the scaffolds into domains.

  20. 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.

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

    DEFF Research Database (Denmark)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2014-05-01

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

  3. 14-3-3 proteins in plant-pathogen interactions.

    Science.gov (United States)

    Lozano-Durán, Rosa; Robatzek, Silke

    2015-05-01

    14-3-3 proteins define a eukaryotic-specific protein family with a general role in signal transduction. Primarily, 14-3-3 proteins act as phosphosensors, binding phosphorylated client proteins and modulating their functions. Since phosphorylation regulates a plethora of different physiological responses in plants, 14-3-3 proteins play roles in multiple signaling pathways, including those controlling metabolism, hormone signaling, cell division, and responses to abiotic and biotic stimuli. Increasing evidence supports a prominent role of 14-3-3 proteins in regulating plant immunity against pathogens at various levels. In this review, potential links between 14-3-3 function and the regulation of plant-pathogen interactions are discussed, with a special focus on the regulation of 14-3-3 proteins in response to pathogen perception, interactions between 14-3-3 proteins and defense-related proteins, and 14-3-3 proteins as targets of pathogen effectors.

  4. Synthetic protein interactions reveal a functional map of the cell

    Science.gov (United States)

    Berry, Lisa K; Ólafsson, Guðjón; Ledesma-Fernández, Elena; Thorpe, Peter H

    2016-01-01

    To understand the function of eukaryotic cells, it is critical to understand the role of protein-protein interactions and protein localization. Currently, we do not know the importance of global protein localization nor do we understand to what extent the cell is permissive for new protein associations – a key requirement for the evolution of new protein functions. To answer this question, we fused every protein in the yeast Saccharomyces cerevisiae with a partner from each of the major cellular compartments and quantitatively assessed the effects upon growth. This analysis reveals that cells have a remarkable and unanticipated tolerance for forced protein associations, even if these associations lead to a proportion of the protein moving compartments within the cell. Furthermore, the interactions that do perturb growth provide a functional map of spatial protein regulation, identifying key regulatory complexes for the normal homeostasis of eukaryotic cells. DOI: http://dx.doi.org/10.7554/eLife.13053.001 PMID:27098839

  5. From networks of protein interactions to networks of functional dependencies

    Directory of Open Access Journals (Sweden)

    Luciani Davide

    2012-05-01

    Full Text Available Abstract Background As protein-protein interactions connect proteins that participate in either the same or different functions, networks of interacting and functionally annotated proteins can be converted into process graphs of inter-dependent function nodes (each node corresponding to interacting proteins with the same functional annotation. However, as proteins have multiple annotations, the process graph is non-redundant, if only proteins participating directly in a given function are included in the related function node. Results Reasoning that topological features (e.g., clusters of highly inter-connected proteins might help approaching structured and non-redundant understanding of molecular function, an algorithm was developed that prioritizes inclusion of proteins into the function nodes that best overlap protein clusters. Specifically, the algorithm identifies function nodes (and their mutual relations, based on the topological analysis of a protein interaction network, which can be related to various biological domains, such as cellular components (e.g., peroxisome and cellular bud or biological processes (e.g., cell budding of the model organism S. cerevisiae. Conclusions The method we have described allows converting a protein interaction network into a non-redundant process graph of inter-dependent function nodes. The examples we have described show that the resulting graph allows researchers to formulate testable hypotheses about dependencies among functions and the underlying mechanisms.

  6. 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

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

    Science.gov (United States)

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

    2015-10-22

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

  8. Detecting reliable non interacting proteins (NIPs) significantly enhancing the computational prediction of protein-protein interactions using machine learning methods.

    Science.gov (United States)

    Srivastava, A; Mazzocco, G; Kel, A; Wyrwicz, L S; Plewczynski, D

    2016-03-01

    Protein-protein interactions (PPIs) play a vital role in most biological processes. Hence their comprehension can promote a better understanding of the mechanisms underlying living systems. However, besides the cost and the time limitation involved in the detection of experimentally validated PPIs, the noise in the data is still an important issue to overcome. In the last decade several in silico PPI prediction methods using both structural and genomic information were developed for this purpose. Here we introduce a unique validation approach aimed to collect reliable non interacting proteins (NIPs). Thereafter the most relevant protein/protein-pair related features were selected. Finally, the prepared dataset was used for PPI classification, leveraging the prediction capabilities of well-established machine learning methods. Our best classification procedure displayed specificity and sensitivity values of 96.33% and 98.02%, respectively, surpassing the prediction capabilities of other methods, including those trained on gold standard datasets. We showed that the PPI/NIP predictive performances can be considerably improved by focusing on data preparation.

  9. Dynamic modularity in protein interaction networks predicts breast cancer outcome

    DEFF Research Database (Denmark)

    Taylor, Ian W; Linding, Rune; Warde-Farley, David

    2009-01-01

    Changes in the biochemical wiring of oncogenic cells drives phenotypic transformations that directly affect disease outcome. Here we examine the dynamic structure of the human protein interaction network (interactome) to determine whether changes in the organization of the interactome can be used...... to predict patient outcome. An analysis of hub proteins identified intermodular hub proteins that are co-expressed with their interacting partners in a tissue-restricted manner and intramodular hub proteins that are co-expressed with their interacting partners in all or most tissues. Substantial differences...

  10. Real-time single-molecule coimmunoprecipitation of weak protein-protein interactions.

    Science.gov (United States)

    Lee, Hong-Won; Ryu, Ji Young; Yoo, Janghyun; Choi, Byungsan; Kim, Kipom; Yoon, Tae-Young

    2013-10-01

    Coimmunoprecipitation (co-IP) analysis is a useful method for studying protein-protein interactions. It currently involves electrophoresis and western blotting, which are not optimized for detecting weak and transient interactions. In this protocol we describe an advanced version of co-IP analysis that uses real-time, single-molecule fluorescence imaging as its detection scheme. Bait proteins are pulled down onto the imaging plane of a total internal reflection (TIR) microscope. With unpurified cells or tissue extracts kept in reaction chambers, we observe single protein-protein interactions between the surface-immobilized bait and the fluorescent protein-labeled prey proteins in real time. Such direct recording provides an improvement of five orders of magnitude in the time resolution of co-IP analysis. With the single-molecule sensitivity and millisecond time resolution, which distinguish our method from other methods for measuring weak protein-protein interactions, it is possible to quantify the interaction kinetics and active fraction of native, unlabeled bait proteins. Real-time single-molecule co-IP analysis, which takes ∼4 h to complete from lysate preparation to kinetic analysis, provides a general avenue for revealing the rich kinetic picture of target protein-protein interactions, and it can be used, for example, to investigate the molecular lesions that drive individual cancers at the level of protein-protein interactions.

  11. 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.

  12. Mitochondrial nucleoid interacting proteins support mitochondrial protein synthesis.

    Science.gov (United States)

    He, J; Cooper, H M; Reyes, A; Di Re, M; Sembongi, H; Litwin, T R; Gao, J; Neuman, K C; Fearnley, I M; Spinazzola, A; Walker, J E; Holt, I J

    2012-07-01

    Mitochondrial ribosomes and translation factors co-purify with mitochondrial nucleoids of human cells, based on affinity protein purification of tagged mitochondrial DNA binding proteins. Among the most frequently identified proteins were ATAD3 and prohibitin, which have been identified previously as nucleoid components, using a variety of methods. Both proteins are demonstrated to be required for mitochondrial protein synthesis in human cultured cells, and the major binding partner of ATAD3 is the mitochondrial ribosome. Altered ATAD3 expression also perturbs mtDNA maintenance and replication. These findings suggest an intimate association between nucleoids and the machinery of protein synthesis in mitochondria. ATAD3 and prohibitin are tightly associated with the mitochondrial membranes and so we propose that they support nucleic acid complexes at the inner membrane of the mitochondrion.

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

    OpenAIRE

    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 le...

  14. Functional Maps of Protein Complexes from Quantitative Genetic Interaction Data

    OpenAIRE

    Sourav Bandyopadhyay; Ryan Kelley; Krogan, Nevan J.; Trey Ideker

    2008-01-01

    Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relat...

  15. Interaction between Protein, Phytate, and Microbial Phytase. In Vitro Studies

    NARCIS (Netherlands)

    Kies, A.K.; Jonge, de L.H.; Kemme, P.A.; Jongbloed, A.W.

    2006-01-01

    The interaction between protein and phytate was investigated in vitro using proteins extracted from five common feedstuffs and from casein. The appearance of naturally present soluble protein-phytate complexes in the feedstuffs, the formation of complexes at different pHs, and the degradation of the

  16. 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 interacti

  17. 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 netwo

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

    Science.gov (United States)

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

    2011-06-01

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

  19. 14-3-3 proteins interact with specific MEK kinases.

    Science.gov (United States)

    Fanger, G R; Widmann, C; Porter, A C; Sather, S; Johnson, G L; Vaillancourt, R R

    1998-02-06

    MEK (mitogen-activated protein kinase/extracellular signal-regulated kinase kinase) kinases (MEKKs) regulate c-Jun N-terminal kinase and extracellular response kinase pathways. The 14-3-3zeta and 14-3-3epsilon isoforms were isolated in a two-hybrid screen for proteins interacting with the N-terminal regulatory domain of MEKK3. 14-3-3 proteins bound both the N-terminal regulatory and C-terminal kinase domains of MEKK3. The binding affinity of 14-3-3 for the MEKK3 N terminus was 90 nM, demonstrating a high affinity interaction. 14-3-3 proteins also interacted with MEKK1 and MEKK2, but not MEKK4. Endogenous 14-3-3 protein and MEKK1 and MEKK2 were similarly distributed in the cell, consistent with their in vitro interactions. MEKK1 and 14-3-3 proteins colocalized using two-color digital confocal immunofluorescence. Binding of 14-3-3 proteins mapped to the N-terminal 393 residues of 196-kDa MEKK1. Unlike MEKK2 and MEKK3, the C-terminal kinase domain of MEKK1 demonstrated little or no ability to interact with 14-3-3 proteins. MEKK1, but not MEKK2, -3 or -4, is a caspase-3 substrate that when cleaved releases the kinase domain from the N-terminal regulatory domain. Functionally, caspase-3 cleavage of MEKK1 releases the kinase domain from the N-terminal 14-3-3-binding region, demonstrating that caspases can selectively alter protein kinase interactions with regulatory proteins. With regard to MEKK1, -2 and -3, 14-3-3 proteins do not appear to directly influence activity, but rather function as "scaffolds" for protein-protein interactions.

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

    Directory of Open Access Journals (Sweden)

    Kyunghyun Park

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

  1. Protein-flavour interactions in relation to development of novel protein foods

    NARCIS (Netherlands)

    Heng, L.; Koningsveld, van G.A.; Gruppen, H.; Boekel, van M.A.J.S.; Vincken, J.P.; Roozen, J.P.; Voragen, A.G.J.

    2004-01-01

    Proteins are known to interact with relatively small molecules such as flavour compounds and saponins, and may thus influence the taste perception of food. In this study, the interactions of flavour volatiles with pea proteins, and the effects of heat on these interactions were investigated. The pre

  2. Casein - whey protein interactions in heated milk

    NARCIS (Netherlands)

    Vasbinder, Astrid Jolanda

    2003-01-01

    Heating of milk is an essential step in the processing of various dairy products, like for example yoghurt. A major consequence of the heat treatment is the denaturation of whey proteins, which either associate with the casein micelle or form soluble whey protein aggregates. By combination of enzyma

  3. RNA-protein interactions: an overview

    DEFF Research Database (Denmark)

    Re, Angela; Joshi, Tejal; Kulberkyte, Eleonora;

    2014-01-01

    RNA binding proteins (RBPs) are key players in the regulation of gene expression. In this chapter we discuss the main protein-RNA recognition modes used by RBPs in order to regulate multiple steps of RNA processing. We discuss traditional and state-of-the-art technologies that can be used to stud...

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

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, C; Zemla, A

    2009-02-25

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

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

    Directory of Open Access Journals (Sweden)

    Xia Li

    2010-06-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  7. Rosetta stone method for detecting protein function and protein-protein interactions from genome sequences

    Science.gov (United States)

    Eisenberg, David; Marcotte, Edward M.; Pellegrini, Matteo; Thompson, Michael J.; Yeates, Todd O.

    2002-10-15

    A computational method system, and computer program are provided for inferring functional links from genome sequences. One method is based on the observation that some pairs of proteins A' and B' have homologs in another organism fused into a single protein chain AB. A trans-genome comparison of sequences can reveal these AB sequences, which are Rosetta Stone sequences because they decipher an interaction between A' and B. Another method compares the genomic sequence of two or more organisms to create a phylogenetic profile for each protein indicating its presence or absence across all the genomes. The profile provides information regarding functional links between different families of proteins. In yet another method a combination of the above two methods is used to predict functional links.

  8. Screening for protein-DNA interactions by automatable DNA-protein interaction ELISA.

    Directory of Open Access Journals (Sweden)

    Luise H Brand

    Full Text Available DNA-binding proteins (DBPs, such as transcription factors, constitute about 10% of the protein-coding genes in eukaryotic genomes and play pivotal roles in the regulation of chromatin structure and gene expression by binding to short stretches of DNA. Despite their number and importance, only for a minor portion of DBPs the binding sequence had been disclosed. Methods that allow the de novo identification of DNA-binding motifs of known DBPs, such as protein binding microarray technology or SELEX, are not yet suited for high-throughput and automation. To close this gap, we report an automatable DNA-protein-interaction (DPI-ELISA screen of an optimized double-stranded DNA (dsDNA probe library that allows the high-throughput identification of hexanucleotide DNA-binding motifs. In contrast to other methods, this DPI-ELISA screen can be performed manually or with standard laboratory automation. Furthermore, output evaluation does not require extensive computational analysis to derive a binding consensus. We could show that the DPI-ELISA screen disclosed the full spectrum of binding preferences for a given DBP. As an example, AtWRKY11 was used to demonstrate that the automated DPI-ELISA screen revealed the entire range of in vitro binding preferences. In addition, protein extracts of AtbZIP63 and the DNA-binding domain of AtWRKY33 were analyzed, which led to a refinement of their known DNA-binding consensi. Finally, we performed a DPI-ELISA screen to disclose the DNA-binding consensus of a yet uncharacterized putative DBP, AtTIFY1. A palindromic TGATCA-consensus was uncovered and we could show that the GATC-core is compulsory for AtTIFY1 binding. This specific interaction between AtTIFY1 and its DNA-binding motif was confirmed by in vivo plant one-hybrid assays in protoplasts. Thus, the value and applicability of the DPI-ELISA screen for de novo binding site identification of DBPs, also under automatized conditions, is a promising approach for a

  9. 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.

  10. 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.

  11. Finding finer functions for partially characterized proteins by protein-protein interaction networks

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins' novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.

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

    Science.gov (United States)

    2016-07-01

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

  13. Protein-surface interaction maps for ion-exchange chromatography.

    Science.gov (United States)

    Freed, Alexander S; Cramer, Steven M

    2011-04-05

    In this paper, protein-surface interaction maps were generated by performing coarse-grained protein-surface calculations. This approach allowed for the rapid determination of the protein-surface interaction energies at a range of orientations and distances. Interaction maps of lysozyme indicated that there was a contiguous series of orientations corresponding to several adjacent preferred binding regions on the protein surface. Examination of these orientations provided insight into the residues involved in surface interactions, which qualitatively agreed with the retention data for single-site mutants. Interaction maps of lysozyme single-site mutants were also generated and provided significant insight into why these variants exhibited significant differences in their chromatographic behavior. This approach was also employed to study the binding behavior of CspB and related mutants. The results indicated that, in addition to describing general trends in the data, these maps provided significant insight into retention data of the single-site mutants. In particular, subtle retention trends observed with the K12 and K13 mutants were well-described using this interaction map approach. Finally, the number of interaction points with energies stronger than -2 kcal/mol was shown to be able to semi-quantitatively predict the behavior of most of the mutants. This rapid approach for calculating protein-surface interaction maps is expected to facilitate future method development for separating closely related protein variants in ion-exchange systems.

  14. 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

  15. Understanding protein–protein interactions by genetic suppression

    Indian Academy of Sciences (India)

    Sitaraman Sujatha; Dipankar Chatterji

    2000-01-01

    Protein–protein interactions influence many cellular processes and it is increasingly being felt that even a weak and remote interplay between two subunits of a protein or between two proteins in a complex may govern the fate of a particular biochemical pathway. In a bacterial system where the complete genome sequence is available, it is an arduous task to assign function to a large number of proteins. It is possible that many of them are peripherally associated with a cellular event and it is very difficult to probe such interaction. However, mutations in the genes that encode such proteins (primary mutations) are useful in these studies. Isolation of a suppressor or a second-site mutation that restores the phenotype abolished by the primary mutation could be an elegant yet simple way to follow a set of interacting proteins. Such a reversion site need not necessarily be geometrically close to the primary mutation site.

  16. 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...... to a common surface on PCNA; hence these interactions need to be tightly regulated and coordinated to ensure proper chromosome replication and integrity. Control of PCNA-protein interactions is multilayered and involves post-translational modifications, in particular ubiquitylation, accessory factors...... and regulated degradation of PCNA-associated proteins. This regulatory framework allows cells to maintain a fine-tuned balance between replication fidelity and processivity in response to DNA damage....

  17. Versatile screening for binary protein-protein interactions by yeast two-hybrid mating

    NARCIS (Netherlands)

    Letteboer, S.J.F.; Roepman, R.

    2008-01-01

    Identification of binary protein-protein interactions is a crucial step in determining the molecular context and functional pathways of proteins. State-of-the-art proteomics techniques provide high-throughput information on the content of proteomes and protein complexes, but give little information

  18. Protein-protein interaction domains of Bacillus subtilis DivIVA

    NARCIS (Netherlands)

    S. van Baarle; I.N. Celik; K.G. Kaval; M. Bramkamp; L.W. Hamoen; S. Halbedel

    2012-01-01

    DivIVA proteins are curvature sensitive membrane binding proteins that recruit other proteins to the poles and the division septum. They consist of a conserved N-terminal lipid binding domain fused to a less conserved C-terminal domain. DivIVA homologues interact with different proteins involved in

  19. Protein-protein interaction domains of Bacillus subtilis DivIVA

    NARCIS (Netherlands)

    van Baarle, S.; Celik, I.N.; Kaval, K.G.; Bramkamp, M.; Hamoen, L.W.; Halbedel, S.

