WorldWideScience

Sample records for atrophin interacting protein

  1. Spartin activates atrophin-1-interacting protein 4 (AIP4 E3 ubiquitin ligase and promotes ubiquitination of adipophilin on lipid droplets

    Directory of Open Access Journals (Sweden)

    Shekhtman Alexander

    2010-05-01

    Full Text Available Abstract Background Spartin protein is involved in degradation of epidermal growth factor receptor and turnover of lipid droplets and a lack of expression of this protein is responsible for hereditary spastic paraplegia type 20 (SPG20. Spartin is a multifunctional protein that associates with many cellular organelles, including lipid droplets. Recent studies showed that spartin interacts with E3 ubiquitin ligases that belong to the neural precursor cell-expressed developmentally downregulated gene (Nedd4 family, including atrophin-1-interacting protein 4 (AIP4/ITCH. However, the biological importance of the spartin-AIP4 interaction remains unknown. Results In this study, we show that spartin is not a substrate for AIP4 activity and that spartin's binding to AIP4 significantly increases self-ubiquitination of this E3 ligase, indicating that spartin disrupts the AIP4 autoinhibitory intramolecular interaction. Correspondingly, spartin has a seven times higher binding affinity to the WW region of AIP4 than the binding of the WW region has to the catalytic homologues of the E6-associated protein C-terminus (HECT domain, as measured by enzyme-linked immunosorbent assay. We also show that spartin recruits AIP4 to lipid droplets and promotes ubiquitination of lipid droplet-associated protein, adipophilin, which regulates turnover of lipid droplets. Conclusions Our findings demonstrate that spartin acts as an adaptor protein that activates and recruits AIP4 E3 ubiquitin ligase to lipid droplets and by this means regulates the level of ubiquitination of adipophilin and potentially other lipid-associated proteins. We propose that this is one of the mechanisms by which spartin regulates lipid droplet turnover and might contribute to the pathology of SPG20.

  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

    2017-05-01

    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 over-expressed 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, CBF1/RBPjκ in vertebrate, Su(H) in Drosophila melanogaster, Lag1 in C. elegans, and other coactivators. © 2017 International Society for Neurochemistry.

  3. Tailless and Atrophin control Drosophila aggression by regulating neuropeptide signalling in the pars intercerebralis

    Science.gov (United States)

    Davis, Shaun M.; Thomas, Amanda L.; Nomie, Krystle J.; Huang, Longwen; Dierick, Herman A.

    2014-02-01

    Aggressive behaviour is widespread throughout the animal kingdom. However, its mechanisms are poorly understood, and the degree of molecular conservation between distantly related species is unknown. Here we show that knockdown of tailless (tll) increases aggression in Drosophila, similar to the effect of its mouse orthologue Nr2e1. Tll localizes to the adult pars intercerebralis (PI), which shows similarity to the mammalian hypothalamus. Knockdown of tll in the PI is sufficient to increase aggression and is rescued by co-expressing human NR2E1. Knockdown of Atrophin, a Tll co-repressor, also increases aggression, and both proteins physically interact in the PI. tll knockdown-induced aggression is fully suppressed by blocking neuropeptide processing or release from the PI. In addition, genetically activating PI neurons increases aggression, mimicking the aggression-inducing effect of hypothalamic stimulation. Together, our results suggest that a transcriptional control module regulates neuropeptide signalling from the neurosecretory cells of the brain to control aggressive behaviour.

  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. The bi...

  5. Protein-Protein Interaction Databases

    DEFF Research Database (Denmark)

    Szklarczyk, Damian; Jensen, Lars Juhl

    2015-01-01

    of research are explored. Here we present an overview of the most widely used protein-protein interaction databases and the methods they employ to gather, combine, and predict interactions. We also point out the trade-off between comprehensiveness and accuracy and the main pitfall scientists have to be aware...

  6. Aquaporin Protein-Protein Interactions

    Directory of Open Access Journals (Sweden)

    Jennifer Virginia Roche

    2017-10-01

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

  7. Our interests in protein-protein interactions

    Indian Academy of Sciences (India)

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

  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. Interaction entropy for protein-protein binding

    Science.gov (United States)

    Sun, Zhaoxi; Yan, Yu N.; Yang, Maoyou; Zhang, John Z. H.