    2013-01-01

    DivIVA proteins are curvature sensitive membrane binding proteins that recruit other proteins to the poles and the division septum. They consist of a conserved N-terminal lipid binding domain fused to a less conserved C-terminal domain. DivIVA homologues interact with different proteins involved in

  20. Protein Interactions Investigated by the Raman Spectroscopy for Biosensor Applications

    Directory of Open Access Journals (Sweden)

    R. P. Kengne-Momo

    2012-01-01

    Full Text Available Interaction and surface binding characteristics of staphylococcal protein A (SpA and an anti-Escherichia coli immunoglobulin G (IgG were studied using the Raman spectroscopy. The tyrosine amino acid residues present in the α-helix structure of SpA were found to be involved in interaction with IgG. In bulk interaction condition the native structure of proteins was almost preserved where interaction-related changes were observed in the overall secondary structure (α-helix of SpA. In the adsorbed state, the protein structure was largely modified, which allowed the identification of tyrosine amino acids involved in SpA and IgG interaction. This study constitutes a direct Raman spectroscopic investigation of SpA and IgG (receptor-antibody interaction mechanism in the goal of a future biosensor application for detection of pathogenic microorganisms.

  1. A protein interaction map of the kalimantacin biosynthesis assembly line

    Directory of Open Access Journals (Sweden)

    Birgit Uytterhoeven

    2016-11-01

    Full Text Available The antimicrobial secondary metabolite kalimantacin is produced by a hybrid polyketide/ non-ribosomal peptide system in Pseudomonas fluorescens BCCM_ID9359. In this study, the kalimantacin biosynthesis gene cluster is analyzed by yeast two-hybrid analysis, creating a protein-protein interaction map of the entire assembly line. In total, 28 potential interactions were identified, of which 13 could be confirmed further. These interactions include the dimerization of ketosynthase domains, a link between assembly line modules 9 and 10, and a specific interaction between the trans-acting enoyl reductase BatK and the carrier proteins of modules 8 and 10. These interactions reveal fundamental insight into the biosynthesis of secondary metabolites.This study is the first to reveal interactions in a complete biosynthetic pathway. Similar future studies could build a strong basis for engineering strategies in such clusters.

  2. Metabotropic Glutamate Receptors and Interacting Proteins in Epileptogenesis.

    Science.gov (United States)

    Qian, Feng; Tang, Feng-Ru

    2016-01-01

    Neurotransmitter and receptor systems are involved in different neurological and neuropsychological disorders such as Parkinson's disease, depression, Alzheimer's disease and epilepsy. Recent advances in studies of signal transduction pathways or interacting proteins of neurotransmitter receptor systems suggest that different receptor systems may share the common signal transduction pathways or interacting proteins which may be better therapeutic targets for development of drugs to effectively control brain diseases. In this paper, we reviewed metabotropic glutamate receptors (mGluRs) and their related signal transduction pathways or interacting proteins in status epilepticus and temporal lobe epilepsy, and proposed some novel therapeutical drug targets for controlling epilepsy and epileptogenesis.

  3. Yeast Interacting Proteins Database: YDR446W, YDR510W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YDR446W ECM11 Non-essential protein apparently involved in meiosis, GFP fusion protein is present in discret...description Non-essential protein apparently involved in meiosis, GFP fusion protein is present in discrete

  4. Yeast Interacting Proteins Database: YHR111W, YIL008W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YHR111W UBA4 Protein that activates Urm1p before its conjugation to proteins (urmyl...description Protein that activates Urm1p before its conjugation to proteins (urmylation); one target is the

  5. PDZ domain-mediated interactions of G protein-coupled receptors with postsynaptic density protein 95

    DEFF Research Database (Denmark)

    Møller, Thor C; Wirth, Volker F; Roberts, Nina Ingerslev;

    2013-01-01

    G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome. Their signaling is regulated by scaffold proteins containing PDZ domains, but although these interactions are important for GPCR function, they are still poorly understood. We here present...... with colocalization of the full-length proteins in cells and with previous studies, we suggest that the range of relevant interactions might extend to interactions with K i = 450 µM in the in vitro assays. Within this range, we identify novel PSD-95 interactions with the chemokine receptor CXCR2, the neuropeptide Y...

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

    Science.gov (United States)

    Klein, Mark A

    2014-08-14

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

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

    Institute of Scientific and Technical Information of China (English)

    LIU Xiang; WANG Yifei

    2007-01-01

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

  8. Integrating protein-protein interactions and text mining for protein function prediction

    Directory of Open Access Journals (Sweden)

    Leser Ulf

    2008-07-01

    Full Text Available Abstract Background Functional annotation of proteins remains a challenging task. Currently the scientific literature serves as the main source for yet uncurated functional annotations, but curation work is slow and expensive. Automatic techniques that support this work are still lacking reliability. We developed a method to identify conserved protein interaction graphs and to predict missing protein functions from orthologs in these graphs. To enhance the precision of the results, we furthermore implemented a procedure that validates all predictions based on findings reported in the literature. Results Using this procedure, more than 80% of the GO annotations for proteins with highly conserved orthologs that are available in UniProtKb/Swiss-Prot could be verified automatically. For a subset of proteins we predicted new GO annotations that were not available in UniProtKb/Swiss-Prot. All predictions were correct (100% precision according to the verifications from a trained curator. Conclusion Our method of integrating CCSs and literature mining is thus a highly reliable approach to predict GO annotations for weakly characterized proteins with orthologs.

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

    Directory of Open Access Journals (Sweden)

    Swapna Lakshmipuram S

    2012-12-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Zencir, Sevil [Department of Biochemistry, Faculty of Science, Ege University, Izmir 35100 (Turkey); Ovee, Mohiuddin [Department of Chemistry and Biochemistry, Auburn University, Auburn, AL 36849 (United States); Dobson, Melanie J. [Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, NS, Canada B3H 4R2 (Canada); Banerjee, Monimoy [Department of Chemistry and Biochemistry, Auburn University, Auburn, AL 36849 (United States); Topcu, Zeki, E-mail: zeki.topcu@ege.edu.tr [Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, Ege University, Izmir 35100 (Turkey); Mohanty, Smita, E-mail: mohansm@auburn.edu [Department of Chemistry and Biochemistry, Auburn University, Auburn, AL 36849 (United States)

    2011-08-12

    Highlights: {yields} Brain-specific angiogenesis inhibitor 2 (BAI2) is a new partner protein for GIP. {yields} BAI2 interaction with GIP was revealed by yeast two-hybrid assay. {yields} Binding of BAI2 to GIP was characterized by NMR, CD and fluorescence. {yields} 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, {beta}-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.

  11. Metabotropic glutamate receptors and interacting proteins: evolving drug targets.

    Science.gov (United States)

    Enz, Ralf

    2012-01-01

    The correct targeting, localization, regulation and signaling of metabotropic glutamate receptors (mGluRs) represent major mechanisms underlying the complex function of neuronal networks. These tasks are accomplished by the formation of synaptic signal complexes that integrate functionally related proteins such as neurotransmitter receptors, enzymes and scaffold proteins. By these means, proteins interacting with mGluRs are important regulators of glutamatergic neurotransmission. Most described mGluR interaction partners bind to the intracellular C-termini of the receptors. These domains are extensively spliced and phosphorylated, resulting in a high variability of binding surfaces offered to interacting proteins. Malfunction of mGluRs and associated proteins are linked to neurodegenerative and neuropsychiatric disorders including addiction, depression, epilepsy, schizophrenia, Alzheimer's, Huntington's and Parkinson's disease. MGluR associated signal complexes are dynamic structures that assemble and disassemble in response to the neuronal fate. This, in principle, allows therapeutic intervention, defining mGluRs and interacting proteins as promising drug targets. In the last years, several studies elucidated the geometry of mGluRs in contact with regulatory proteins, providing a solid fundament for the development of new therapeutic strategies. Here, I will give an overview of human disorders directly associated with mGluR malfunction, provide an up-to-date summary of mGluR interacting proteins and highlight recently described structures of mGluR domains in contact with binding partners.

  12. Membrane interaction of retroviral Gag proteins

    Directory of Open Access Journals (Sweden)

    Robert Alfred Dick

    2014-04-01

    Full Text Available Assembly of an infectious retroviral particle relies on multimerization of the Gag polyprotein at the inner leaflet of the plasma membrane. The three domains of Gag common to all retroviruses-- MA, CA, and NC-- provide the signals for membrane binding, assembly, and viral RNA packaging, respectively. These signals do not function independently of one another. For example, Gag multimerization enhances membrane binding and is more efficient when NC is interacting with RNA. MA binding to the plasma membrane is governed by several principles, including electrostatics, recognition of specific lipid head groups, hydrophobic interactions, and membrane order. HIV-1 uses many of these principles while Rous sarcoma virus (RSV appears to use fewer. This review describes the principles that govern Gag interactions with membranes, focusing on RSV and HIV-1 Gag. The review also defines lipid and membrane behavior, and discusses the complexities in determining how lipid and membrane behavior impact Gag membrane binding.

  13. 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.

  14. Protein solvent and weak protein protein interactions in halophilic malate dehydrogenase

    Science.gov (United States)

    Ebel, Christine; Faou, Pierre; Zaccai, Giuseppe

    1999-01-01

    With the aim to correlate the solvation, stability and solubility properties of halophilic malate dehydrogenase, we characterized its weak interparticle interactions by small-angle neutron scattering in various solvents. The protein concentration dependence of the apparent radius of gyration and forward scattered intensity extrapolated from Guinier plots, and thus the second virial coefficient, A2, were determined for each solvent condition. In NaCl 1M+2-methylpentane-2,4-diol 30%, a solvent that promotes protein crystallization, A2 is negative, -0.4×10 -4 ml mol g -2 and indicating attractive interactions; in ammonium sulfate 3M, in which the protein precipitates at high concentrations, A2˜0. In 2-5M NaCl, 1-3.5M NaOAc, 1-4.5M KF or 1-2M (NH 4) 2SO 4, in which the protein is very soluble, A2 is positive with a value of the order of 0.4×10 -4 ml mol g -2 which decreases with increasing salt concentration. In MgCl 2 however, A2 increases with increasing salt concentration from 0.2 to 1.3M.

  15. Bilayer-thickness-mediated interactions between integral membrane proteins.

    Science.gov (United States)

    Kahraman, Osman; Koch, Peter D; Klug, William S; Haselwandter, Christoph A

    2016-04-01

    Hydrophobic thickness mismatch between integral membrane proteins and the surrounding lipid bilayer can produce lipid bilayer thickness deformations. Experiment and theory have shown that protein-induced lipid bilayer thickness deformations can yield energetically favorable bilayer-mediated interactions between integral membrane proteins, and large-scale organization of integral membrane proteins into protein clusters in cell membranes. Within the continuum elasticity theory of membranes, the energy cost of protein-induced bilayer thickness deformations can be captured by considering compression and expansion of the bilayer hydrophobic core, membrane tension, and bilayer bending, resulting in biharmonic equilibrium equations describing the shape of lipid bilayers for a given set of bilayer-protein boundary conditions. Here we develop a combined analytic and numerical methodology for the solution of the equilibrium elastic equations associated with protein-induced lipid bilayer deformations. Our methodology allows accurate prediction of thickness-mediated protein interactions for arbitrary protein symmetries at arbitrary protein separations and relative orientations. We provide exact analytic solutions for cylindrical integral membrane proteins with constant and varying hydrophobic thickness, and develop perturbative analytic solutions for noncylindrical protein shapes. We complement these analytic solutions, and assess their accuracy, by developing both finite element and finite difference numerical solution schemes. We provide error estimates of our numerical solution schemes and systematically assess their convergence properties. Taken together, the work presented here puts into place an analytic and numerical framework which allows calculation of bilayer-mediated elastic interactions between integral membrane proteins for the complicated protein shapes suggested by structural biology and at the small protein separations most relevant for the crowded membrane

  16. Protein-protein interactions between proteins of Citrus tristeza virus isolates.

    Science.gov (United States)

    Nchongboh, Chofong Gilbert; Wu, Guan-Wei; Hong, Ni; Wang, Guo-Ping

    2014-12-01

    Citrus tristeza virus (CTV) is one of the most devastating pathogens of citrus. Its genome is organized into 12 open reading frames (ORFs), of which ten ORFs located at the 3'-terminus of the genome have multiple biological functions. The ten genes at the 3'-terminus of the genome of a severe isolate (CTV-S4) and three ORFs (CP, CPm and p20) of three other isolates (N4, S45 and HB1) were cloned into pGBKT7 and pGADT7 yeast shuttle vectors. Yeast two-hybridization (Y2H) assays results revealed a strong self-interaction for CP and p20, and a unique interaction between the CPm of CTV-S4 (severe) and CP of CTV-N4 (mild) isolates. Bimolecular fluorescence complementation also confirmed these interactions. Analysis of the deletion mutants delineated the domains of CP and p20 self-interaction. Furthermore, the domains responsible for CP and p20 self-interactions were mapped at the CP amino acids sites 41-84 and p20 amino acids sites 1-21 by Y2H. This study provided new information on CTV protein interactions which will help for further understanding the biological functions.

  17. Scalable prediction of compound-protein interactions using minwise hashing.

    Science.gov (United States)

    Tabei, Yasuo; Yamanishi, Yoshihiro

    2013-01-01

    The identification of compound-protein interactions plays key roles in the drug development toward discovery of new drug leads and new therapeutic protein targets. There is therefore a strong incentive to develop new efficient methods for predicting compound-protein interactions on a genome-wide scale. In this paper we develop a novel chemogenomic method to make a scalable prediction of compound-protein interactions from heterogeneous biological data using minwise hashing. The proposed method mainly consists of two steps: 1) construction of new compact fingerprints for compound-protein pairs by an improved minwise hashing algorithm, and 2) application of a sparsity-induced classifier to the compact fingerprints. We test the proposed method on its ability to make a large-scale prediction of compound-protein interactions from compound substructure fingerprints and protein domain fingerprints, and show superior performance of the proposed method compared with the previous chemogenomic methods in terms of prediction accuracy, computational efficiency, and interpretability of the predictive model. All the previously developed methods are not computationally feasible for the full dataset consisting of about 200 millions of compound-protein pairs. The proposed method is expected to be useful for virtual screening of a huge number of compounds against many protein targets.

  18. 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.

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

    Directory of Open Access Journals (Sweden)

    Sylvain Lacomble

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

  20. Water-mediated ionic interactions in protein structures

    Indian Academy of Sciences (India)

    R Sabarinathan; K Aishwarya; R Sarani; M Kirti Vaishnavi; K Sekar

    2011-06-01

    It is well known that water molecules play an indispensable role in the structure and function of biological macromolecules. The water-mediated ionic interactions between the charged residues provide stability and plasticity and in turn address the function of the protein structures. Thus, this study specifically addresses the number of possible water-mediated ionic interactions, their occurrence, distribution and nature found in 90% non-redundant protein chains. Further, it provides a statistical report of different charged residue pairs that are mediated by surface or buried water molecules to form the interactions. Also, it discusses its contributions in stabilizing various secondary structural elements of the protein. Thus, the present study shows the ubiquitous nature of the interactions that imparts plasticity and flexibility to a protein molecule.

  1. Designing specificity of protein-substrate interactions

    NARCIS (Netherlands)

    Coluzza, I.; Frenkel, D.

    2004-01-01

    One of the key properties of biological molecules is that they can bind strongly to certain substrates yet interact only weakly with the very large number of other molecules that they encounter. Using a simple lattice model, we test several methods to design molecule-substrate binding specificity. W

  2. Inferring High-Confidence Human Protein-Protein Interactions

    Science.gov (United States)

    2012-01-01

    Similarly, the top-ranked interaction between L-threonine dehydrogenase ( TDH ) and aminoacetone synthetase (alias of GCAT) catalyzes the conversion of L...acetyltransferase TDH 2 L-threonine dehydrogenase 2 577.4 11.0 1328.0 CXCL16 4 Inducible T cell co-stimulator CXCR6 4 Inducible T cell co-stimulator

  3. Neurodegenerative diseases: quantitative predictions of protein-RNA interactions.

    Science.gov (United States)

    Cirillo, Davide; Agostini, Federico; Klus, Petr; Marchese, Domenica; Rodriguez, Silvia; Bolognesi, Benedetta; Tartaglia, Gian Gaetano

    2013-02-01

    Increasing evidence indicates that RNA plays an active role in a number of neurodegenerative diseases. We recently introduced a theoretical framework, catRAPID, to predict the binding ability of protein and RNA molecules. Here, we use catRAPID to investigate ribonucleoprotein interactions linked to inherited intellectual disability, amyotrophic lateral sclerosis, Creutzfeuld-Jakob, Alzheimer's, and Parkinson's diseases. We specifically focus on (1) RNA interactions with fragile X mental retardation protein FMRP; (2) protein sequestration caused by CGG repeats; (3) noncoding transcripts regulated by TAR DNA-binding protein 43 TDP-43; (4) autogenous regulation of TDP-43 and FMRP; (5) iron-mediated expression of amyloid precursor protein APP and α-synuclein; (6) interactions between prions and RNA aptamers. Our results are in striking agreement with experimental evidence and provide new insights in processes associated with neuronal function and misfunction.

  4. Deducing topology of protein-protein interaction networks from experimentally measured sub-networks

    Directory of Open Access Journals (Sweden)

    MacLellan W Robb

    2008-07-01

    Full Text Available Abstract Background Protein-protein interaction networks are commonly sampled using yeast two hybrid approaches. However, whether topological information reaped from these experimentally-measured sub-networks can be extrapolated to complete protein-protein interaction networks is unclear. Results By analyzing various experimental protein-protein interaction datasets, we found that they are not random samples of the parent networks. Based on the experimental bait-prey behaviors, our computer simulations show that these non-random sampling features may affect the topological information. We tested the hypothesis that a core sub-network exists within the experimentally sampled network that better maintains the topological characteristics of the parent protein-protein interaction network. We developed a method to filter the experimentally sampled network to result in a core sub-network that more accurately reflects the topology of the parent network. These findings have fundamental implications for large-scale protein interaction studies and for our understanding of the behavior of cellular networks. Conclusion The topological information from experimental measured networks network as is may not be the correct source for topological information about the parent protein-protein interaction network. We define a core sub-network that more accurately reflects the topology of the parent network.

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

    Science.gov (United States)

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

    2013-01-01

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

  6. 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. .

  7. Proteomic Analyses of NF1-Interacting Proteins in Keratinocytes

    Science.gov (United States)

    2015-04-01

    AWARD NUMBER: W81XWH-14-1-0070 TITLE: Proteomic Analyses of NF1 -Interacting Proteins in Keratinocytes PRINCIPAL INVESTIGATOR: Shyni Varghese...TITLE AND SUBTITLE 5a.CONTRACT NUMBER Proteomic Analyses of NF1 -Interacting Proteins in Keratinocytes 5b. GRANT NUMBER W81XWH-14-1-0070 5c. PROGRAM...in the NF1 null epidermis, we analyzed NF1 expression in a mouse model of psoriasis (imiquimod-induced psoriasis-like skin inflammation) and

  8. Transient interactions studied by NMR : iron sulfur proteins and their interaction partners

    NARCIS (Netherlands)

    Xu, Xingfu

    2009-01-01

    The interactions between proteins are of central importance for virtually every process in a living cell. It has long been a mystery how two proteins associate to form a complex in a complicated cellular context. Recently, it was found that an intermediate state called encounter state, of a protein

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

    Science.gov (United States)

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

    2013-01-01

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

  10. 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.

  11. Quantification of protein interaction kinetics in a micro droplet

    Science.gov (United States)

    Yin, L. L.; Wang, S. P.; Shan, X. N.; Zhang, S. T.; Tao, N. J.

    2015-11-01

    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.

  12. Measurements of Protein-Protein Interactions by Size Exclusion Chromatography

    OpenAIRE

    Bloustine, J.; Berejnov, V.; Fraden, S

    2003-01-01

    A method is presented for determining second virial coefficients (B2) of protein solutions from retention time measurements in size exclusion chromatography. We determine B2 by analyzing the concentration dependence of the chromatographic partition coefficient. We show the ability of this method to track the evolution of B2 from positive to negative values in lysozyme and bovine serum albumin solutions. Our size exclusion chromatography results agree quantitatively with data obtained by light...

  13. 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.

  14. Imaging mRNA and protein interactions within neurons

    Science.gov (United States)

    Eliscovich, Carolina; Shenoy, Shailesh M.

    2017-01-01

    RNA–protein interactions are essential for proper gene expression regulation, particularly in neurons with unique spatial constraints. Currently, these interactions are defined biochemically, but a method is needed to evaluate them quantitatively within morphological context. Colocalization of two-color labels using wide-field microscopy is a method to infer these interactions. However, because of chromatic aberrations in the objective lens, this approach lacks the resolution to determine whether two molecules are physically in contact or simply nearby by chance. Here, we developed a robust super registration methodology that corrected the chromatic aberration across the entire image field to within 10 nm, which is capable of determining whether two molecules are physically interacting or simply in proximity by random chance. We applied this approach to image single-molecule FISH in combination with immunofluorescence (smFISH-IF) and determined whether the association between an mRNA and binding protein(s) within a neuron was significant or accidental. We evaluated several mRNA-binding proteins identified from RNA pulldown assays to determine which of these exhibit bona fide interactions. Surprisingly, many known mRNA-binding proteins did not bind the mRNA in situ, indicating that adventitious interactions are significant using existing technology. This method provides an ability to evaluate two-color registration compatible with the scale of molecular interactions. PMID:28223507

  15. Molecular imprinting of protein by coordinate interaction

    Institute of Scientific and Technical Information of China (English)

    Jun Wang; Zhen Dong Hua; Zhi Yong Chen; Yuan Zong Li; Mei Ping Zhao

    2009-01-01

    In this article, a novel strong interaction by forming complex between bovine serum albumin (BSA) and copper ion was utilized for the preparation of molecular imprinted hydrogel in aqueous solution. Results show that the inclusion of copper ion in preparation can bridge the template BSA and functional monomers together and improve the imprinting effect compared to the polymer made without copper ion added. High selectivity factor and large adsorption capacity are also observed for the obtained BSA-imprinted hydrogel.

  16. Mutant analysis, protein-protein interactions and subcellular localization of the Arabidopsis B sister (ABS) protein.

    Science.gov (United States)

    Kaufmann, Kerstin; Anfang, Nicole; Saedler, Heinz; Theissen, Günter

    2005-09-01

    Recently, close relatives of class B floral homeotic genes, termed B(sister) genes, have been identified in both angiosperms and gymnosperms. In contrast to the B genes themselves, B(sister) genes are exclusively expressed in female reproductive organs, especially in the envelopes or integuments surrounding the ovules. This suggests an important ancient function in ovule or seed development for B(sister) genes, which has been conserved for about 300 million years. However, investigation of the first loss-of-function mutant for a B(sister) gene (ABS/TT16 from Arabidopsis) revealed only a weak phenotype affecting endothelium formation. Here, we present an analysis of two additional mutant alleles, which corroborates this weak phenotype. Transgenic plants that ectopically express ABS show changes in the growth and identity of floral organs, suggesting that ABS can interact with floral homeotic proteins. Yeast-two-hybrid and three-hybrid analyses indicated that ABS can form dimers with SEPALLATA (SEP) floral homeotic proteins and multimeric complexes that also include the AGAMOUS-like proteins SEEDSTICK (STK) or SHATTERPROOF1/2 (SHP1, SHP2). These data suggest that the formation of multimeric transcription factor complexes might be a general phenomenon among MIKC-type MADS-domain proteins in angiosperms. Heterodimerization of ABS with SEP3 was confirmed by gel retardation assays. Fusion proteins tagged with CFP (Cyan Fluorescent Protein) and YFP (Yellow Fluorescent Protein) in Arabidopsis protoplasts showed that ABS is localized in the nucleus. Phylogenetic analysis revealed the presence of a structurally deviant, but closely related, paralogue of ABS in the Arabidopsis genome. Thus the evolutionary developmental genetics of B(sister) genes can probably only be understood as part of a complex and redundant gene network that may govern ovule formation in a conserved manner, which has yet to be fully explored.

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

    Science.gov (United States)

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

    2004-11-01

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

  18. Yeast Interacting Proteins Database: YLR291C, YPL070W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YPL070W MUK1 Cytoplasmic protein of unknown function containing a Vps9 domain; computation...rotein of unknown function containing a Vps9 domain; computational analysis of large-scale protein-protein i

  19. Yeast Interacting Proteins Database: YIL008W, YHR111W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ait as prey (1) YHR111W UBA4 Protein that activates Urm1p before its conjugation ...4 Prey description Protein that activates Urm1p before its conjugation to proteins (urmylation); one target

  20. 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

  1. Rare disease relations through common genes and protein interactions.

    Science.gov (United States)

    Fernandez-Novo, Sara; Pazos, Florencio; Chagoyen, Monica

    2016-06-01

    ODCs (Orphan Disease Connections), available at http://csbg.cnb.csic.es/odcs, is a novel resource to explore potential molecular relations between rare diseases. These molecular relations have been established through the integration of disease susceptibility genes and human protein-protein interactions. The database currently contains 54,941 relations between 3032 diseases.

  2. Interaction of maize chromatin-associated HMG proteins with mononucleosomes

    DEFF Research Database (Denmark)

    Lichota, J.; Grasser, Klaus D.

    2003-01-01

    maize HMGA and five different HMGB proteins with mononucleosomes (containing approx. 165 bp of DNA) purified from micrococcal nuclease-digested maize chromatin. The HMGB proteins interacted with the nucleosomes independent of the presence of the linker histone H1, while the binding of HMGA...

  3. Physical and chemical interactions in cold gelation of food proteins

    NARCIS (Netherlands)

    Alting, A.C.; Jongh, de H.H.J.; Visschers, R.W.; Simons, J.W.F.A.

    2002-01-01

    pH-Induced cold gelation of whey proteins is a two-step process. After protein aggregates have been prepared by heat treatment, gelation is established at ambient temperature by gradually lowering the pH. To demonstrate the importance of electrostatic interactions between aggregates during this latt

  4. Use and application of hydrophobic interaction chromatography for protein purification.

    Science.gov (United States)

    McCue, Justin T

    2014-01-01

    The objective of this section is to provide the reader with guidelines and background on the use and experimental application of Hydrophobic Interaction chromatography (HIC) for the purification of proteins. The section will give step by step instructions on how to use HIC in the laboratory to purify proteins. General guidelines and relevant background information is also provided.

  5. Identification of proteins interacting with Arabidopsis ACD11

    DEFF Research Database (Denmark)

    Petersen, Nikolaj H T; Joensen, Jan; McKinney, Lea V;

    2009-01-01

    in a yeast two-hybrid screen of an Arabidopsis cDNA library to identify ACD11 interacting proteins. One interactor identified is a protein of unknown function with an RNA recognition motif (RRM) designated BPA1 (binding partner of ACD11). Co-immunoprecipitation experiments confirmed the ACD11-BPA1...

  6. Role of connexin43-interacting proteins at gap junctions

    NARCIS (Netherlands)

    Giepmans, Ben N G

    2006-01-01

    Gap junctions are arrays of cell-to-cell channels that allow diffusion of small molecules between neighboring cells. The individual channels are formed by the four-transmembrane connexin (Cx) proteins. Recently, multiple proteins have been found to interact at the cytoplasmic site with the most abun

  7. Understanding Protein Synthesis: An Interactive Card Game Discussion

    Science.gov (United States)

    Lewis, Alison; Peat, Mary; Franklin, Sue

    2005-01-01

    Protein synthesis is a complex process and students find it difficult to understand. This article describes an interactive discussion "game" used by first year biology students at the University of Sydney. The students, in small groups, use the game in which the processes of protein synthesis are actioned by the students during a…

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

    Science.gov (United States)

    Dukare, Sandeep; Klempnauer, Karl-Heinz

    2016-07-01

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

  9. Bilayer-thickness-mediated interactions between integral membrane proteins

    CERN Document Server

    Kahraman, Osman; Klug, William S; Haselwandter, Christoph A

    2016-01-01

    Hydrophobic thickness mismatch between integral membrane proteins and the surrounding lipid bilayer can produce lipid bilayer thickness deformations. Experiment and theory have shown that protein-induced lipid bilayer thickness deformations can yield energetically favorable bilayer-mediated interactions between integral membrane proteins, and large-scale organization of integral membrane proteins into protein clusters in cell membranes. Within the continuum elasticity theory of membranes, the energy cost of protein-induced bilayer thickness deformations can be captured by considering compression and expansion of the bilayer hydrophobic core, membrane tension, and bilayer bending, resulting in biharmonic equilibrium equations describing the shape of lipid bilayers for a given set of bilayer-protein boundary conditions. Here we develop a combined analytic and numerical methodology for the solution of the equilibrium elastic equations associated with protein-induced lipid bilayer deformations. Our methodology al...

  10. 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.

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

    Science.gov (United States)

    Luo, Jiawei; Qi, Yi

    2015-01-01

    Background 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. Method 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. Results 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). Conclusions LIDC is more effective for the prediction of essential proteins than other recently developed methods. PMID:26125187

  12. 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.

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

    Directory of Open Access Journals (Sweden)

    Yang Jiong

    2007-09-01

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

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

    Institute of Scientific and Technical Information of China (English)

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

    2013-01-01

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

  15. Genome-wide protein-protein interaction screening by protein-fragment complementation assay (PCA) in living cells.

    Science.gov (United States)

    Rochette, Samuel; Diss, Guillaume; Filteau, Marie; Leducq, Jean-Baptiste; Dubé, Alexandre K; Landry, Christian R

    2015-01-01

    Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein's function, regulation and localization often depend on its interactions with other proteins. Here, we describe a protocol for the yeast protein-fragment complementation assay (PCA), a powerful method to detect direct and proximal associations between proteins in living cells. The interaction between two proteins, each fused to a dihydrofolate reductase (DHFR) protein fragment, translates into growth of yeast strains in presence of the drug methotrexate (MTX). Differential fitness, resulting from different amounts of reconstituted DHFR enzyme, can be quantified on high-density colony arrays, allowing to differentiate interacting from non-interacting bait-prey pairs. The high-throughput protocol presented here is performed using a robotic platform that parallelizes mating of bait and prey strains carrying complementary DHFR-fragment fusion proteins and the survival assay on MTX. This protocol allows to systematically test for thousands of protein-protein interactions (PPIs) involving bait proteins of interest and offers several advantages over other PPI detection assays, including the study of proteins expressed from their endogenous promoters without the need for modifying protein localization and for the assembly of complex reporter constructs.

  16. Functional interactions between polypyrimidine tract binding protein and PRI peptide ligand containing proteins.

    Science.gov (United States)

    Coelho, Miguel B; Ascher, David B; Gooding, Clare; Lang, Emma; Maude, Hannah; Turner, David; Llorian, Miriam; Pires, Douglas E V; Attig, Jan; Smith, Christopher W J

    2016-08-15

    Polypyrimidine tract binding protein (PTBP1) is a heterogeneous nuclear ribonucleoprotein (hnRNP) that plays roles in most stages of the life-cycle of pre-mRNA and mRNAs in the nucleus and cytoplasm. PTBP1 has four RNA binding domains of the RNA recognition motif (RRM) family, each of which can bind to pyrimidine motifs. In addition, RRM2 can interact via its dorsal surface with proteins containing short peptide ligands known as PTB RRM2 interacting (PRI) motifs, originally found in the protein Raver1. Here we review our recent progress in understanding the interactions of PTB with RNA and with various proteins containing PRI ligands.

  17. Molecular interactions of Epstein-Barr virus capsid proteins.

    Science.gov (United States)

    Wang, Wen-Hung; Chang, Li-Kwan; Liu, Shih-Tung

    2011-02-01

    The capsids of herpesviruses, which comprise major and minor capsid proteins, have a common icosahedral structure with 162 capsomers. An electron microscopic study shows that Epstein-Barr virus (EBV) capsids in the nucleus are immunolabeled by anti-BDLF1 and anti-BORF1 antibodies, indicating that BDLF1 and BORF1 are the minor capsid proteins of EBV. Cross-linking and electrophoresis studies of purified BDLF1 and BORF1 revealed that these two proteins form a triplex that is similar to that formed by the minor capsid proteins, VP19C and VP23, of herpes simplex virus type 1 (HSV-1). Although the interaction between VP23, a homolog of BDLF1, and the major capsid protein VP5 could not be verified biochemically in earlier studies, the interaction between BDLF1 and the EBV major capsid protein, viral capsid antigen (VCA), can be confirmed by glutathione S-transferase (GST) pulldown assay and coimmunoprecipitation. Additionally, in HSV-1, VP5 interacts with only the middle region of VP19C; in EBV, VCA interacts with both the N-terminal and middle regions of BORF1, a homolog of VP19C, revealing that the proteins in the EBV triplex interact with the major capsid protein differently from those in HSV-1. A GST pulldown study also identifies the oligomerization domains in VCA and the dimerization domain in BDLF1. The results presented herein reveal how the EBV capsid proteins interact and thereby improve our understanding of the capsid structure of the virus.

  18. 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.

  19. Insight into structural organization and protein-protein interaction of non structural 3 (NS3) proteins from dengue serotypes.

    Science.gov (United States)

    Parida, Pratap; Yadav, R N S; Sarma, Kishore

    2014-01-01

    Dengue infections produce a distinct character of virus-induced intracellular membrane alterations which are associated with the viral replication machinery. Currently, the NS3 protein is being targeted for antiviral therapy against dengue. NS3 protein of dengue virus interacts with nuclear receptor binding protein (NRBP) of human causing cell trafficking between the Endoplasmic Reticulum (ER) and Golgi, which interacts with Rac3, a member of the Rho-GTPase family. No crystal structure of the NRBP is available for any species, thus limiting the complete understanding of structure- function relationships of this protein. The present study deals with the molecular modeling of the viral protein (NS3 of DENV1-4), the host protein (NRBP) and their interactions through protein-protein docking study. Theoretical threedimensional structures of the NRBP and NS3 were modeled using the Modeller 9v8, and the evaluated models were docked using GRAMM-X to study the mode of protein-protein interaction (NRBP as receptor and NS3 as ligand). The docked docking complexes were further evaluated for interaction analysis by the RosettaDock Server. Suface and interface residues were observed along with hydrogen and hydrophobic interaction. The conserved residues forming hydrogen interaction of NRBP with DENV1-4 serotypes were found to be GLN 305, SER 363 and GLN 379.

  20. Multiphasic interactions between nucleotides and target proteins

    CERN Document Server

    Nissen, Per

    2016-01-01

    The nucleotides guanosine tetraphosphate (ppGpp) and guanosine pentaphosphate (pppGpp) bind to target proteins to promote bacterial survival (Corrigan et al. 2016). Thus, the binding of the nucleotides to RsgA, a GTPase, inhibits the hydrolysis of GTP. The dose response, taken to be curvilinear with respect to the logarithm of the inhibitor concentration, is instead much better (P<0.001 when the 6 experiments are combined) represented as multiphasic, with high to exceedingly high absolute r values for the straight lines, and with transitions in the form of non-contiguities (jumps). Profiles for the binding of radiolabeled nucleotides to HprT and Gmk, GTP synthesis enzymes, were, similarly, taken to be curvilinear with respect to the logarithm of the protein concentration. However, the profiles are again much better represented as multiphasic than as curvilinear (the P values range from 0.047 to <0.001 for each of the 8 experiments for binding of ppGpp and pppGpp to HprT). The binding of GTP to HprT and ...