    2017-03-01

    Protein-protein interactions are at the heart of signal transduction and are central to the function of protein machine in biology. The highly specific protein-protein binding is quantitatively characterized by the binding free energy whose accurate calculation from the first principle is a grand challenge in computational biology. In this paper, we show how the interaction entropy approach, which was recently proposed for protein-ligand binding free energy calculation, can be applied to computing the entropic contribution to the protein-protein binding free energy. Explicit theoretical derivation of the interaction entropy approach for protein-protein interaction system is given in detail from the basic definition. Extensive computational studies for a dozen realistic protein-protein interaction systems are carried out using the present approach and comparisons of the results for these protein-protein systems with those from the standard normal mode method are presented. Analysis of the present method for application in protein-protein binding as well as the limitation of the method in numerical computation is discussed. Our study and analysis of the results provided useful information for extracting correct entropic contribution in protein-protein binding from molecular dynamics simulations.

  10. Evolution of protein-protein interactions

    Indian Academy of Sciences (India)

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

  11. 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/.

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

    Directory of Open Access Journals (Sweden)

    Carmen Sánchez Claros

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

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

    KAUST Repository

    Sánchez Claros, Carmen

    2012-06-08

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

  14. 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...

  15. 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

  16. 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

  17. 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.

  18. 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

  19. 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

  20. Immunoprecipitation-based analysis of protein-protein interactions.

    Science.gov (United States)

    Speth, Corinna; Toledo-Filho, Luis A A; Laubinger, Sascha

    2014-01-01

    Several techniques allow the detection of protein-protein interactions. In vivo co-immunoprecipitation (Co-IP) studies are an important complement to other commonly used techniques such as yeast two-hybrid or fluorescence complementation, as they reveal interactions between functional proteins at physiological relevant concentrations. Here, we describe an in vivo Co-IP approach using either GFP affinity matrix or specific antibodies to purify proteins of interests and their interacting partners.

  1. Analysis of Protein-Membrane Interactions

    DEFF Research Database (Denmark)

    Kemmer, Gerdi Christine

    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....... Discovered interactions were then probed on the level of the membrane using liposome-based assays. In the second part, a transmembrane protein was investigated. Assays to probe activity of the plasma membrane ATPase (Arabidopsis thaliana H+ -ATPase isoform 2 (AHA2)) in single liposomes using both giant...

  2. 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.

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

    Lifescience Database Archive (English)

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

  4. 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; computational

  5. Building blocks for protein interaction devices

    OpenAIRE

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

    2010-01-01

    Here, we propose a framework for the design of synthetic protein networks from modular protein?protein or protein?peptide interactions and provide a starter toolkit of protein building blocks. Our proof of concept experiments outline a general work flow for part?based protein systems engineering. We streamlined the iterative BioBrick cloning protocol and assembled 25 synthetic multidomain proteins each from seven standardized DNA fragments. A systematic screen revealed two main factors contro...

  6. 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.

  7. NPIDB: nucleic acid?protein interaction database

    OpenAIRE

    Kirsanov, Dmitry D.; Zanegina, Olga N.; Aksianov, Evgeniy A.; Spirin, Sergei A.; Karyagina, Anna S.; Alexeevski, Andrei V

    2012-01-01

    The Nucleic acid?Protein Interaction DataBase (http://npidb.belozersky.msu.ru/) contains information derived from structures of DNA?protein and RNA?protein complexes extracted from the Protein Data Bank (3846 complexes in October 2012). It provides a web interface and a set of tools for extracting biologically meaningful characteristics of nucleoprotein complexes. The content of the database is updated weekly. The current version of the Nucleic acid?Protein Interaction DataBase is an upgrade ...

  8. 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

  9. Chapter 4: Protein interactions and disease.

    Directory of Open Access Journals (Sweden)

    Mileidy W Gonzalez

    Full Text Available Proteins do not function in isolation; it is their interactions with one another and also with other molecules (e.g. DNA, RNA that mediate metabolic and signaling pathways, cellular processes, and organismal systems. Due to their central role in biological function, protein interactions also control the mechanisms leading to healthy and diseased states in organisms. Diseases are often caused by mutations affecting the binding interface or leading to biochemically dysfunctional allosteric changes in proteins. Therefore, protein interaction networks can elucidate the molecular basis of disease, which in turn can inform methods for prevention, diagnosis, and treatment. In this chapter, we will describe the computational approaches to predict and map networks of protein interactions and briefly review the experimental methods to detect protein interactions. We will describe the application of protein interaction networks as a translational approach to the study of human disease and evaluate the challenges faced by these approaches.

  10. 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...

  11. 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...protein of unknown function; computational analysis of large-scale protein-protein interaction data suggests

  12. The protein interaction map of bacteriophage lambda

    Directory of Open Access Journals (Sweden)

    Uetz Peter

    2011-09-01

    Full Text Available Abstract Background Bacteriophage lambda is a model phage for most other dsDNA phages and has been studied for over 60 years. Although it is probably the best-characterized phage there are still about 20 poorly understood open reading frames in its 48-kb genome. For a complete understanding we need to know all interactions among its proteins. We have manually curated the lambda literature and compiled a total of 33 interactions that have been found among lambda proteins. We set out to find out how many protein-protein interactions remain to be found in this phage. Results In order to map lambda's interactions, we have cloned 68 out of 73 lambda open reading frames (the "ORFeome" into Gateway vectors and systematically tested all proteins for interactions using exhaustive array-based yeast two-hybrid screens. These screens identified 97 interactions. We found 16 out of 30 previously published interactions (53%. We have also found at least 18 new plausible interactions among functionally related proteins. All previously found and new interactions are combined into structural and network models of phage lambda. Conclusions Phage lambda serves as a benchmark for future studies of protein interactions among phage, viruses in general, or large protein assemblies. We conclude that we could not find all the known interactions because they require chaperones, post-translational modifications, or multiple proteins for their interactions. The lambda protein network connects 12 proteins of unknown function with well characterized proteins, which should shed light on the functional associations of these uncharacterized proteins.

  13. 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...... interacting and non-interacting protein pairs to classify the structural features that sustain the binding (or non-binding) behavior. Our study indicates that not only the interacting region but also the rest of the protein surface are important for the interaction fate. The interpretation...

  14. 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.

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

    Lifescience Database Archive (English)

    Full Text Available with this bait as prey (0) YGL161C YIP5 Protein that interacts with Rab GTPases, localized to late Golgi vesicles; computational...IP5 Prey description Protein that interacts with Rab GTPases, localized to late Golgi vesicles; computatio...nal analysis of large-scale protein-protein interaction data suggests a possible ro

  16. 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; computational...IP4 Prey description Protein that interacts with Rab GTPases, localized to late Golgi vesicles; computatio...nal analysis of large-scale protein-protein interaction data suggests a possible ro

  17. Mining protein networks for synthetic genetic interactions

    Directory of Open Access Journals (Sweden)

    Zhao Shan

    2008-10-01

    Full Text Available Abstract Background The local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/lethal gene pairs have been extensively identified. Results We design a support vector machine system that uses graph-theoretic properties of two proteins in a protein interaction network as input features for prediction of synthetic sick/lethal interactions. The system is trained on interacting and non-interacting gene pairs culled from large scale genetic screens as well as literature-curated data. We find that the method is capable of predicting synthetic genetic interactions with sensitivity and specificity both exceeding 85%. We further find that the prediction performance is reasonably robust with respect to errors in the protein interaction network and with respect to changes in the features of test datasets. Using the prediction system, we carried out novel predictions of synthetic sick/lethal gene pairs at a genome-wide scale. These pairs appear to have functional properties that are similar to those that characterize the known synthetic lethal gene pairs. Conclusion Our analysis shows that protein interaction networks can be used to predict synthetic lethal interactions with accuracies on par with or exceeding that of other computational methods that use a variety of input features, including functional annotations. This indicates that protein

  18. 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

  19. 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 (

  20. 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....

  1. Role for protein-protein interaction databases in human genetics.

    Science.gov (United States)

    Pattin, Kristine A; Moore, Jason H

    2009-12-01

    Proteomics and the study of protein-protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein-protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein-protein interactions in human genetics and genetic epidemiology. Since protein-protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.

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

    Science.gov (United States)

    Garner, Amanda L; Janda, Kim D

    2011-01-01

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

  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. Novel protein-protein interactions inferred from literature context.

    Directory of Open Access Journals (Sweden)

    Herman H H B M van Haagen

    Full Text Available We have developed a method that predicts Protein-Protein Interactions (PPIs based on the similarity of the context in which proteins appear in literature. This method outperforms previously developed PPI prediction algorithms that rely on the conjunction of two protein names in MEDLINE abstracts. We show significant increases in coverage (76% versus 32% and sensitivity (66% versus 41% at a specificity of 95% for the prediction of PPIs currently archived in 6 PPI databases. A retrospective analysis shows that PPIs can efficiently be predicted before they enter PPI databases and before their interaction is explicitly described in the literature. The practical value of the method for discovery of novel PPIs is illustrated by the experimental confirmation of the inferred physical interaction between CAPN3 and PARVB, which was based on frequent co-occurrence of both proteins with concepts like Z-disc, dysferlin, and alpha-actinin. The relationships between proteins predicted by our method are broader than PPIs, and include proteins in the same complex or pathway. Dependent on the type of relationships deemed useful, the precision of our method can be as high as 90%. The full set of predicted interactions is available in a downloadable matrix and through the webtool Nermal, which lists the most likely interaction partners for a given protein. Our framework can be used for prioritizing potential interaction partners, hitherto undiscovered, for follow-up studies and to aid the generation of accurate protein interaction maps.