  1. On the ion-mediated interaction between protein and DNA

    CERN Document Server

    Barbi, Maria

    2013-01-01

    The mechanism allowing a protein to search of a target sequence on DNA is currently described as an intermittent process composed of 3D diffusion in bulk and 1D diffusion along the DNA molecule. Due to the relevant charge of protein and DNA, electrostatic interaction should play a crucial role during this search. In this paper, we explicitly derive the mean field theory allowing for a description of the protein-DNA electrostatics in solution. This approach leads to a unified model of the search process, where 1D and 3D diffusion appear as a natural consequence of the diffusion on an extended interaction energy profile.

  2. Interaction of Protein and Cell with Different Chitosan Membranes

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    Interaction between proteins, cells and biomaterial surfaces is commonly observed and often used to measure biocompatibility of biomaterials.In this investigation, three kinds of biomaterials derived from chitosan were prepared.The surface wettability of these polymers, interaction of protein with material surface, and their effects on cell adhesion and growth were studied.The results show that the surface contact angle and surface charge of biomaterials have a close bearing on protein adsorption as well as cell adhesion and growth, indicating that through different chemical modifications, chitosan can be made into different kinds of biomedical materials to satisfy various needs.

  3. A cell-based method for screening RNA-protein interactions: identification of constitutive transport element-interacting proteins.

    Directory of Open Access Journals (Sweden)

    Robert L Nakamura

    Full Text Available We have developed a mammalian cell-based screening platform to identify proteins that assemble into RNA-protein complexes. Based on Tat-mediated activation of the HIV LTR, proteins that interact with an RNA target elicit expression of a GFP reporter and are captured by fluorescence activated cell sorting. This "Tat-hybrid" screening platform was used to identify proteins that interact with the Mason Pfizer monkey virus (MPMV constitutive transport element (CTE, a structured RNA hairpin that mediates the transport of unspliced viral mRNAs from the nucleus to the cytoplasm. Several hnRNP-like proteins, including hnRNP A1, were identified and shown to interact with the CTE with selectivity in the reporter system comparable to Tap, a known CTE-binding protein. In vitro gel shift and pull-down assays showed that hnRNP A1 is able to form a complex with the CTE and Tap and that the RGG domain of hnRNP A1 mediates binding to Tap. These results suggest that hnRNP-like proteins may be part of larger export-competent RNA-protein complexes and that the RGG domains of these proteins play an important role in directing these binding events. The results also demonstrate the utility of the screening platform for identifying and characterizing new components of RNA-protein complexes.

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

    Science.gov (United States)

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

    2011-01-01

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

  5. Energetic pathway sampling in a protein interaction domain

    DEFF Research Database (Denmark)

    Hultqvist, Greta; Haq, S. Raza; Punekar, Avinash S.;

    2013-01-01

    The affinity and specificity of protein-ligand interactions are influenced by energetic crosstalk within the protein domain. However, the molecular details of such intradomain allostery are still unclear. Here, we have experimentally detected and computationally predicted interaction pathways...... changes may reshape energetic signaling. The results were analyzed in the context of other members of the PDZ family, which were found to contain distinct interaction pathways for different peptide ligands. The data reveal a fascinating scenario whereby several energetic pathways are sampled within one...

  6. 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.

  7. 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

    employ stable isotopic amino acids in cell culture (SILAC) to differentially label proteins in EGF-stimulated versus unstimulated cells. Combined cell lysates were affinity-purified over the SH2 domain of the adapter protein Grb2 (GST-SH2 fusion protein) that specifically binds phosphorylated EGFR......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...

  8. PDZ domain-mediated interactions of G protein-coupled receptors with postsynaptic density protein 95

    DEFF Research Database (Denmark)

    Møller, Thor C; Wirth, Volker F; Roberts, Nina Ingerslev;

    2013-01-01

    G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome. Their signaling is regulated by scaffold proteins containing PDZ domains, but although these interactions are important for GPCR function, they are still poorly understood. We here present...

  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. Radicals involved in photoallergen/protein interactions

    Energy Technology Data Exchange (ETDEWEB)

    Delahanty, J.N.; Evans, J.C.; Rowlands, C.C.; Barratt, M.D.; Pendlington, R.U. (University College, Cardiff (England))

    1989-01-01

    Aqueous solutions (pH = 8) of both 3,3'-dimethyl and 4,4'-dimethyl substituted analogues of the photoallergen fentichlor (bis(2-hydroxy-5-chlorophenyl)sulphide) produced stable semiquinone radicals when irradiated with u.v. light (greater than 310 nm). These radicals have been characterised using electron spin resonance techniques: the results confirm the assignment of hyperfine coupling constants for the parent fentichlor radical. The binding of fentichlor to HSA was found to be partly oxygen dependent demonstrating a role for semiquinone type radicals in the binding mechanism. The stoichiometry and specificity of the binding of the dimethyl analogues to soluble proteins were found to be similar to that of fentichlor itself.

  11. Coarse-grained versus atomistic simulations: realistic interaction free energies for real proteins

    NARCIS (Netherlands)

    A. May; R. Pool; E. van Dijk; J. Bijlard; S. Abeln; J. Heringa; K.A. Feenstra

    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 at

  12. 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.

  13. Yeast Interacting Proteins Database: YOR284W, YOR284W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YOR284W HUA2 Cytoplasmic protein of unknown function; computational analysis of lar...it as bait (1) Rows with this bait as prey (4) YOR284W HUA2 Cytoplasmic protein of unknown function; computa...tein of unknown function; computational analysis of large-scale protein-protein i... HUA2 Prey description Cytoplasmic protein of unknown function; computational ana

  14. Identification of Topological Network Modules in Perturbed Protein Interaction Networks

    Science.gov (United States)

    Sardiu, Mihaela E.; Gilmore, Joshua M.; Groppe, Brad; Florens, Laurence; Washburn, Michael P.

    2017-01-01

    Biological networks consist of functional modules, however detecting and characterizing such modules in networks remains challenging. Perturbing networks is one strategy for identifying modules. Here we used an advanced mathematical approach named topological data analysis (TDA) to interrogate two perturbed networks. In one, we disrupted the S. cerevisiae INO80 protein interaction network by isolating complexes after protein complex components were deleted from the genome. In the second, we reanalyzed previously published data demonstrating the disruption of the human Sin3 network with a histone deacetylase inhibitor. Here we show that disrupted networks contained topological network modules (TNMs) with shared properties that mapped onto distinct locations in networks. We define TMNs as proteins that occupy close network positions depending on their coordinates in a topological space. TNMs provide new insight into networks by capturing proteins from different categories including proteins within a complex, proteins with shared biological functions, and proteins disrupted across networks. PMID:28272416

  15. 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.

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

    Science.gov (United States)

    Chen, T Scott; Keating, Amy E

    2012-07-01

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

  17. Protein interaction networks--more than mere modules.

    Directory of Open Access Journals (Sweden)

    Stefan Pinkert

    2010-01-01

    Full Text Available It is widely believed that the modular organization of cellular function is reflected in a modular structure of molecular networks. A common view is that a "module" in a network is a cohesively linked group of nodes, densely connected internally and sparsely interacting with the rest of the network. Many algorithms try to identify functional modules in protein-interaction networks (PIN by searching for such cohesive groups of proteins. Here, we present an alternative approach independent of any prior definition of what actually constitutes a "module". In a self-consistent manner, proteins are grouped into "functional roles" if they interact in similar ways with other proteins according to their functional roles. Such grouping may well result in cohesive modules again, but only if the network structure actually supports this. We applied our method to the PIN from the Human Protein Reference Database (HPRD and found that a representation of the network in terms of cohesive modules, at least on a global scale, does not optimally represent the network's structure because it focuses on finding independent groups of proteins. In contrast, a decomposition into functional roles is able to depict the structure much better as it also takes into account the interdependencies between roles and even allows groupings based on the absence of interactions between proteins in the same functional role. This, for example, is the case for transmembrane proteins, which could never be recognized as a cohesive group of nodes in a PIN. When mapping experimental methods onto the groups, we identified profound differences in the coverage suggesting that our method is able to capture experimental bias in the data, too. For example yeast-two-hybrid data were highly overrepresented in one particular group. Thus, there is more structure in protein-interaction networks than cohesive modules alone and we believe this finding can significantly improve automated function

  18. Twenty years of protein interaction studies for biological function deciphering.

    Science.gov (United States)

    Legrain, Pierre; Rain, Jean-Christophe

    2014-07-31

    Intensive methodological developments and technology innovation have been devoted to protein-protein interaction studies over 20years. Genetic indirect assays and sophisticated large scale biochemical analyses have jointly contributed to the elucidation of protein-protein interactions, still with a lot of drawbacks despite heavy investment in human resources and technologies. With the most recent developments in mass spectrometry and computational tools for studying protein content of complex samples, the initial goal of deciphering molecular bases of biological functions is now within reach. Here, we described the various steps of this process and gave examples of key milestones in this scientific story line. This article is part of a Special Issue entitled: 20years of Proteomics in memory of Viatliano Pallini. Guest Editors: Luca Bini, Juan J. Calvete, Natacha Turck, Denis Hochstrasser and Jean-Charles Sanchez.

  19. The Role of RPGR and Its Interacting Proteins in Ciliopathies.

    Science.gov (United States)

    Patnaik, Sarita Rani; Raghupathy, Rakesh Kotapati; Zhang, Xun; Mansfield, David; Shu, Xinhua

    2015-01-01

    Ciliopathies encompass a group of genetic disorders characterized by defects in the formation, maintenance, or function of cilia. Retinitis pigmentosa (RP) is frequently one of the clinical features presented in diverse ciliopathies. RP is a heterogeneous group of inherited retinal disorders, characterized by the death of photoreceptors and affecting more than one million individuals worldwide. The retinitis pigmentosa GTPase regulator (RPGR) gene is mutated in up to 20% of all RP patients. RPGR protein has different interacting partners to function in ciliary protein trafficking. In this review, we specifically focus on RPGR and its two interacting proteins: RPGRIP1 and RPGRIP1L. We summarize the function of the three proteins and highlight recent studies that provide insight into the cellular function of those proteins.

  20. CARMIL is a bona fide capping protein interactant.

    Science.gov (United States)

    Remmert, Kirsten; Olszewski, Thomas E; Bowers, M Blair; Dimitrova, Mariana; Ginsburg, Ann; Hammer, John A

    2004-01-23

    CARMIL, also known as Acan 125, is a multidomain protein that was originally identified on the basis of its interaction with the Src homology 3 (SH3) domain of type I myosins from Acanthamoeba. In a subsequent study of CARMIL from Dictyostelium, pull-down assays indicated that the protein also bound capping protein and the Arp2/3 complex. Here we present biochemical evidence that Acanthamoeba CARMIL interacts tightly with capping protein. In biochemical preparations, CARMIL copurified extensively with two polypeptides that were shown by microsequencing to be the alpha- and beta-subunits of Acanthamoeba capping protein. The complex between CARMIL and capping protein, which is readily demonstratable by chemical cross-linking, can be completely dissociated by size exclusion chromatography at pH 5.4. Analytical ultracentrifugation, surface plasmon resonance and SH3 domain pull-down assays indicate that the dissociation constant of capping protein for CARMIL is approximately 0.4 microm or lower. Using CARMIL fusion proteins, the binding site for capping protein was shown to reside within the carboxyl-terminal, approximately 200 residue, proline-rich domain of CARMIL. Finally, chemical cross-linking, analytical ultracentrifugation, and rotary shadowed electron microscopy revealed that CARMIL is asymmetric and that it exists in a monomer dimer equilibrium with an association constant of 1.0 x 10(6) m(-1). Together, these results indicate that CARMIL self-associates and interacts with capping protein with affinities that, given the cellular concentrations of the proteins ( approximately 1 and 2 microm for capping protein and CARMIL, respectively), indicate that both activities should be physiologically relevant.

  1. Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model.

    Science.gov (United States)

    An, Ji-Yong; Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Yan, Gui-Ying; Hu, Ji-Pu

    2016-10-01

    Predicting protein-protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high-throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM-BiGP that combines the relevance vector machine (RVM) model and Bi-gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi-gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five-fold cross-validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM-BiGP method is significantly better than the SVM-based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future

  2. Interactions between Phage-Shock Proteins in Escherichia coli

    OpenAIRE

    Adams, Hendrik; Teertstra, Wieke; Demmers, Jeroen; Boesten, Rolf; Tommassen, Jan

    2003-01-01

    Expression of the pspABCDE operon of Escherichia coli is induced upon infection by filamentous phage and by many other stress conditions, including defects in protein export. Expression of the operon requires the alternative sigma factor σ54 and the transcriptional activator PspF. In addition, PspA plays a negative regulatory role, and the integral-membrane proteins PspB and PspC play a positive one. In this study, we investigated whether the suggested protein-protein interactions implicated ...

  3. Studying host cell protein interactions with monoclonal antibodies using high throughput protein A chromatography.

    Science.gov (United States)

    Sisodiya, Vikram N; Lequieu, Joshua; Rodriguez, Maricel; McDonald, Paul; Lazzareschi, Kathlyn P

    2012-10-01

    Protein A chromatography is typically used as the initial capture step in the purification of monoclonal antibodies produced in Chinese hamster ovary (CHO) cells. Although exploiting an affinity interaction for purification, the level of host cell proteins in the protein A eluent varies significantly with different feedstocks. Using a batch binding chromatography method, we performed a controlled study to assess host cell protein clearance across both MabSelect Sure and Prosep vA resins. We individually spiked 21 purified antibodies into null cell culture fluid generated with a non-producing cell line, creating mock cell culture fluids for each antibody with an identical composition of host cell proteins and antibody concentration. We demonstrated that antibody-host cell protein interactions are primarily responsible for the variable levels of host cell proteins in the protein A eluent for both resins when antibody is present. Using the additives guanidine HCl and sodium chloride, we demonstrated that antibody-host cell protein interactions may be disrupted, reducing the level of host cell proteins present after purification on both resins. The reduction in the level of host cell proteins differed between antibodies suggesting that the interaction likely varies between individual antibodies but encompasses both an electrostatic and hydrophobic component.

  4. Probing interactions between collagen proteins via microrheology

    Science.gov (United States)

    Shayegan, Marjan; Forde, Nancy R.

    2012-10-01

    Collagen is the major structural protein of our connective tissues. It provides integrity and mechanical strength through its hierarchical organization. Defects in collagen can lead to serious connective tissue diseases. Collagen is also widely used as a biomaterial. Given that mechanical properties are related to the structure of materials, the main goal of our research is to understand how molecular structure correlates with microscale mechanical properties of collagen solutions and networks. We use optical tweezers to trap and monitor thermal fluctuations of an embedded probe particle, from which viscoelastic properties of the solution are extracted. We find that elasticity becomes comparable to viscous behavior at collagen concentrations of 5mg/ml. Furthermore, by simultaneously neutralizing pH and adding salt, we observe changes in viscosity and elasticity of the solution over time. We attribute this to the self-assembly process of collagen molecules into fibrils with different mechanical properties. Self-assembly of collagen under these conditions is verified by turbidity measurements as well as electron microscopy. By comparing results from these local studies of viscoelasticity, we can detect spatial heterogeneity of fibril formation throughout the solution.

  5. Yeast Interacting Proteins Database: YDL121C, YDL100C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YDL121C - Putative protein of unknown function; green fluorescent protein (GFP)-fus...ion protein localizes to the endoplasmic retiuculum; YDL121C is not an essential protein Rows with this bait... as bait (1) Rows with this bait as prey (0) YDL100C GET3 Guanine nucleotide exchange factor for Gpa1p; ampl...his prey as prey (10) Rows with this prey as bait (2) 3 5 2 2 0 0 0 0 0 - - - - - 0 0 8 - Show YDL121C Bait ORF YDL...n; green fluorescent protein (GFP)-fusion protein localizes to the endoplasmic retiuculum; YDL121C is not an

  6. Hepatitis B virus X protein interacts with β5 subunit of heterotrimeric guanine nucleotide binding protein

    Directory of Open Access Journals (Sweden)

    Chen Wei

    2005-08-01

    Full Text Available Abstract Background To isolate cellular proteins interacting with hepatitis B virus X protein (HBX, from HepG2 cells infected with hepatitis B virus (HBV. Results HBV particles were produced in culture medium of HepG2 cells transfected with the mammalian expression vector containing the linear HBV genome, as assessed by commercially available ELISA assay. A cDNA library was made from these cells exposed to HBV. From yeast two hybrid screening with HBX as bait, human guanine nucleotide binding protein β subunit 5L (GNβ5 was isolated from the cDNA library constructed in this study as a new HBX-interacting protein. The HBX-GNβ5 interaction was further supported by mammalian two hybrid assay. Conclusion The use of a cDNA library constructed from HBV-transfected HepG2 cells has resulted in the isolation of new cellular proteins interacting with HBX.

  7. Arabidopsis scaffold protein RACK1A interacts with diverse environmental stress and photosynthesis related proteins.

    Science.gov (United States)

    Kundu, Nabanita; Dozier, Uvetta; Deslandes, Laurent; Somssich, Imre E; Ullah, Hemayet

    2013-05-01

    Scaffold proteins are known to regulate important cellular processes by interacting with multiple proteins to modulate molecular responses. RACK1 (Receptor for Activated C Kinase 1) is a WD-40 type scaffold protein, conserved in eukaryotes, from Chlamydymonas to plants and humans, expresses ubiquitously and plays regulatory roles in diverse signal transduction and stress response pathways. Here we present the use of Arabidopsis RACK1A, the predominant isoform of a 3-member family, as a bait to screen a split-ubiquitin based cDNA library. In total 97 proteins from dehydration, salt stress, ribosomal and photosynthesis pathways are found to potentially interact with RACK1A. False positive interactions were eliminated following extensive selection based growth potentials. Confirmation of a sub-set of selected interactions is demonstrated through the co-transformation with individual plasmid containing cDNA and the respective bait. Interaction of diverse proteins points to a regulatory role of RACK1A in the cross-talk between signaling pathways. Promoter analysis of the stress and photosynthetic pathway genes revealed conserved transcription factor binding sites. RACK1A is known to be a multifunctional protein and the current identification of potential interacting proteins and future in vivo elucidations of the physiological basis of such interactions will shed light on the possible molecular mechanisms that RACK1A uses to regulate diverse signaling pathways.

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

    Science.gov (United States)

    Yang, Lei; Tang, Xianglong

    2014-01-01

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

  9. 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.