  5. 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...tion Proteasome-interacting protein involved in the assembly of the base subcomplex of the 19S proteasomal r

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

    Lifescience Database Archive (English)

    Full Text Available s bait as prey (0) YGL198W YIP4 Protein that interacts with Rab GTPases, localized to late Golgi vesicles; computational...iption Protein that interacts with Rab GTPases, localized to late Golgi vesicles; computational

  7. Yeast Interacting Proteins Database: YOR158W, YLR424W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YOR158W PET123 Mitochondrial ribosomal protein of the small subunit; PET123 exhibits genetic interactions...al ribosomal protein of the small subunit; PET123 exhibits genetic interactions with PET122, which encodes a

  8. 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

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

    Lifescience Database Archive (English)

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

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

    Lifescience Database Archive (English)

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

  11. Yeast Interacting Proteins Database: YNL258C, YLR440C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

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

  12. Yeast Interacting Proteins Database: YNL078W, YKR048C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available Protein localized in the bud neck at G2/M phase; physically interacts with septins; possibly involved in...Protein localized in the bud neck at G2/M phase; physically interacts with septins; possibly involved in

  13. 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

  14. 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

  15. Modeling disordered protein interactions from biophysical principles

    National Research Council Canada - National Science Library

    Peterson, Lenna X; Roy, Amitava; Christoffer, Charles; Terashi, Genki; Kihara, Daisuke

    2017-01-01

    ...-protein interactions (PPIs) are formed with IDPs [3]. A well-known example is the p53 tumor suppressor, which contains disordered regions that interact with dozens of partner proteins [4]. Due to the abundance and characteristic features of IDPs in PPI networks, including many critical signaling pathways, fully understanding the molecular mechanisms of PPI networ...

  16. 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

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

    OpenAIRE

    Susan Khor

    2014-01-01

    Identifying reliable domain-domain interactions will increase our ability to predict novel protein-protein interactions, to unravel interactions in protein complexes, and thus gain more information about the function and behavior of genes. One of the challenges of identifying reliable domain-domain interactions is domain promiscuity. Promiscuous domains are domains that can occur in many domain architectures and are therefore found in many proteins. This becomes a problem for a method where t...

  18. Building blocks for protein interaction devices.

    Science.gov (United States)

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

    2010-05-01

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

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

    NARCIS (Netherlands)

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

    2008-01-01

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

  20. 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…

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

    Lifescience Database Archive (English)

    Full Text Available YGL198W YIP4 Protein that interacts with Rab GTPases, localized to late Golgi vesicles; computational... GTPases, localized to late Golgi vesicles; computational analysis of large-scale protein-protein interactio

  2. 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.

  3. Predicting Protein Interactions by Brownian Dynamics Simulations

    Directory of Open Access Journals (Sweden)

    Xuan-Yu Meng

    2012-01-01

    Full Text Available We present a newly adapted Brownian-Dynamics (BD-based protein docking method for predicting native protein complexes. The approach includes global BD conformational sampling, compact complex selection, and local energy minimization. In order to reduce the computational costs for energy evaluations, a shell-based grid force field was developed to represent the receptor protein and solvation effects. The performance of this BD protein docking approach has been evaluated on a test set of 24 crystal protein complexes. Reproduction of experimental structures in the test set indicates the adequate conformational sampling and accurate scoring of this BD protein docking approach. Furthermore, we have developed an approach to account for the flexibility of proteins, which has been successfully applied to reproduce the experimental complex structure from the structure of two unbounded proteins. These results indicate that this adapted BD protein docking approach can be useful for the prediction of protein-protein interactions.

  4. 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

  5. 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.