  10. Screening and analysis on the protein interaction of the protein VP7 in grass carp reovirus.

    Science.gov (United States)

    Yan, Xiuying; Xie, Jiguo; Li, Jie; Shuanghu, Cai; Wu, Zaohe; Jian, Jichang

    2015-06-01

    Grass carp reovirus (GCRV) has caused serious economic losses for several decades in China. The protein VP7 is one of the important structural proteins in GCRV. Recent studies indicated that the protein VP7 had the commendable antigenicity and immunogenicity. The protein VP7 cooperated with VP5 could change the conformation of the cell membrane and facilitate entry of GCRV into host cells. We speculated that the protein VP7 should play an important role in the pathogenesis of GCRV. In order to explore the function of the protein VP7, the bait protein expression plasmid pGBKT7-vp7 and the cDNA library of CIK cells were constructed. By yeast two-hybrid system, after multiple screening with the high screening rate medium, rotary verification, sequencing and bioinformatics analysis, the interactions of the protein VP7 with ribosomal protein S20 (RPS20) and eukaryotic translation initiation factor 3 subunit b (eIF3b) in CIK cells were identified. RPS20 played the important roles in the generation of influenza B virus and a variety of diseases. eIF3b was relative to the infection of some viruses. This study suggested that the protein VP7 played the role in viral replication and most likely interacted with host proteins by RPS20 and eIF3b. The interaction mechanisms of the protein VP7 with RPS20 and eIF3b, and the subsequent effector mechanisms needed to be further studied. The corresponding protein interaction of the protein VP7 was not acquired in bioinformatics. The protein VP7 and its untranslated region may have the unknown special function. This study laid the foundation for deeply exploring the function of the protein VP7 in GCRV and had the important scientific significance for exploring the pathogenic mechanism of GCRV.

  11. Rigidity and flexibility in protein-protein interaction networks: a case study on neuromuscular disorders

    OpenAIRE

    2014-01-01

    Mutations in proteins can have deleterious effects on a protein's stability and function, which ultimately causes particular diseases. Genetically inherited muscular dystrophies (MDs) include several genetic diseases, which cause increasing weakness in muscles and disability to perform muscular functions progressively. Different types of mutations in the gene coding translates into defunct proteins cause different neuro-muscular diseases. Defunct protein interactions in human proteome may cau...

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

    OpenAIRE

    2014-01-01

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

  13. Interactions of the HSV-1 UL25 Capsid Protein with Cellular Microtubule-associated Protein

    Institute of Scientific and Technical Information of China (English)

    Lei GUO; Ying ZHANG; Yan-chun CHE; Wen-juan WU; Wei-zhong LI; Li-chun WANG; Yun LIAO; Long-ding LIU; Qi-han LI

    2008-01-01

    An interaction between the HSV-1 UL25 capsid protein and cellular microtubule-associated protein was found using a yeast two-hybrid screen and β-D-galactosidase activity assays. Immunofluorescence microscopy of the UL25 protein demonstrated its co-localization with cellular microtubule-associated protein in the plasma membrane. Further investigations with deletion mutants suggest that UL25 is likely to have a function in the nucleus.

  14. An analysis pipeline for the inferenceof protein-protein interaction networks

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, Ronald C. [Pacific Northwest National Laboratory (PNNL); Singhal, Mudita [Pacific Northwest National Laboratory (PNNL); Daly, Don S. [Pacific Northwest National Laboratory (PNNL); Gilmore, Jason [Pacific Northwest National Laboratory (PNNL); Cannon, Bill [Pacific Northwest National Laboratory (PNNL); Domico, Kelly [Pacific Northwest National Laboratory (PNNL); White, Amanda M. [Pacific Northwest National Laboratory (PNNL); Auberry, Deanna L [ORNL; Auberry, Kenneth J [ORNL; Hooker, Brian [Pacific Northwest National Laboratory (PNNL); Hurst, Gregory {Greg} B [ORNL; McDermott, Jason [Pacific Northwest National Laboratory (PNNL); McDonald, W Hayes [ORNL; Pelletier, Dale A [ORNL; Schmoyer, Denise D [ORNL; Wiley, Steven [Pacific Northwest National Laboratory (PNNL)

    2009-09-01

    We present an integrated platform that is used for the reconstruction and analysis of protein-protein interaction networks inferred from Mass Spectrometry (MS) bait-prey experiment data. At the heart of this pipeline is 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. Among the many algorithms available in SEBINI is the Bayesian Estimator of Probabilities of Protein-Protein Associations (BEPro3) algorithm, which is used to infer interaction networks from such MS affinity isolation data. For integration, comparison and analysis of the inferred protein-protein interactions with interaction evidence obtained from multiple public sources, the pipeline connects to the Collective Analysis of Biological Interaction Networks (CABIN) software. Incorporating BEPro3 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 MS bait-prey experiments.

  15. 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.

  16. 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

  17. Noncovalent protein interaction with poly(ADP-ribose).

    Science.gov (United States)

    Malanga, Maria; Althaus, Felix R

    2011-01-01

    Compared to most common posttranslational modifications of proteins, a peculiarity of poly(ADP-ribosyl)ation is the molecular heterogeneity and complexity of the reaction product, poly(ADP-ribose) (PAR). In fact, protein-bound PAR consists of variously sized (2-200 ADP-ribose residues) linear or branched molecules, negatively charged at physiological pH. It is now clear that PAR not only affects the function of the polypeptide to which it is covalently bound, but it can also influence the activity of other proteins by engaging specific noncovalent interactions. In the last 10 years, the family of PAR-binding proteins has been rapidly growing and functional studies have expanded the regulatory potential of noncovalent -protein targeting by PAR far beyond initial assumptions.In this chapter, methods are described for: (1) PAR synthesis and analysis; (2) detecting PAR-binding proteins in protein mixtures; (3) defining affinity and specificity of PAR binding to individual proteins or protein fragments; and (4) identifying PAR molecules selectively involved in the interaction.

  18. Detection of interaction between biomineralising proteins and calcium carbonate microcrystals.

    Science.gov (United States)

    Rademaker, Hanna; Launspach, Malte

    2011-01-01

    The natural composite nacre is characterised by astonishing mechanical properties, although the main constituent is a brittle mineral shaped as tablets interdispersed by organic layers. To mimic the natural formation process which takes place at ambient conditions an understanding of the mechanism responsible for a defined microstructure of nacre is necessary. Since proteins are assumed to be involved in this mechanism, it is advantageous to identify distinct proteins interacting with minerals from the totality of proteins contained in nacre. Here, we adopted and modified a recently published approach given by Suzuki et al. [1] that gives a hint of specific protein-mineral interactions. Synthesised aragonite or calcite microcrystals were incubated with a protein mixture extracted from nacre of Haliotis laevigata. After incubation the mineral phase was dissolved and investigated for attached proteins. The results give a hint of one protein that seems to bind specifically to aragonite and not to calcite. The presented protocol seems to be suitable to detect mineral binding proteins quickly and therefore can point to proteins whose mineral binding capabilities should be investigated further.

  19. Yeast Interacting Proteins Database: YNL189W, YBR072W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ait as prey (0) YBR072W HSP26 Small heat shock protein (sHSP) with chaperone activity; forms hollow...chaperone activity; forms hollow, sphere-shaped oligomers that suppress unfolded proteins aggregation; oligo

  20. Yeast Interacting Proteins Database: YJR091C, YOR014W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...ssociated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpression causes increas

  1. Yeast Interacting Proteins Database: YJR091C, YPL159C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpres

  2. Yeast Interacting Proteins Database: YJR091C, YLR059C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre... mRNAs encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; ove

  3. Yeast Interacting Proteins Database: YJR091C, YHR130C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...As encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexp

  4. Yeast Interacting Proteins Database: YJR091C, YDR203W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...As encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexp

  5. Yeast Interacting Proteins Database: YJR091C, YDR008C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre... membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpression cau

  6. Yeast Interacting Proteins Database: YJR091C, YOR317W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...NAs encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overex

  7. Yeast Interacting Proteins Database: YHL002W, YNR006W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ubiquitinated membrane proteins into intralumenal vesicles prior to vacuolar degradation, as well as for rec...quired for sorting of ubiquitinated membrane proteins into intralumenal vesicles prior to vacuolar degradation,

  8. Yeast Interacting Proteins Database: YNR006W, YHL002W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ired for sorting of ubiquitinated membrane proteins into intralumenal vesicles prior to vacuolar degradation,...omplex required for sorting of ubiquitinated membrane proteins into intralumenal vesicles prior to vacuolar degradation,

  9. Yeast Interacting Proteins Database: YPL002C, YJR102C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ndent sorting of proteins into the endosome; appears to be functionally related to SNF7; involved in glucose...x, which is involved in ubiquitin-dependent sorting of proteins into the endosome; appears

  10. Yeast Interacting Proteins Database: YHR114W, YLR112W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available y (0) YLR112W - Dubious open reading frame unlikely to encode a protein, based on...e name - Prey description Dubious open reading frame unlikely to encode a protein, based on available experi

  11. Yeast Interacting Proteins Database: YHR114W, YDL134C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available y (0) YDL134C PPH21 Catalytic subunit of protein phosphatase 2A, functionally red...gene name PPH21 Prey description Catalytic subunit of protein phosphatase 2A, functionally redundant with Pp

  12. Yeast Interacting Proteins Database: YNL311C, YKL001C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available -purification experiments; putative F-box protein; analysis of integrated high-throughput datasets predicts ...ments; putative F-box protein; analysis of integrated high-throughput datasets predicts involvement in ubiqu

  13. Yeast Interacting Proteins Database: YLR263W, YDR510W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available W SMT3 Ubiquitin-like protein of the SUMO family, conjugated to lysine residues o...MT3 Prey description Ubiquitin-like protein of the SUMO family, conjugated to lysine residues of target prot

  14. Yeast Interacting Proteins Database: YCL020W, YDR510W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available R510W SMT3 Ubiquitin-like protein of the SUMO family, conjugated to lysine residu... name SMT3 Prey description Ubiquitin-like protein of the SUMO family, conjugated to lysine residues of targ

  15. Yeast Interacting Proteins Database: YNL152W, YMR032W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YNL152W INN1 Essential protein that associates with the contractile actomyosin ring... Bait description Essential protein that associates with the contractile actomyosin ring, required for ingre

  16. Yeast Interacting Proteins Database: YLR291C, YOR284W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YOR284W HUA2 Cytoplasmic protein of unknown function; computational analysis of l...prey (0) Prey ORF YOR284W Prey gene name HUA2 Prey description Cytoplasmic protein of unknown function; computation

  17. Yeast Interacting Proteins Database: YKR092C, YKL023W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available W - Putative protein of unknown function, predicted by computational methods to b...ait as prey (0) Prey ORF YKL023W Prey gene name - Prey description Putative protein of unknown function, predicted by computation

  18. Yeast Interacting Proteins Database: YLR295C, YJR083C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available 7) Rows with this bait as prey (0) YJR083C ACF4 Protein of unknown function, computational analysis of large...me ACF4 Prey description Protein of unknown function, computational analysis of l

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

    Lifescience Database Archive (English)

    Full Text Available it as prey (1) YPL070W MUK1 Cytoplasmic protein of unknown function containing a Vps9 domain; computation...ey description Cytoplasmic protein of unknown function containing a Vps9 domain; computational analysis of l

  20. Yeast Interacting Proteins Database: YER081W, YOR318C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YOR318C - Dubious open reading frame unlikely to encode a protein, based on available experimental and comparative...likely to encode a protein, based on available experimental and comparative seque

  1. Yeast Interacting Proteins Database: YBL033C, YNL105W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available reading frame unlikely to encode a protein, based on available experimental and comparative sequence data; p...a protein, based on available experimental and comparative sequence data; partial

  2. Yeast Interacting Proteins Database: YER081W, YPR126C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YPR126C - Dubious open reading frame unlikely to encode a functional protein, based on available experimental and comparative...ubious open reading frame unlikely to encode a functional protein, based on available experimental and comparative

  3. Yeast Interacting Proteins Database: YMR316W, YER125W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YMR316W DIA1 Protein of unknown function, involved in invasive and pseudohyphal gro... of unknown function, involved in invasive and pseudohyphal growth; green fluorescent protein (GFP)-fusion p

  4. Yeast Interacting Proteins Database: YML064C, YKL103C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available he peptidase family M18; often used as a marker protein in studies of autophagy a... to the peptidase family M18; often used as a marker protein in studies of autophagy and cytosol to vacuole

  5. Yeast Interacting Proteins Database: YHR005C, YOR212W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YHR005C GPA1 GTP-binding alpha subunit of the heterotrimeric G protein that couples...the heterotrimeric G protein that couples to pheromone receptors; negatively regulates the mating pathway by

  6. Yeast Interacting Proteins Database: YPL077C, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YPL077C - Putative protein of unknown function; regulates PIS1 expression; mutant display...Bait description Putative protein of unknown function; regulates PIS1 expression; mutant display

  7. Yeast Interacting Proteins Database: YPR029C, YFR043C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available his bait as prey (1) YFR043C IRC6 Putative protein of unknown function; null mutant displays increased level...C6 Prey description Putative protein of unknown function; null mutant displays increased levels of spontaneo

  8. Yeast Interacting Proteins Database: YPL204W, YOL149W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YPL204W HRR25 Protein kinase involved in regulating diverse events including vesicu...tion Protein kinase involved in regulating diverse events including vesicular trafficking, DNA repair, and c

  9. Yeast Interacting Proteins Database: YPL204W, YER095W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YPL204W HRR25 Protein kinase involved in regulating diverse events including vesicu... gene name HRR25 Bait description Protein kinase involved in regulating diverse events including vesicular t

  10. Yeast Interacting Proteins Database: YGR086C, YKL142W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ption induced under cell wall stress; protein levels are reduced under anaerobic conditions; originally thou...iption induced under cell wall stress; protein levels are reduced under anaerobic conditions; originally tho

  11. Yeast Interacting Proteins Database: YDL089W, YML008C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available rDNA repeat stability; null mutant causes increase in unequal sister-chromatid exchange; GFP-fusion protein...peat stability; null mutant causes increase in unequal sister-chromatid exchange; GFP-fusion protein localiz

  12. Yeast Interacting Proteins Database: YLR288C, YLR125W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available prey (1) YLR125W - Putative protein of unknown function; mutant has decreased Ty3... name - Prey description Putative protein of unknown function; mutant has decreased Ty3 transposition; YLR12

  13. Yeast Interacting Proteins Database: YPL204W, YHR185C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available Sporulation protein required for prospore membrane formation at selected spindle poles...n Sporulation protein required for prospore membrane formation at selected spindle poles, ensures functional

  14. Yeast Interacting Proteins Database: YJR091C, YEL013W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpression causes...ed proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpression causes increased sens

  15. Yeast Interacting Proteins Database: YKL002W, YFL034C-B [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available integral membrane proteins into lumenal vesicles of multivesicular bodies, and for delivery of newly synthes...ntegral membrane proteins into lumenal vesicles of multivesicular bodies, and for delivery of newly synthesi

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

    Lifescience Database Archive (English)

    Full Text Available ity (BRITE) - Alternative path with 1 intervening protein (YPD) 0 Alternative path with 2 intervening proteins (YPD) 0 IST hit 3 IST hit in the opposite bait/prey orientation - ...

  17. Response of the mosquito protein interaction network to dengue infection

    Directory of Open Access Journals (Sweden)

    Pike Andrew D

    2010-06-01

    Full Text Available Abstract Background Two fifths of the world's population is at risk from dengue. The absence of effective drugs and vaccines leaves vector control as the primary intervention tool. Understanding dengue virus (DENV host interactions is essential for the development of novel control strategies. The availability of genome sequences for both human and mosquito host greatly facilitates genome-wide studies of DENV-host interactions. Results We developed the first draft of the mosquito protein interaction network using a computational approach. The weighted network includes 4,214 Aedes aegypti proteins with 10,209 interactions, among which 3,500 proteins are connected into an interconnected scale-free network. We demonstrated the application of this network for the further annotation of mosquito proteins and dissection of pathway crosstalk. Using three datasets based on physical interaction assays, genome-wide RNA interference (RNAi screens and microarray assays, we identified 714 putative DENV-associated mosquito proteins. An integrated analysis of these proteins in the network highlighted four regions consisting of highly interconnected proteins with closely related functions in each of replication/transcription/translation (RTT, immunity, transport and metabolism. Putative DENV-associated proteins were further selected for validation by RNAi-mediated gene silencing, and dengue viral titer in mosquito midguts was significantly reduced for five out of ten (50.0% randomly selected genes. Conclusions Our results indicate the presence of common host requirements for DENV in mosquitoes and humans. We discuss the significance of our findings for pharmacological intervention and genetic modification of mosquitoes for blocking dengue transmission.

  18. 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. 

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

    Science.gov (United States)

    Li, Jian-Feng; Zhang, Dandan

    2014-07-01

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

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

    Institute of Scientific and Technical Information of China (English)

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

    2006-01-01

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

  1. 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.

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

    Science.gov (United States)

    Schweiger, Regina; Schwenkert, Serena

    2014-03-09

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

  3. Yeast Interacting Proteins Database: YLR295C, YDR510W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available 7) Rows with this bait as prey (0) YDR510W SMT3 Ubiquitin-like protein of the SUMO family, conjugated to lys...uitin-like protein of the SUMO family, conjugated to lysine residues of target proteins; regulates chromatid

  4. Yeast Interacting Proteins Database: YOR037W, YCL056C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available (GFP)-fusion protein localizes to the cytoplasm in a punctate pattern; null mutant displays decreased thermo...e pattern; null mutant displays decreased thermotolerance Rows with this prey as prey Rows with this prey as... of unknown function; green fluorescent protein (GFP)-fusion protein localizes to the cytoplasm in a punctat

  5. Yeast Interacting Proteins Database: YGR071C, YJL058C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available own function; deletion mutant has increased glycogen accumulation and displays elongated buds; green fluores...YGR071C - Putative protein of unknown function; deletion mutant has increased glycogen accumulation and disp...lays elongated buds; green fluorescent protein (GFP)-fusion protein localizes to th

  6. Machine Learning of Protein Interactions in Fungal Secretory Pathways.

    Directory of Open Access Journals (Sweden)

    Jana Kludas

    Full Text Available In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict protein interactions in other, related species. In our methodology, we combine several state of the art machine learning approaches, namely, multiple kernel learning (MKL, pairwise kernels and kernelized structured output prediction in the supervised graph inference framework. For MKL, we apply recently proposed centered kernel alignment and p-norm path following approaches to integrate several feature sets describing the proteins, demonstrating improved performance. For graph inference, we apply input-output kernel regression (IOKR in supervised and semi-supervised modes as well as output kernel trees (OK3. In our experiments simulating increasing genetic distance, Input-Output Kernel Regression proved to be the most robust prediction approach. We also show that the MKL approaches improve the predictions compared to uniform combination of the kernels. We evaluate the methods on the task of predicting protein-protein-interactions in the secretion pathways in fungi, S.cerevisiae, baker's yeast, being the source, T. reesei being the target of the inter-species transfer learning. We identify completely novel candidate secretion proteins conserved in filamentous fungi. These proteins could contribute to their unique secretion capabilities.

  7. Machine Learning of Protein Interactions in Fungal Secretory Pathways

    Science.gov (United States)

    Kludas, Jana; Arvas, Mikko; Castillo, Sandra; Pakula, Tiina; Oja, Merja; Brouard, Céline; Jäntti, Jussi; Penttilä, Merja

    2016-01-01

    In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict protein interactions in other, related species. In our methodology, we combine several state of the art machine learning approaches, namely, multiple kernel learning (MKL), pairwise kernels and kernelized structured output prediction in the supervised graph inference framework. For MKL, we apply recently proposed centered kernel alignment and p-norm path following approaches to integrate several feature sets describing the proteins, demonstrating improved performance. For graph inference, we apply input-output kernel regression (IOKR) in supervised and semi-supervised modes as well as output kernel trees (OK3). In our experiments simulating increasing genetic distance, Input-Output Kernel Regression proved to be the most robust prediction approach. We also show that the MKL approaches improve the predictions compared to uniform combination of the kernels. We evaluate the methods on the task of predicting protein-protein-interactions in the secretion pathways in fungi, S.cerevisiae, baker’s yeast, being the source, T. reesei being the target of the inter-species transfer learning. We identify completely novel candidate secretion proteins conserved in filamentous fungi. These proteins could contribute to their unique secretion capabilities. PMID:27441920

  8. 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.

  9. Dynamic multibody protein interactions suggest versatile pathways for copper trafficking.

    Science.gov (United States)

    Keller, Aaron M; Benítez, Jaime J; Klarin, Derek; Zhong, Linghao; Goldfogel, Matthew; Yang, Feng; Chen, Tai-Yen; Chen, Peng

    2012-05-30

    As part of intracellular copper trafficking pathways, the human copper chaperone Hah1 delivers Cu(+) to the Wilson's Disease Protein (WDP) via weak and dynamic protein-protein interactions. WDP contains six homologous metal binding domains (MBDs) connected by flexible linkers, and these MBDs all can receive Cu(+) from Hah1. The functional roles of the MBD multiplicity in Cu(+) trafficking are not well understood. Building on our previous study of the dynamic interactions between Hah1 and the isolated fourth MBD of WDP, here we study how Hah1 interacts with MBD34, a double-domain WDP construct, using single-molecule fluorescence resonance energy transfer (smFRET) combined with vesicle trapping. By alternating the positions of the smFRET donor and acceptor, we systematically probed Hah1-MBD3, Hah1-MBD4, and MBD3-MBD4 interaction dynamics within the multidomain system. We found that the two interconverting interaction geometries were conserved in both intermolecular Hah1-MBD and intramolecular MBD-MBD interactions. The Hah1-MBD interactions within MBD34 are stabilized by an order of magnitude relative to the isolated single-MBDs, and thermodynamic and kinetic evidence suggest that Hah1 can interact with both MBDs simultaneously. The enhanced interaction stability of Hah1 with the multi-MBD system, the dynamic intramolecular MBD-MBD interactions, and the ability of Hah1 to interact with multiple MBDs simultaneously suggest an efficient and versatile mechanism for the Hah1-to-WDP pathway to transport Cu(+).

  10. Yeast Interacting Proteins Database: YMR124W, YLR031W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available in localizes to the cell periphery, cytoplasm, bud, and bud neck; interacts with Crm1p in two-hybrid assay; ... periphery, cytoplasm, bud, and bud neck; interacts with Crm1p in two-hybrid assay

  11. Yeast Interacting Proteins Database: YMR153W, YLR324W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available peroxisome number; partially functionally redundant with Pex31p; genetic interactions suggest action at a step downstream of steps... partially functionally redundant with Pex31p; genetic interactions suggest action at a step downstream of steps

  12. 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.

  13. 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.

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

    Science.gov (United States)

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

    2015-01-05

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

  15. CAG trinucleotide RNA repeats interact with RNA-binding proteins

    Energy Technology Data Exchange (ETDEWEB)

    McLaughlin, B.A.; Eberwine, J.; Spencer, C. [Univ. of Pennsylvania, Philadelphia, PA (United States)

    1996-09-01

    Genes associated with several neurological diseases are characterized by the presence of an abnormally long trinucleotide repeat sequence. By way of example, Huntington`s disease (HD), is characterized by selective neuronal degeneration associated with the expansion of a polyglutamine-encoding CAG tract. Normally, this CAG tract is comprised of 11-34 repeats, but in HD it is expanded to >37 repeats in affected individuals. The mechanism by which CAG repeats cause neuronal degeneration is unknown, but it has been speculated that the expansion primarily causes abnormal protein functioning, which in turn causes HD pathology. Other mechanisms, however, have not been ruled out. Interactions between RNA and RNA-binding proteins have previously been shown to play a role in the expression of several eukaryotic genes. Herein, we report the association of cytoplasmic proteins with normal length and extended CAG repeats, using gel shift and LJV crosslinking assays. Cytoplasmic protein extracts from several rat brain regions, including the striatum and cortex, sites of neuronal degeneration in HD, contain a 63-kD RNA-binding protein that specifically interacts with these CAG-repeat sequences. These protein-RNA interactions are dependent on the length of the CAG repeat, with longer repeats binding substantially more protein. Two CAG repeat-binding proteins are present in human cortex and striatum; one comigrates with the rat protein at 63 kD, while the other migrates at 49 kD. These data suggest mechanisms by which RNA-binding proteins may be involved in the pathological course of trinucleotide repeat-associated neurological diseases. 47 refs., 5 figs.

  16. High mobility group A-interacting proteins in cancer: focus on chromobox protein homolog 7, homeodomain interacting protein kinase 2 and PATZ

    Directory of Open Access Journals (Sweden)

    Monica Fedele

    2012-03-01

    Full Text Available The High Mobility Group A (HMGA proteins, a family of DNA architectural factors, by interacting with different proteins play crucial roles in neoplastic transformation of a wide range of tissues. Therefore, the search for HMGA-interacting partners was carried out by several laboratories in order to investigate the mechanisms underlying HMGA-dependent tumorigenesis. Three of the several HMGA-binding proteins are discussed in this review. These are the Chromobox family protein (chromobox protein homolog 7, CBX7, the homeodomain interacting protein kinase 2 (HIPK2 and the POZ/domain and Kruppel zinc finger family member, PATZ. All of them play a critical role in tumorigenesis, and may also be independent markers of cancer. Their activities are linked to cell cycle, apoptosis and senescence. In this review, we discuss the properties of each protein, including their effect on HMGA1 functions, and propose a model accounting for how their activities might be coordinated.

  17. Nanoparticle corona for proteins: mechanisms of interaction between dendrimers and proteins.

    Science.gov (United States)

    Shcharbin, Dzmitry; Ionov, Maksim; Abashkin, Viktar; Loznikova, Svetlana; Dzmitruk, Volha; Shcharbina, Natallia; Matusevich, Ludmila; Milowska, Katarzyna; Gałęcki, Krystian; Wysocki, Stanisław; Bryszewska, Maria

    2015-10-01

    Protein absorption at the surface of big nanoparticles and formation of 'protein corona' can completely change their biological properties. In contrast, we have studied the binding of small nanoparticles - dendrimers - to proteins and the formation of their 'nanoparticle corona'. Three different types of interactions were observed. (1) If proteins have rigid structure and active site buried deeply inside, the 'nanoparticle corona' is unaffected. (2) If proteins have a flexible structure and their active site is also buried deeply inside, the 'nanoparticle corona' affects protein structure, but not enzymatic activity. (3) The 'nanoparticle corona' changes both the structure and enzymatic activity of flexible proteins that have surface-based active centers. These differences are important in understanding interactions taking place at a bio-nanointerface.

  18. Chaperone-interacting TPR proteins in Caenorhabditis elegans.

    Science.gov (United States)

    Haslbeck, Veronika; Eckl, Julia M; Kaiser, Christoph J O; Papsdorf, Katharina; Hessling, Martin; Richter, Klaus

    2013-08-23

    The ATP-hydrolyzing molecular chaperones Hsc70/Hsp70 and Hsp90 bind a diverse set of tetratricopeptide repeat (TPR)-containing cofactors via their C-terminal peptide motifs IEEVD and MEEVD. These cochaperones contribute to substrate turnover and confer specific activities to the chaperones. Higher eukaryotic genomes encode a large number of TPR-domain-containing proteins. The human proteome contains more than 200 TPR proteins, and that of Caenorhabditis elegans, about 80. It is unknown how many of them interact with Hsc70 or Hsp90. We systematically screened the C. elegans proteome for TPR-domain-containing proteins that likely interact with Hsc70 and Hsp90 and ranked them due to their similarity with known chaperone-interacting TPRs. We find C. elegans to encode many TPR proteins, which are not present in yeast. All of these have homologs in fruit fly or humans. Highly ranking uncharacterized open reading frames C33H5.8, C34B2.5 and ZK370.8 may encode weakly conserved homologs of the human proteins RPAP3, TTC1 and TOM70. C34B2.5 and ZK370.8 bind both Hsc70 and Hsp90 with low micromolar affinities. Mutation of amino acids involved in EEVD binding disrupts the interaction. In vivo, ZK370.8 is localized to mitochondria in tissues with known chaperone requirements, while C34B2.5 colocalizes with Hsc70 in intestinal cells. The highest-ranking open reading frame with non-conserved EEVD-interacting residues, F52H3.5, did not show any binding to Hsc70 or Hsp90, suggesting that only about 15 of the TPR-domain-containing proteins in C. elegans interact with chaperones, while the many others may have evolved to bind other ligands.

  19. Spectroscopy reveals that ethyl esters interact with proteins in wine.

    Science.gov (United States)

    Di Gaspero, Mattia; Ruzza, Paolo; Hussain, Rohanah; Vincenzi, Simone; Biondi, Barbara; Gazzola, Diana; Siligardi, Giuliano; Curioni, Andrea

    2017-02-15

    Impairment of wine aroma after vinification is frequently associated to bentonite treatments and this can be the result of protein removal, as recently demonstrated for ethyl esters. To evaluate the existence of an interaction between wine proteins and ethyl esters, the effects induced by these fermentative aroma compounds on the secondary structure and stability of VVTL1, a Thaumatin-like protein purified from wine, was analyzed by Synchrotron Radiation Circular Dichroism (SRCD) spectroscopy. The secondary structure of wine VVTL1 was not strongly affected by the presence of selected ethyl esters. In contrast, VVTL1 stability was slightly increased by the addition of ethyl-octanoate, -decanoate and -dodecanoate, but decreased by ethyl-hexanoate. This indicates the existence of an interaction between VVTL1 and at least some aroma compounds produced during fermentation. The data suggest that proteins removal from wine by bentonite can result in indirect removal of at least some aroma compounds associated with them.

  20. Identification of proteins that interact with murine cytomegalovirus early protein M112-113 in brain

    Institute of Scientific and Technical Information of China (English)

    WANG Hui; LIU Xing-lou; SHU Sai-nan; HUANG Yong-jian; FANG Feng

    2011-01-01

    Background Murine cytomegalovirus (MCMV) early protein M112-113 is involved in viral DNA replication and believed to play a crucial role in the viral pathogenesis.To investigate the biological function of M112-113 protein in the pathogenesis of the brain disorders caused by cytomegalovirus (CMV),a screening for proteins interacting with M112-113 was performed by a yeast two-hybrid system.Methods Bait plasmid pGBKT7-M112-113 was constructed and transformed into AH109 yeast.After confirmation of the expression of MCMV M112-113 in yeast,the bait yeast was mated with a prey yeast containing mouse brain cDNA library plasmid to screen the proteins interacting with M112-113.Interactions between M112-113 and the obtained proteins were verified by yeast two-hybrid assay and chemiluminescent co-immunoprecipitaion.Results Two proteins interacting with M112-113 were identified,including metastasis-associated 1 (MTA1) and zinc finger,CCHC domain containing 18 (ZCCHC18).M112-113 protein could interact with MTA1 or ZCCHC18 in yeast and mammalian cells.Conclusion The interactions of M112-113 with MTA1 or ZCCHC18 may be related to the pathogenesis of MCMV-associated disease in central nervous system.

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

    Directory of Open Access Journals (Sweden)

    Fernández-Recio Juan

    2008-10-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    D’Agostino, Tommaso [Dipartimento di Fisica, Università di Palermo, Via Archirafi 36, 90123 Palermo (Italy); Solana, José Ramón [Departamento de Física Aplicada, Universidad de Cantabria, 39005 Santander (Spain); Emanuele, Antonio, E-mail: antonio.emanuele@unipa.it [Dipartimento di Fisica, Università di Palermo, Via Archirafi 36, 90123 Palermo (Italy)

    2013-10-16

    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. An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology

    Directory of Open Access Journals (Sweden)

    Jain Shobhit

    2010-11-01

    Full Text Available Abstract Background Semantic similarity measures are useful to assess the physiological relevance of protein-protein interactions (PPIs. They quantify similarity between proteins based on their function using annotation systems like the Gene Ontology (GO. Proteins that interact in the cell are likely to be in similar locations or involved in similar biological processes compared to proteins that do not interact. Thus the more semantically similar the gene function annotations are among the interacting proteins, more likely the interaction is physiologically relevant. However, most semantic similarity measures used for PPI confidence assessment do not consider the unequal depth of term hierarchies in different classes of cellular location, molecular function, and biological process ontologies of GO and thus may over-or under-estimate similarity. Results We describe an improved algorithm, Topological Clustering Semantic Similarity (TCSS, to compute semantic similarity between GO terms annotated to proteins in interaction datasets. Our algorithm, considers unequal depth of biological knowledge representation in different branches of the GO graph. The central idea is to divide the GO graph into sub-graphs and score PPIs higher if participating proteins belong to the same sub-graph as compared to if they belong to different sub-graphs. Conclusions The TCSS algorithm performs better than other semantic similarity measurement techniques that we evaluated in terms of their performance on distinguishing true from false protein interactions, and correlation with gene expression and protein families. We show an average improvement of 4.6 times the F1 score over Resnik, the next best method, on our Saccharomyces cerevisiae PPI dataset and 2 times on our Homo sapiens PPI dataset using cellular component, biological process and molecular function GO annotations.

  4. Preferential Interactions and the Effect of Protein PEGylation.

    Directory of Open Access Journals (Sweden)

    Louise Stenstrup Holm

    Full Text Available PEGylation is a strategy used by the pharmaceutical industry to prolong systemic circulation of protein drugs, whereas formulation excipients are used for stabilization of proteins during storage. Here we investigate the role of PEGylation in protein stabilization by formulation excipients that preferentially interact with the protein.The model protein hen egg white lysozyme was doubly PEGylated on two lysines with 5 kDa linear PEGs (mPEG-succinimidyl valerate, MW 5000 and studied in the absence and presence of preferentially excluded sucrose and preferentially bound guanine hydrochloride. Structural characterization by far- and near-UV circular dichroism spectroscopy was supplemented by investigation of protein thermal stability with the use of differential scanning calorimetry, far and near-UV circular dichroism and fluorescence spectroscopy. It was found that PEGylated lysozyme was stabilized by the preferentially excluded excipient and destabilized by the preferentially bound excipient in a similar manner as lysozyme. However, compared to lysozyme in all cases the melting transition was lower by up to a few degrees and the calorimetric melting enthalpy was decreased to half the value for PEGylated lysozyme. The ratio between calorimetric and van't Hoff enthalpy suggests that our PEGylated lysozyme is a dimer.The PEGylated model protein displayed similar stability responses to the addition of preferentially active excipients. This suggests that formulation principles using preferentially interacting excipients are similar for PEGylated and non-PEGylated proteins.

  5. Structures of multidomain proteins adsorbed on hydrophobic interaction chromatography surfaces.

    Science.gov (United States)

    Gospodarek, Adrian M; Sun, Weitong; O'Connell, John P; Fernandez, Erik J

    2014-12-05

    In hydrophobic interaction chromatography (HIC), interactions between buried hydrophobic residues and HIC surfaces can cause conformational changes that interfere with separations and cause yield losses. This paper extends our previous investigations of protein unfolding in HIC chromatography by identifying protein structures on HIC surfaces under denaturing conditions and relating them to solution behavior. The thermal unfolding of three model multidomain proteins on three HIC surfaces of differing hydrophobicities was investigated with hydrogen exchange mass spectrometry (HXMS). The data were analyzed to obtain unfolding rates and Gibbs free energies for unfolding of adsorbed proteins. The melting temperatures of the proteins were lowered, but by different amounts, on the different surfaces. In addition, the structures of the proteins on the chromatographic surfaces were similar to the partially unfolded structures produced in the absence of a surface by temperature as well as by chemical denaturants. Finally, it was found that patterns of residue exposure to solvent on different surfaces at different temperatures can be largely superimposed. These findings suggest that protein unfolding on various HIC surfaces might be quantitatively related to protein unfolding in solution and that details of surface unfolding behavior might be generalized.