  6. Yeast Interacting Proteins Database: YGR239C, YDR142C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available PEX21 Peroxin required for targeting of peroxisomal matrix proteins containing PTS2; interacts with Pex7p;...N-terminal nonapeptide signal (PTS2) of peroxisomal matrix proteins; WD repeat protein; defects in human homolog...description Peroxin required for targeting of peroxisomal matrix proteins containing PTS2; interacts with Pex7p;...N-terminal nonapeptide signal (PTS2) of peroxisomal matrix proteins; WD repeat protein; defects in human homolog

  7. Evolutionarily conserved herpesviral protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Even Fossum

    2009-09-01

    Full Text Available Herpesviruses constitute a family of large DNA viruses widely spread in vertebrates and causing a variety of different diseases. They possess dsDNA genomes ranging from 120 to 240 kbp encoding between 70 to 170 open reading frames. We previously reported the protein interaction networks of two herpesviruses, varicella-zoster virus (VZV and Kaposi's sarcoma-associated herpesvirus (KSHV. In this study, we systematically tested three additional herpesvirus species, herpes simplex virus 1 (HSV-1, murine cytomegalovirus and Epstein-Barr virus, for protein interactions in order to be able to perform a comparative analysis of all three herpesvirus subfamilies. We identified 735 interactions by genome-wide yeast-two-hybrid screens (Y2H, and, together with the interactomes of VZV and KSHV, included a total of 1,007 intraviral protein interactions in the analysis. Whereas a large number of interactions have not been reported previously, we were able to identify a core set of highly conserved protein interactions, like the interaction between HSV-1 UL33 with the nuclear egress proteins UL31/UL34. Interactions were conserved between orthologous proteins despite generally low sequence similarity, suggesting that function may be more conserved than sequence. By combining interactomes of different species we were able to systematically address the low coverage of the Y2H system and to extract biologically relevant interactions which were not evident from single species.

  8. Integrative computational modeling of protein interactions

    NARCIS (Netherlands)

    Garcia Lopes Maia Rodrigues, João; Bonvin, Alexandre M J J

    2014-01-01

    Protein interactions define the homeostatic state of the cell. Our ability to understand these interactions and their role in both health and disease is tied to our knowledge of the 3D atomic structure of the interacting partners and their complexes. Despite advances in experimental method of

  9. 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. Copyright © 2014 Elsevier Inc. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Vladimir Voynov

    2009-12-01

    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.

  11. Mapping Protein-Protein Interactions by Quantitative Proteomics

    DEFF Research Database (Denmark)

    Dengjel, Joern; Kratchmarova, Irina; Blagoev, Blagoy

    2010-01-01

    Proteins exert their function inside a cell generally in multiprotein complexes. These complexes are highly dynamic structures changing their composition over time and cell state. The same protein may thereby fulfill different functions depending on its binding partners. Quantitative mass...... spectrometry (MS)-based proteomics in combination with affinity purification protocols has become the method of choice to map and track the dynamic changes in protein-protein interactions, including the ones occurring during cellular signaling events. Different quantitative MS strategies have been used...... to characterize protein interaction networks. In this chapter we describe in detail the use of stable isotope labeling by amino acids in cell culture (SILAC) for the quantitative analysis of stimulus-dependent dynamic protein interactions....

  12. 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.

  13. 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...plasmic protein containing a zinc finger domain with sequence similarity to that of Type I J-proteins; computational

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

    Science.gov (United States)

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

    2004-01-01

    Isothermal titration calorimetry (ITC) is a powerful technique to study both protein-ligand and protein-protein interactions. This methods chapter is devoted to describing protein-protein interactions, in particular, the association between two different proteins and the self-association of a protein into homodimers. ITC is the only technique that determines directly the thermodynamic parameters of a given reaction: DeltaG, DeltaH, DeltaS, and DeltaCP. Isothermal titration calorimeters have evolved over the years and one of the latest models is the VP-ITC produced by Microcal, Inc. In this chapter we will be describing the general procedure for performing an ITC experiment as well as for the specific cases of porcine pancreatic trypsin binding to soybean trypsin inhibitor and the dissociation of bovine pancreatic alpha-chymotrypsin.

  15. 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; computational...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

  16. 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; computational...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

  17. 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

  18. Van der Waals Interactions Involving Proteins

    Science.gov (United States)

    Roth, Charles M.; Neal, Brian L.; Lenhoff, Abraham M.

    1996-01-01

    Van der Waals (dispersion) forces contribute to interactions of proteins with other molecules or with surfaces, but because of the structural complexity of protein molecules, the magnitude of these effects is usually estimated based on idealized models of the molecular geometry, e.g., spheres or spheroids. The calculations reported here seek to account for both the geometric irregularity of protein molecules and the material properties of the interacting media. Whereas the latter are found to fall in the generally accepted range, the molecular shape is shown to cause the magnitudes of the interactions to differ significantly from those calculated using idealized models. with important consequences. First, the roughness of the molecular surface leads to much lower average interaction energies for both protein-protein and protein-surface cases relative to calculations in which the protein molecule is approximated as a sphere. These results indicate that a form of steric stabilization may be an important effect in protein solutions. Underlying this behavior is appreciable orientational dependence, one reflection of which is that molecules of complementary shape are found to exhibit very strong attractive dispersion interactions. Although this has been widely discussed previously in the context of molecular recognition processes, the broader implications of these phenomena may also be important at larger molecular separations, e.g., in the dynamics of aggregation, precipitation, and crystal growth.

  19. Protein-Protein Interaction Detection: Methods and Analysis

    Directory of Open Access Journals (Sweden)

    V. Srinivasa Rao

    2014-01-01

    Full Text Available Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid, TAP (tandem affinity purification, and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.

  20. 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.

  1. Molecular principles of human virus protein-protein interactions.

    Science.gov (United States)

    Halehalli, Rachita Ramachandra; Nagarajaram, Hampapathalu Adimurthy

    2015-04-01

    Viruses, from the human protein-protein interaction network perspective, target hubs, bottlenecks and interconnected nodes enriched in certain biological pathways. However, not much is known about the general characteristic features of the human proteins interacting with viral proteins (referred to as hVIPs) as well as the motifs and domains utilized by human-virus protein-protein interactions (referred to as Hu-Vir PPIs). Our study has revealed that hVIPs are mostly disordered proteins, whereas viral proteins are mostly ordered proteins. Protein disorder in viral proteins and hVIPs varies from one subcellular location to another. In any given viral-human PPI pair, at least one of the two proteins is structurally disordered suggesting that disorder associated conformational flexibility as one of the characteristic features of virus-host interaction. Further analyses reveal that hVIPs are (i) slowly evolving proteins, (ii) associated with high centrality scores in human-PPI network, (iii) involved in multiple pathways, (iv) enriched in eukaryotic linear motifs (ELMs) associated with protein modification, degradation and regulatory processes, (v) associated with high number of splice variants and (vi) expressed abundantly across multiple tissues. These aforementioned findings suggest that conformational flexibility, spatial diversity, abundance and slow evolution are the characteristic features of the human proteins targeted by viral proteins. Hu-Vir PPIs are mostly mediated via domain-motif interactions (DMIs) where viral proteins employ motifs that mimic host ELMs to bind to domains in human proteins. DMIs are shared among viruses belonging to different families indicating a possible convergent evolution of these motifs to help viruses to adopt common strategies to subvert host cellular pathways. Hu-Vir PPI data, DDI and DMI data for human-virus PPI can be downloaded from http://cdfd.org.in/labpages/computational_biology_datasets.html. Supplementary data are

  2. 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

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

    NARCIS (Netherlands)

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

    2015-01-01

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

  4. 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

  5. Duchenne Muscular Dystrophy (DMD) Protein-Protein Interaction Mapping.

    Science.gov (United States)

    Rezaei Tavirani, Mostafa; OkHOVATIAN, Farshad; Zamanian Azodi, Mona; Rezaei Tavirani, Majid

    2017-01-01

    Duchenne muscular dystrophy (DMD) is one of the mortal diseases, subjected to study in terms of molecular investigation. In this study, the protein interaction map of this muscle-wasting condition was generated to gain a better knowledge of interactome profile of DMD. Applying Cytoscape and String Database, the protein-protein interaction network was constructed and the gene ontology of the constructed network was analyzed for biological process, molecular function, and cellular component annotations. Among 100 proteins related to DMD, dystrophin, utrophin, caveolin 3, and myogenic differentiation 1 play key roles in DMD network. In addition, the gene ontology analysis showed that regulation processes, kinase activity, and sarcoplasmic reticulum were the highlighted biological processes, molecular function, and cell component enrichments respectively for the proteins related to DMD. The central proteins and the enriched ontologies can be suggested as possible prominent agents in DMD; however, the validation studies may be required.