  6. Detection of interaction between biomineralising proteins and calcium carbonate microcrystals

    Directory of Open Access Journals (Sweden)

    Hanna Rademaker

    2011-04-01

    Full Text Available The natural composite nacre is characterised by astonishing mechanical properties, although the main constituent is a brittle mineral shaped as tablets interdispersed by organic layers. To mimic the natural formation process which takes place at ambient conditions an understanding of the mechanism responsible for a defined microstructure of nacre is necessary. Since proteins are assumed to be involved in this mechanism, it is advantageous to identify distinct proteins interacting with minerals from the totality of proteins contained in nacre. Here, we adopted and modified a recently published approach given by Suzuki et al. that gives a hint of specific protein–mineral interactions. Synthesised aragonite or calcite microcrystals were incubated with a protein mixture extracted from nacre of Haliotis laevigata. After incubation the mineral phase was dissolved and investigated for attached proteins. The results give a hint of one protein that seems to bind specifically to aragonite and not to calcite. The presented protocol seems to be suitable to detect mineral binding proteins quickly and therefore can point to proteins whose mineral binding capabilities should be investigated further.

  7. 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.

  8. Structure and expression of a novel compact myelin protein – Small VCP-interacting protein (SVIP)

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Jiawen [Department of Neurology, Vanderbilt University School of Medicine (United States); Peng, Dungeng [Department of Biochemistry, Vanderbilt University School of Medicine (United States); Voehler, Markus [Center for Structural Biology, Vanderbilt University (United States); Sanders, Charles R. [Department of Biochemistry, Vanderbilt University School of Medicine (United States); Center for Structural Biology, Vanderbilt University (United States); Li, Jun, E-mail: jun.li.2@vanderbilt.edu [Department of Neurology, Vanderbilt University School of Medicine (United States); Tennessee Valley Healthcare System (TVHS) – Nashville VA (United States)

    2013-10-11

    Highlights: •SVIP (small p97/VCP-interacting protein) co-localizes with myelin basic protein (MBP) in compact myelin. •We determined that SVIP is an intrinsically disordered protein (IDP). •The helical content of SVIP increases dramatically during its interaction with negatively charged lipid membrane. •This study provides structural insight into interactions between SVIP and myelin membranes. -- Abstract: SVIP (small p97/VCP-interacting protein) was initially identified as one of many cofactors regulating the valosin containing protein (VCP), an AAA+ ATPase involved in endoplasmic-reticulum-associated protein degradation (ERAD). Our previous study showed that SVIP is expressed exclusively in the nervous system. In the present study, SVIP and VCP were seen to be co-localized in neuronal cell bodies. Interestingly, we also observed that SVIP co-localizes with myelin basic protein (MBP) in compact myelin, where VCP was absent. Furthermore, using nuclear magnetic resonance (NMR) and circular dichroism (CD) spectroscopic measurements, we determined that SVIP is an intrinsically disordered protein (IDP). However, upon binding to the surface of membranes containing a net negative charge, the helical content of SVIP increases dramatically. These findings provide structural insight into interactions between SVIP and myelin membranes.

  9. Interaction of plasma proteins with commercial protein repellent polyvinyl chloride (PVC): a word of caution.

    Science.gov (United States)

    De Somer, F; Van Landschoot, A; Van Nooten, G; Delanghe, J

    2008-07-01

    Protein adsorption onto polymers remains a problem. In recent years, several protein-repellent PVC tubings have been developed. Although several studies report the interaction between plasma coagulation proteins and PVC, few address the interaction with other plasma proteins. Two commercial brands of untreated medical grade PVC tubing, phosphorylcholine-coated PVC tubing, triblock-copolymer (polycaprolactone-polydimethylsiloxane-polycaprolactone)-treated PVC tubing and poly-2-methoxyethylacrylate (PMEA)-coated tubing were exposed for 60 minutes to human plasma. A broad spectrum of plasma proteins was found on all tubing. The adsorbed albumin to total protein ratio is lower than the similar ratio in plasma while alpha1 and alpha2 globulins are over-represented in the protein spectrum. On PMEA tubing, not only alpha globulins, but also beta and gamma globulins, are found in high concentrations in the adsorbed protein. PMEA tubing and uncoated PVC tubing of brand B had a higher amount of protein adsorbed compared against all other tubing (p < 0.05). There were no statistical differences in protein adsorption between the triblock-copolymer-treated tubing, the phosphorylcholine-coated tubing and the uncoated PVC tubing of brand A. The average thickness of the protein layer was 23 nm. Plasma protein adsorption still exists on uncoated and protein-repellent tubing and can initiate a systemic inflammatory reaction.

  10. Visualization of host-polerovirus interaction topologies using Protein Interaction Reporter technology

    Science.gov (United States)

    Demonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. The majority of interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll viru...

  11. REVIEW ARTICLE: DNA protein interactions and bacterial chromosome architecture

    Science.gov (United States)

    Stavans, Joel; Oppenheim, Amos

    2006-12-01

    Bacteria, like eukaryotic organisms, must compact the DNA molecule comprising their genome and form a functional chromosome. Yet, bacteria do it differently. A number of factors contribute to genome compaction and organization in bacteria, including entropic effects, supercoiling and DNA-protein interactions. A gamut of new experimental techniques have allowed new advances in the investigation of these factors, and spurred much interest in the dynamic response of the chromosome to environmental cues, segregation, and architecture, during both exponential and stationary phases. We review these recent developments with emphasis on the multifaceted roles that DNA-protein interactions play.

  12. Analyzing protein-ligand interactions by dynamic NMR spectroscopy.

    Science.gov (United States)

    Mittermaier, Anthony; Meneses, Erick

    2013-01-01

    Nuclear magnetic resonance (NMR) spectroscopy can provide detailed information on protein-ligand interactions that is inaccessible using other biophysical techniques. This chapter focuses on NMR-based approaches for extracting affinity and rate constants for weakly binding transient protein complexes with lifetimes of less than about a second. Several pulse sequences and analytical techniques are discussed, including line-shape simulations, spin-echo relaxation dispersion methods (CPMG), and magnetization exchange (EXSY) experiments.

  13. Protein-lipid interactions in bilayer membranes: a lattice model.

    Science.gov (United States)

    Pink, D A; Chapman, D

    1979-04-01

    A lattice model has been developed to study the effects of intrinsic membrane proteins upon the thermodynamic properties of a lipid bilayer membrane. We assume that only nearest-neighbor van der Waals and steric interactions are important and that the polar group interactions can be represented by effective pressure-area terms. Phase diagrams, the temperature T(0), which locates the gel-fluid melting, the transition enthalpy, and correlations were calculated by mean field and cluster approximations. Average lipid chain areas and chain areas when the lipid is in a given protein environment were obtained. Proteins that have a "smooth" homogeneous surface ("cholesterol-like") and those that have inhomogeneous surfaces or that bind lipids specifically were considered. We find that T(0) can vary depending upon the interactions and that another peak can appear upon the shoulder of the main peak which reflects the melting of a eutectic mixture. The transition enthalpy decreases generally, as was found before, but when a second peak appears departures from this behavior reflect aspects of the eutectic mixture. We find that proteins have significant nonzero probabilities for being adjacent to one another so that no unbroken "annulus" of lipid necessarily exists around a protein. If T(0) does not increase much, or decreases, with increasing c, then lipids adjacent to a protein cannot all be all-trans on the time scale (10(-7) sec) of our system. Around a protein the lipid correlation depth is about one lipid layer, and this increases with c. Possible consequences of ignoring changes in polar group interactions due to clustering of proteins are discussed.

  14. Screening and identification of proteins interacting with nucleostemin

    Institute of Scientific and Technical Information of China (English)

    Hai-Xia Yang; Geng-Lin Jin; Ling Meng; Jian-Zhi Zhang; Wen-Bin Liu; Cheng-Chao Shou

    2005-01-01

    AIM: To identify the proteins interacting with nucleostemin (NS), thereby gaining an insight into the function of NS.METHODS: Yeast two-hybrid assay was performed to screen a human placenta cDNA library with the full length of NS as a bait. X-Gal assay and β-galactosidase filter assay were subsequently conducted to check the positive clones and the gene was identified by DNA sequencing.To further confirm the interaction of two proteins, the DNA fragment coding NS and the DNA fragment isolated from the positive clone were inserted into the mammalian expression vector pcDNA3 and pcDNA3-myc, respectively.Then, two plasmids were cotransfected into the COS-7 cells by DEAE-dextron. The total protein from the cotransfected cells was extracted and coimmunoprecipitation and Western blot were performed with suitable antibodies sequentially.RESULTS: Two positive clones that interacted with NS were obtained from human placenta cDNA library. One was an alpha isoform of human protein phosphatase 2 regulatory subunit B (B56) (PPP2R5A) and the other was a novel gene being highly homologous to the gene associated with spondylo paralysis. The co-immunoprecipitation also showed that NS specifically interacted with PPP2R5A.CONCLUSION: NS and PPP2R5A interact in yeast and mammalian cells, respectively, which is helpful for addressing the function of NS in cancer development and progression.

  15. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2008-01-01

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

  17. Comparison of tryptophan interactions to free and grafted BSA protein.

    Science.gov (United States)

    Garnier, F; Randon, J; Rocca, J L

    2000-04-28

    The binding of d- and l-tryptophan molecules to bovine serum albumin (BSA) protein has been studied using liquid chromatography and ultrafiltration in the pH range from 7 to 11. A hydrophobic interaction between tryptophan and BSA has been observed at pH 7.0 on BSA grafted chromatographic column. However, this interaction is negligible at higher pH for which the interaction to the stereospecific site was predominant. For both grafted and free proteins, the complexation mechanism was a competitive binding of d- and l-enantiomers on a single site. The apparent complexation constants for both d- and l-tryptophan show a maximum in the pH range 9-10. The variations of the apparent complexation constants versus pH were the result of the protonation of both the amino acid and a single site of the protein assuming that the complexation occurs between the zwitter-ionic amino acid form and the unprotonated BSA site. The apparent pK(BSA) is slightly shifted from 8.3 for grafted BSA protein to 9.4 for free BSA protein. This shift is presumably as a result of the different protein conformation.

  18. Evolutionary pressure on the topology of protein interface interaction networks.

    Science.gov (United States)

    Johnson, Margaret E; Hummer, Gerhard

    2013-10-24

    The densely connected structure of protein-protein interaction (PPI) networks reflects the functional need of proteins to cooperate in cellular processes. However, PPI networks do not adequately capture the competition in protein binding. By contrast, the interface interaction network (IIN) studied here resolves the modular character of protein-protein binding and distinguishes between simultaneous and exclusive interactions that underlie both cooperation and competition. We show that the topology of the IIN is under evolutionary pressure, and we connect topological features of the IIN to specific biological functions. To reveal the forces shaping the network topology, we use a sequence-based computational model of interface binding along with network analysis. We find that the more fragmented structure of IINs, in contrast to the dense PPI networks, arises in large part from the competition between specific and nonspecific binding. The need to minimize nonspecific binding favors specific network motifs, including a minimal number of cliques (i.e., fully connected subgraphs) and many disconnected fragments. Validating the model, we find that these network characteristics are closely mirrored in the IIN of clathrin-mediated endocytosis. Features unexpected on the basis of our motif analysis are found to indicate either exceptional binding selectivity or important regulatory functions.

  19. Membrane interacting regions of Dengue virus NS2A protein.

    Science.gov (United States)

    Nemésio, Henrique; Villalaín, José

    2014-08-28

    The Dengue virus (DENV) NS2A protein, essential for viral replication, is a poorly characterized membrane protein. NS2A displays both protein/protein and membrane/protein interactions, yet neither its functions in the viral cycle nor its active regions are known with certainty. To highlight the different membrane-active regions of NS2A, we characterized the effects of peptides derived from a peptide library encompassing this protein's full length on different membranes by measuring their membrane leakage induction and modulation of lipid phase behavior. Following this initial screening, one region, peptide dens25, had interesting effects on membranes; therefore, we sought to thoroughly characterize this region's interaction with membranes. This peptide presents an interfacial/hydrophobic pattern characteristic of a membrane-proximal segment. We show that dens25 strongly interacts with membranes that contain a large proportion of lipid molecules with a formal negative charge, and that this effect has a major electrostatic contribution. Considering its membrane modulating capabilities, this region might be involved in membrane rearrangements and thus be important for the viral cycle.

  20. Potential interaction between warfarin and high dietary protein intake.

    Science.gov (United States)

    Hornsby, Lori B; Hester, E Kelly; Donaldson, Amy R

    2008-04-01

    A 55-year-old Caucasian man was receiving warfarin therapy after undergoing aortic valve replacement. His international normalized ratio (INR) was stabilized with warfarin 95 mg/week for 5 weeks. Commencement of a low-carbohydrate, high-protein diet resulted in a series of subtherapeutic INRs that led to a 16% increase in the dosage requirement to maintain therapeutic INRs. After the patient discontinued the diet, his INR increased, and several dosage reductions were required until his INR stabilized with his original dosage of 95 mg/week. Two additional case reports have described a possible interaction between warfarin and a high-protein diet. The potential for increased dietary protein intake to raise serum albumin levels and/or cytochrome P450 activity has been postulated as mechanisms for the resulting decrease in INRs. Given the available animal and human data that demonstrate alterations in drug metabolism in the presence of altered dietary protein intake, an increase in warfarin metabolism due to cytochrome P450 activation appears to be the most likely cause. In addition to the previously reported cases, this case indicates a potential interaction between warfarin and a high-protein diet. Because of the popularity of high-protein diets and because of the risks associated with inadequate or excessive warfarin anticoagulation, patients and health care providers should be aware of this interaction to ensure appropriate monitoring when warranted.

  1. A coarse grain model for protein-surface interactions

    Science.gov (United States)

    Wei, Shuai; Knotts, Thomas A.

    2013-09-01

    The interaction of proteins with surfaces is important in numerous applications in many fields—such as biotechnology, proteomics, sensors, and medicine—but fundamental understanding of how protein stability and structure are affected by surfaces remains incomplete. Over the last several years, molecular simulation using coarse grain models has yielded significant insights, but the formalisms used to represent the surface interactions have been rudimentary. We present a new model for protein surface interactions that incorporates the chemical specificity of both the surface and the residues comprising the protein in the context of a one-bead-per-residue, coarse grain approach that maintains computational efficiency. The model is parameterized against experimental adsorption energies for multiple model peptides on different types of surfaces. The validity of the model is established by its ability to quantitatively and qualitatively predict the free energy of adsorption and structural changes for multiple biologically-relevant proteins on different surfaces. The validation, done with proteins not used in parameterization, shows that the model produces remarkable agreement between simulation and experiment.

  2. Yeast Interacting Proteins Database: YER179W, YER179W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YER179W DMC1 Meiosis-specific protein required for repair of double-strand breaks and pairing... double-strand breaks and pairing between homologous chromosomes; homolog of Rad5...ific protein required for repair of double-strand breaks and pairing between homo...name DMC1 Prey description Meiosis-specific protein required for repair of double-strand breaks and pairin

  3. Protein-spanning water networks and implications for prediction of protein-protein interactions mediated through hydrophobic effects.

    Science.gov (United States)

    Cui, Di; Ou, Shuching; Patel, Sandeep

    2014-12-01

    Hydrophobic effects, often conflated with hydrophobic forces, are implicated as major determinants in biological association and self-assembly processes. Protein-protein interactions involved in signaling pathways in living systems are a prime example where hydrophobic effects have profound implications. In the context of protein-protein interactions, a priori knowledge of relevant binding interfaces (i.e., clusters of residues involved directly with binding interactions) is difficult. In the case of hydrophobically mediated interactions, use of hydropathy-based methods relying on single residue hydrophobicity properties are routinely and widely used to predict propensities for such residues to be present in hydrophobic interfaces. However, recent studies suggest that consideration of hydrophobicity for single residues on a protein surface require accounting of the local environment dictated by neighboring residues and local water. In this study, we use a method derived from percolation theory to evaluate spanning water networks in the first hydration shells of a series of small proteins. We use residue-based water density and single-linkage clustering methods to predict hydrophobic regions of proteins; these regions are putatively involved in binding interactions. We find that this simple method is able to predict with sufficient accuracy and coverage the binding interface residues of a series of proteins. The approach is competitive with automated servers. The results of this study highlight the importance of accounting of local environment in determining the hydrophobic nature of individual residues on protein surfaces.

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

    DEFF Research Database (Denmark)

    Feliu, Elisenda; Aloy, Patrick; Oliva, Baldo

    2011-01-01

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

  5. Yeast Interacting Proteins Database: YKL103C, YKL103C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available he peptidase family M18; often used as a marker protein in studies of autophagy and cytosol to vacuole targe...; often used as a marker protein in studies of autophagy and cytosol to vacuole targeting (CVT) pathway Rows...e yscI; zinc metalloproteinase that belongs to the peptidase family M18; often used as a marker protein in studies...t belongs to the peptidase family M18; often used as a marker protein in studies of autophagy and cytosol to

  6. Yeast Interacting Proteins Database: YPL003W, YPR066W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YPL003W ULA1 Protein that acts together with Uba3p to activate Rub1p before its con... (1) Rows with this bait as prey (0) YPR066W UBA3 Protein that acts together with Ula1p to activate Rub1p before...it gene name ULA1 Bait description Protein that acts together with Uba3p to activate Rub1p before...together with Ula1p to activate Rub1p before its conjugation to proteins (neddylation), which may play a rol

  7. A quantitative approach to study indirect effects among disease proteins in the human protein interaction network

    Directory of Open Access Journals (Sweden)

    Jordán Ferenc

    2010-07-01

    Full Text Available Abstract Background Systems biology makes it possible to study larger and more intricate systems than before, so it is now possible to look at the molecular basis of several diseases in parallel. Analyzing the interaction network of proteins in the cell can be the key to understand how complex processes lead to diseases. Novel tools in network analysis provide the possibility to quantify the key interacting proteins in large networks as well as proteins that connect them. Here we suggest a new method to study the relationships between topology and functionality of the protein-protein interaction network, by identifying key mediator proteins possibly maintaining indirect relationships among proteins causing various diseases. Results Based on the i2d and OMIM databases, we have constructed (i a network of proteins causing five selected diseases (DP, disease proteins plus their interacting partners (IP, non-disease proteins, the DPIP network and (ii a protein network showing only these IPs and their interactions, the IP network. The five investigated diseases were (1 various cancers, (2 heart diseases, (3 obesity, (4 diabetes and (5 autism. We have quantified the number and strength of IP-mediated indirect effects between the five groups of disease proteins and hypothetically identified the most important mediator proteins linking heart disease to obesity or diabetes in the IP network. The results present the relationship between mediator role and centrality, as well as between mediator role and functional properties of these proteins. Conclusions We show that a protein which plays an important indirect mediator role between two diseases is not necessarily a hub in the PPI network. This may suggest that, even if hub proteins and disease proteins are trivially of great interest, mediators may also deserve more attention, especially if disease-disease associations are to be understood. Identifying the hubs may not be sufficient to understand

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

    Directory of Open Access Journals (Sweden)

    Alexandra Traister

    2016-06-01

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

  9. Key interactions of surfactants in therapeutic protein formulations: A review.

    Science.gov (United States)

    Khan, Tarik A; Mahler, Hanns-Christian; Kishore, Ravuri S K

    2015-11-01

    Proteins as amphiphilic, surface-active macromolecules, demonstrate substantial interfacial activity, which causes considerable impact on their multifarious applications. A commonly adapted measure to prevent interfacial damage to proteins is the use of nonionic surfactants. Particularly in biotherapeutic formulations, the use of nonionic surfactants is ubiquitous in order to prevent the impact of interfacial stress on drug product stability. The scope of this review is to convey the current understanding of interactions of nonionic surfactants with proteins both at the interface and in solution, with specific focus to their effects on biotherapeutic formulations.

  10. 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

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

    Directory of Open Access Journals (Sweden)

    Jessica B Hostetler

    2015-12-01

    Full Text Available A vaccine targeting Plasmodium vivax will be an essential component of any comprehensive malaria elimination program, but major gaps in our understanding of P. vivax biology, including the protein-protein interactions that mediate merozoite invasion of reticulocytes, hinder the search for candidate antigens. Only one ligand-receptor interaction has been identified, that between P. vivax Duffy Binding Protein (PvDBP and the erythrocyte Duffy Antigen Receptor for Chemokines (DARC, and strain-specific immune responses to PvDBP make it a complex vaccine target. To broaden the repertoire of potential P. vivax merozoite-stage vaccine targets, we exploited a recent breakthrough in expressing full-length ectodomains of Plasmodium proteins in a functionally-active form in mammalian cells and initiated a large-scale study of P. vivax merozoite proteins that are potentially involved in reticulocyte binding and invasion.We selected 39 P. vivax proteins that are predicted to localize to the merozoite surface or invasive secretory organelles, some of which show homology to P. falciparum vaccine candidates. Of these, we were able to express 37 full-length protein ectodomains in a mammalian expression system, which has been previously used to express P. falciparum invasion ligands such as PfRH5. To establish whether the expressed proteins were correctly folded, we assessed whether they were recognized by antibodies from Cambodian patients with acute vivax malaria. IgG from these samples showed at least a two-fold change in reactivity over naïve controls in 27 of 34 antigens tested, and the majority showed heat-labile IgG immunoreactivity, suggesting the presence of conformation-sensitive epitopes and native tertiary protein structures. Using a method specifically designed to detect low-affinity, extracellular protein-protein interactions, we confirmed a predicted interaction between P. vivax 6-cysteine proteins P12 and P41, further suggesting that the proteins

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

    Energy Technology Data Exchange (ETDEWEB)

    Weiss, Shimon

    2006-08-30

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

  13. Analysis of protein interactions at native chloroplast membranes by ellipsometry.

    Directory of Open Access Journals (Sweden)

    Verena Kriechbaumer

    Full Text Available Membrane bound receptors play vital roles in cell signaling, and are the target for many drugs, yet their interactions with ligands are difficult to study by conventional techniques due to the technical difficulty of monitoring these interactions in lipid environments. In particular, the ability to analyse the behaviour of membrane proteins in their native membrane environment is limited. Here, we have developed a quantitative approach to detect specific interactions between low-abundance chaperone receptors within native chloroplast membranes and their soluble chaperone partners. Langmuir-Schaefer film deposition was used to deposit native chloroplasts onto gold-coated glass slides, and interactions between the molecular chaperones Hsp70 and Hsp90 and their receptors in the chloroplast membranes were detected and quantified by total internal reflection ellipsometry (TIRE. We show that native chloroplast membranes deposited on gold-coated glass slides using Langmuir-Schaefer films retain functional receptors capable of binding chaperones with high specificity and affinity. Taking into account the low chaperone receptor abundance in native membranes, these binding properties are consistent with data generated using soluble forms of the chloroplast chaperone receptors, OEP61 and Toc64. Therefore, we conclude that chloroplasts have the capacity to selectively bind chaperones, consistent with the notion that chaperones play an important role in protein targeting to chloroplasts. Importantly, this method of monitoring by TIRE does not require any protein labelling. This novel combination of techniques should be applicable to a wide variety of membranes and membrane protein receptors, thus presenting the opportunity to quantify protein interactions involved in fundamental cellular processes, and to screen for drugs that target membrane proteins.

  14. 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.

  15. Yeast Interacting Proteins Database: YML064C, YBR072W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available th this bait as prey (0) YBR072W HSP26 Small heat shock protein (sHSP) with chaperone activity; forms hollow...tein (sHSP) with chaperone activity; forms hollow, sphere-shaped oligomers that suppress unfolded proteins a

  16. Yeast Interacting Proteins Database: YJR091C, YDL013W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...h mRNAs encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; ov

  17. Yeast Interacting Proteins Database: YJR091C, YHR026W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpre...s with mRNAs encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondri

  18. Yeast Interacting Proteins Database: YPL002C, YLR417W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ndent sorting of proteins into the endosome; appears to be functionally related to SNF7; involved in glucose...ESCRT-II complex, which is involved in ubiquitin-dependent sorting of proteins into the endosome; appears to be functionally

  19. Yeast Interacting Proteins Database: YOR111W, YDL161W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available with this bait as prey (2) YDL161W ENT1 Epsin-like protein involved in endocytosis and actin patch assembly and functionally...-like protein involved in endocytosis and actin patch assembly and functionally redundant with Ent2p; binds

  20. Yeast Interacting Proteins Database: YKR007W, YGR163W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available (1) YGR163W GTR2 Putative GTP binding protein that negatively regulates Ran/Tc4 ...163W Prey gene name GTR2 Prey description Putative GTP binding protein that negatively regulates Ran/Tc4 GTP

  1. Yeast Interacting Proteins Database: YNL189W, YDR510W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ait as prey (0) YDR510W SMT3 Ubiquitin-like protein of the SUMO family, conjugated...s prey (0) Prey ORF YDR510W Prey gene name SMT3 Prey description Ubiquitin-like protein of the SUMO family, conjugated

  2. Yeast Interacting Proteins Database: YKL043W, YDR510W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available s prey (0) YDR510W SMT3 Ubiquitin-like protein of the SUMO family, conjugated to ... Prey ORF YDR510W Prey gene name SMT3 Prey description Ubiquitin-like protein of the SUMO family, conjugated

  3. Yeast Interacting Proteins Database: YHR180W, YDL100C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available on Dubious open reading frame unlikely to encode a protein, based on available experimental and comparativ...YHR180W - Dubious open reading frame unlikely to encode a protein, based on available experimental and compa...rative sequence data Rows with this bait as bait (1) Rows with this bait as prey (0

  4. Yeast Interacting Proteins Database: YDR271C, YOR128C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available n Dubious open reading frame unlikely to encode a protein, based on available experimental and comparative...YDR271C - Dubious open reading frame unlikely to encode a protein, based on available experimental and compa...rative sequence data; partially overlaps the verified ORF CCC2/YDR270W Rows with th

  5. Yeast Interacting Proteins Database: YER081W, YDR194C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YDR194C MSS116 DEAD-box protein required for efficient splicing of mitochondrial Group I and II introns; non...e name MSS116 Prey description DEAD-box protein required for efficient splicing of mitochondrial Group I and

  6. Yeast Interacting Proteins Database: YNL189W, YKL103C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available that belongs to the peptidase family M18; often used as a marker protein in studies of autophagy and cytoso...amily M18; often used as a marker protein in studies of autophagy and cytosol to vacuole targeting (CVT) pat

  7. Yeast Interacting Proteins Database: YJL070C, YDR504C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available he authentic, non-tagged protein is detected in highly purified mitochondria in high-throughput studies; YJL...c, non-tagged protein is detected in highly purified mitochondria in high-throughput studies; YJL070C is a n

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

    Lifescience Database Archive (English)

    Full Text Available that belongs to the peptidase family M18; often used as a marker protein in studies of autophagy and cytosol...ily M18; often used as a marker protein in studies of autophagy and cytosol to vacuole targeting (CVT) pathw

  9. Yeast Interacting Proteins Database: YPL105C, YDR429C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available co-purification experiments; authentic, non-tagged protein is detected in highly purified mitochondria in high-throughput studies...entic, non-tagged protein is detected in highly purified mitochondria in high-throughput studies Rows with t

  10. Yeast Interacting Proteins Database: YDR311W, YKL103C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ngs to the peptidase family M18; often used as a marker protein in studies of aut...ase that belongs to the peptidase family M18; often used as a marker protein in studies of autophagy and cyt

  11. Yeast Interacting Proteins Database: YMR025W, YGR120C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available or removal of the ubiquitin-like protein Rub1p from Cdc53p (cullin); involved in adaptation to pheromone sig... (cullin); involved in adaptation to pheromone signaling Rows with this bait as bait Rows with this bait as ...signalosome, which is required for deneddylation, or removal of the ubiquitin-like protein Rub1p from Cdc53p

  12. Yeast Interacting Proteins Database: YKR100C, YDL100C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YKR100C SKG1 Transmembrane protein with a role in cell wall polymer composition; lo...position; localizes on the inner surface of the plasma membrane at the bud and in t...RF YKR100C Bait gene name SKG1 Bait description Transmembrane protein with a role in cell wall polymer com

  13. Yeast Interacting Proteins Database: YDL089W, YDR233C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available rDNA repeat stability; null mutant causes increase in unequal sister-chromatid exchange; GFP-fusion protein...DNA repeat stability; null mutant causes increase in unequal sister-chromatid exchange; GFP-fusion protein l

  14. Yeast Interacting Proteins Database: YDL089W, YPR028W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available rDNA repeat stability; null mutant causes increase in unequal sister-chromatid exchange; GFP-fusion protein...ility; null mutant causes increase in unequal sister-chromatid exchange; GFP-fusion protein localizes to the

  15. Yeast Interacting Proteins Database: YOR264W, YCR086W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YOR264W DSE3 Daughter cell-specific protein, may help establish daughter fate Rows ...0 42 - Show YOR264W Bait ORF YOR264W Bait gene name DSE3 Bait description Daughter cell-specific protein, ma

  16. Yeast Interacting Proteins Database: YJR091C, YKL002W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpression causes...h mRNAs encoding membrane-associated proteins; involved in localizing the Arp2/3 complex to mitochondria; overexpression causes

  17. Yeast Interacting Proteins Database: YHR129C, YMR294W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YHR129C ARP1 Actin-related protein of the dynactin complex; required for spindle orientation...ein of the dynactin complex; required for spindle orientation and nuclear migrati...PD) 1 Alternative path with 2 intervening proteins (YPD) 2 IST hit 21 IST hit in the opposite bait/prey orientation 7 ...

  18. Yeast Interacting Proteins Database: YJR008W, YHR129C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available the dynactin complex; required for spindle orientation and nuclear migration; put...dynactin complex; required for spindle orientation and nuclear migration; putative ortholog of mammalian cen...ervening protein (YPD) 0 Alternative path with 2 intervening proteins (YPD) 0 IST hit 3 IST hit in the opposite bait/prey orientation - ...

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

    Directory of Open Access Journals (Sweden)

    Thorsten Wille

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

  20. Simple Protein Complex Purification and Identification Method Suitable for High- throughput Mapping of Protein Interaction Networks

    Energy Technology Data Exchange (ETDEWEB)

    Markillie, Lye Meng; Lin, Chiann Tso; Adkins, Joshua N.; Auberry, Deanna L.; Hill, Eric A.; Hooker, Brian S.; Moore, Priscilla A.; Moore, Ronald J.; Shi, Liang; Wiley, H. S.; Kery, Vladimir

    2005-04-11

    Most of the current methods for purification and identification of protein complexes use endogenous expression of affinity tagged bait, tandem affinity tag purification of protein complexes followed by specific elution of complexes from beads, gel separation, in-gel digestion and mass spectrometric analysis of protein interactors. We propose a single affinity tag in vitro pulldown assay with denaturing elution, trypsin digestion in organic solvent and LC ESI MS/MS protein identification using SEQUEST analysis. Our method is simple, easy to scale up and automate thus suitable for high throughput mapping of protein interaction networks and functional proteomics.

  1. Synergistic interactions of lipids and myelin basic protein

    Science.gov (United States)

    Hu, Yufang; Doudevski, Ivo; Wood, Denise; Moscarello, Mario; Husted, Cynthia; Genain, Claude; Zasadzinski, Joseph A.; Israelachvili, Jacob

    2004-09-01

    This report describes force measurements and atomic force microscope imaging of lipid-protein interactions that determine the structure of a model membrane system that closely mimics the myelin sheath. Our results suggest that noncovalent, mainly electrostatic and hydrophobic, interactions are responsible for the multilamellar structure and stability of myelin. We find that myelin basic protein acts as a lipid coupler between two apposed bilayers and as a lipid "hole-filler," effectively preventing defect holes from developing. From our protein-mediated-adhesion and force-distance measurements, we develop a simple quantitative model that gives a reasonably accurate picture of the molecular mechanism and adhesion of bilayer-bridging proteins by means of noncovalent interactions. The results and model indicate that optimum myelin adhesion and stability depend on the difference between, rather than the product of, the opposite charges on the lipid bilayers and myelin basic protein, as well as on the repulsive forces associated with membrane fluidity, and that small changes in any of these parameters away from the synergistically optimum values can lead to large changes in the adhesion or even its total elimination. Our results also show that the often-asked question of which membrane species, the lipids or the proteins, are the "important ones" may be misplaced. Both components work synergistically to provide the adhesion and overall structure. A better appreciation of the mechanism of this synergy may allow for a better understanding of stacked and especially myelin membrane structures and may lead to better treatments for demyelinating diseases such as multiple sclerosis. lipid-protein interactions | myelin membrane structure | membrane adhesion | membrane regeneration/healing | demyelinating diseases

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

    Science.gov (United States)

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

    2012-01-01

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

  4. 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.

  5. 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

  6. 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.

  7. Protein-Phospholipid Interactions in Nonclassical Protein Secretion: Problem and Methods of Study

    Directory of Open Access Journals (Sweden)

    David Neivandt

    2013-02-01

    Full Text Available Extracellular proteins devoid of signal peptides use nonclassical secretion mechanisms for their export. These mechanisms are independent of the endoplasmic reticulum and Golgi. Some nonclassically released proteins, particularly fibroblast growth factors (FGF 1 and 2, are exported as a result of their direct translocation through the cell membrane. This process requires specific interactions of released proteins with membrane phospholipids. In this review written by a cell biologist, a structural biologist and two membrane engineers, we discuss the following subjects: (i Phenomenon of nonclassical protein release and its biological significance; (ii Composition of the FGF1 multiprotein release complex (MRC; (iii The relationship between FGF1 export and acidic phospholipid externalization; (iv Interactions of FGF1 MRC components with acidic phospholipids; (v Methods to study the transmembrane translocation of proteins; (vi Membrane models to study nonclassical protein release.

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

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

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

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

    Science.gov (United States)

    Xu, Wei; Qiao, Kangjian; Tang, Yi

    2013-01-01

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

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

    NARCIS (Netherlands)

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

    2008-01-01

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

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

    Science.gov (United States)

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

    2015-04-01

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

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

    NARCIS (Netherlands)

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

    2002-01-01

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

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

    DEFF Research Database (Denmark)

    Hartmann-Petersen, Rasmus; Gordon, Colin

    2005-01-01

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

  14. A systematic evaluation of protein kinase A-A-kinase anchoring protein interaction motifs

    NARCIS (Netherlands)

    Burgers, Pepijn P; van der Heyden, MAG; Kok, Bart; Heck, Albert J R; Scholten, Arjen

    2015-01-01

    Protein kinase A (PKA) in vertebrates is localized to specific locations in the cell via A-kinase anchoring proteins (AKAPs). The regulatory subunits of the four PKA isoforms (RIα, RIβ, RIIα, and RIIβ) each form a homodimer, and their dimerization domain interacts with a small helical region present

  15. 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 di

  16. Yeast Interacting Proteins Database: YMR139W, YJR094C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available events, activates transcription of early meiotic genes through interaction with Ume6p, degraded by the 26S proteasome following...scription of early meiotic genes through interaction with Ume6p, degraded by the 26S proteasome following ph

  17. Yeast Interacting Proteins Database: YGR218W, YMR124W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ry, cytoplasm, bud, and bud neck; interacts with Crm1p in two-hybrid assay; YMR12...bud, and bud neck; interacts with Crm1p in two-hybrid assay; YMR124W is not an essential gene Rows with this

  18. Yeast Interacting Proteins Database: YLR447C, YDR277C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available uction pathway, required for repression of transcription by Rgt1p; interacts with Rgt1p and the Snf3p and Rgt2p glucose sensors...transduction pathway, required for repression of transcription by Rgt1p; interacts with Rgt1p and the Snf3p and Rgt2p glucose sensors

  19. Yeast Interacting Proteins Database: YGR239C, YDR142C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ing PTS2; interacts with Pex7p; partially redundant with Pex18p Rows with this bait as bait (2) Rows with th...2; interacts with Pex7p; partially redundant with Pex18p Rows with this bait as bait Rows with this bait as

  20. Yeast Interacting Proteins Database: YMR125W, YPL178W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available so contains Sto1p, component of the spliceosomal commitment complex; interacts with Npl3p, possibly to packa...lso contains Sto1p, component of the spliceosomal commitment complex; interacts with Npl3p, possibly to pack