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

    Science.gov (United States)

    Zhang, Zhe; Witham, Shawn; Alexov, Emil

    2011-01-01

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

  7. Iterative cluster analysis of protein interaction data.

    Science.gov (United States)

    Arnau, Vicente; Mars, Sergio; Marín, Ignacio

    2005-02-01

    Generation of fast tools of hierarchical clustering to be applied when distances among elements of a set are constrained, causing frequent distance ties, as happens in protein interaction data. We present in this work the program UVCLUSTER, that iteratively explores distance datasets using hierarchical clustering. Once the user selects a group of proteins, UVCLUSTER converts the set of primary distances among them (i.e. the minimum number of steps, or interactions, required to connect two proteins) into secondary distances that measure the strength of the connection between each pair of proteins when the interactions for all the proteins in the group are considered. We show that this novel strategy has advantages over conventional clustering methods to explore protein-protein interaction data. UVCLUSTER easily incorporates the information of the largest available interaction datasets to generate comprehensive primary distance tables. The versatility, simplicity of use and high speed of UVCLUSTER on standard personal computers suggest that it can be a benchmark analytical tool for interactome data analysis. The program is available upon request from the authors, free for academic users. Additional information available at http://www.uv.es/genomica/UVCLUSTER.

  8. 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.

  9. 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...

  10. Eukaryotic LYR Proteins Interact with Mitochondrial Protein Complexes

    Directory of Open Access Journals (Sweden)

    Heike Angerer

    2015-02-01

    Full Text Available In eukaryotic cells, mitochondria host ancient essential bioenergetic and biosynthetic pathways. LYR (leucine/tyrosine/arginine motif proteins (LYRMs of the Complex1_LYR-like superfamily interact with protein complexes of bacterial origin. Many LYR proteins function as extra subunits (LYRM3 and LYRM6 or novel assembly factors (LYRM7, LYRM8, ACN9 and FMC1 of the oxidative phosphorylation (OXPHOS core complexes. Structural insights into complex I accessory subunits LYRM6 and LYRM3 have been provided by analyses of EM and X-ray structures of complex I from bovine and the yeast Yarrowia lipolytica, respectively. Combined structural and biochemical studies revealed that LYRM6 resides at the matrix arm close to the ubiquinone reduction site. For LYRM3, a position at the distal proton-pumping membrane arm facing the matrix space is suggested. Both LYRMs are supposed to anchor an acyl-carrier protein (ACPM independently to complex I. The function of this duplicated protein interaction of ACPM with respiratory complex I is still unknown. Analysis of protein-protein interaction screens, genetic analyses and predicted multi-domain LYRMs offer further clues on an interaction network and adaptor-like function of LYR proteins in mitochondria.

  11. 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.

  12. 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

  13. Yeast Interacting Proteins Database: YOR158W, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YOR158W PET123 Mitochondrial ribosomal protein of the small subunit; PET123 exhibits genetic interactions...23 Bait description Mitochondrial ribosomal protein of the small subunit; PET123 exhibits genetic interact...ions with PET122, which encodes a COX3 mRNA-specific translational activator Rows w

  14. Concentration dependent model of protein-protein interaction networks

    CERN Document Server

    Zhang, Jingshan

    2007-01-01

    The scale free structure p(k)~k^{-gamma} of protein-protein interaction networks can be produced by a static physical model. We find the earlier study of deterministic threshold models with exponential fitness distributions can be generalized to explain the apparent scale free degree distribution of the physical model, and this explanation provides a generic mechanism of "scale free" networks. We predict the dependence of gamma on experimental protein concentrations. The clustering coefficient distribution of the model is also studied.

  15. 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.

  16. 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

  17. Protein complexes predictions within protein interaction networks using genetic algorithms.

    Science.gov (United States)

    Ramadan, Emad; Naef, Ahmed; Ahmed, Moataz

    2016-07-25

    Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .

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

    Directory of Open Access Journals (Sweden)

    Silvia Martini

    2013-01-01

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

  19. 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.

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

    Lifescience Database Archive (English)

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

  1. A protein-protein interaction dictates Borrelial infectivity.

    Science.gov (United States)

    Thakur, Meghna; Sharma, Kavita; Chao, Kinlin; Smith, Alexis A; Herzberg, Osnat; Pal, Utpal

    2017-06-07

    Two Borrelia burgdorferi interacting proteins, BB0238 and BB0323, play distinct roles in pathogen biology and infectivity although a significance of their interaction remained enigmatic. Here we identified the polypeptide segment essential for BB0238-BB0323 interaction and examined how it supports spirochete infectivity. We show that the interaction region in BB0323 requires amino acid residues 22-200, suggesting that the binding encompasses discontinuous protein segments. In contrast, the interaction region in BB0238 spans only 11 amino acids, residues 120-130. A deletion of these 11 amino acids neither alters the overall secondary structure of the protein, nor affects its stability or oligomerization property, however, it reduces the post-translational stability of the binding partner, BB0323. Mutant B. burgdorferi isolates producing BB0238 lacking the 11-amino acid interaction region were able to persist in ticks but failed to transmit to mice or to establish infection. These results suggest that BB0238-BB0323 interaction is critical for post-translational stability of BB0323, and that this interaction is important for mammalian infectivity and transmission of B. burgdorferi. We show that saturation or inhibition of BB0238-BB0323 interaction could be studied in a luciferase assay, which could be amenable for future identification of small molecule inhibitors to combat B. burgdorferi infection.

  2. Negation of protein-protein interactions: analysis and extraction.

    Science.gov (United States)

    Sanchez-Graillet, Olivia; Poesio, Massimo

    2007-07-01

    Negative information about protein-protein interactions--from uncertainty about the occurrence of an interaction to knowledge that it did not occur--is often of great use to biologists and could lead to important discoveries. Yet, to our knowledge, no proposals focusing on extracting such information have been proposed in the text mining literature. In this work, we present an analysis of the types of negative information that is reported, and a heuristic-based system using a full dependency parser to extract such information. We performed a preliminary evaluation study that shows encouraging results of our system. Finally, we have obtained an initial corpus of negative protein-protein interactions as basis for the construction of larger ones. The corpus is available by request from the authors.

  3. Quantitative study of protein-protein interactions by quartz nanopipettes.

    Science.gov (United States)

    Tiwari, Purushottam Babu; Astudillo, Luisana; Miksovska, Jaroslava; Wang, Xuewen; Li, Wenzhi; Darici, Yesim; He, Jin

    2014-09-07

    In this report, protein-modified quartz nanopipettes were used to quantitatively study protein-protein interactions in attoliter sensing volumes. As shown by numerical simulations, the ionic current through the conical-shaped nanopipette is very sensitive to the surface charge variation near the pore mouth. With the appropriate modification of negatively charged human neuroglobin (hNgb) onto the inner surface of a nanopipette, we were able to detect concentration-dependent current change when the hNgb-modified nanopipette tip was exposed to positively charged cytochrome c (Cyt c) with a series of concentrations in the bath solution. Such current change is due to the adsorption of Cyt c to the inner surface of the nanopipette through specific interactions with hNgb. In contrast, a smaller current change with weak concentration dependence was observed when Cyt c was replaced with lysozyme, which does not specifically bind to hNgb. The equilibrium dissociation constant (KD) for the Cyt c-hNgb complex formation was derived and the value matched very well with the result from surface plasmon resonance measurement. This is the first quantitative study of protein-protein interactions by a conical-shaped nanopore based on charge sensing. Our results demonstrate that nanopipettes can potentially be used as a label-free analytical tool to quantitatively characterize protein-protein interactions.

  4. 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, computational...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

  5. 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 lar...Rows with this bait as prey (0) Prey ORF YOR284W Prey gene name HUA2 Prey description Cytoplasmic protein of unknown function; comput...ational analysis of large-scale protein-protein interact

  6. Hydrophobic interactions of sucralose with protein structures.

    Science.gov (United States)

    Shukla, Nimesh; Pomarico, Enrico; Hecht, Cody J S; Taylor, Erika A; Chergui, Majed; Othon, Christina M

    2018-02-01

    Sucralose is a commonly employed artificial sweetener that appears to destabilize protein native structures. This is in direct contrast to the bio-preservative nature of its natural counterpart, sucrose, which enhances the stability of biomolecules against environmental stress. We have further explored the molecular interactions of sucralose as compared to sucrose to illuminate the origin of the differences in their bio-preservative efficacy. We show that the mode of interactions of sucralose and sucrose in bulk solution differ subtly through the use of hydration dynamics measurement and computational simulation. Sucralose does not appear to disturb the native state of proteins for moderate concentrations (sucralose appears to differ in its interactions with protein leading to the reduction of native state stability. This difference in interaction appears weak. We explored the difference in the preferential exclusion model using time-resolved spectroscopic techniques and observed that both molecules appear to be effective reducers of bulk hydration dynamics. However, the chlorination of sucralose appears to slightly enhance the hydrophobicity of the molecule, which reduces the preferential exclusion of sucralose from the protein-water interface. The weak interaction of sucralose with hydrophobic pockets on the protein surface differs from the behavior of sucrose. We experimentally followed up upon the extent of this weak interaction using isothermal titration calorimetry (ITC) measurements. We propose this as a possible origin for the difference in their bio-preservative properties. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Binding interactions of niclosamide with serum proteins

    Directory of Open Access Journals (Sweden)

    Esra Maltas

    2014-12-01

    Full Text Available A study of the binding of niclosamide (NC to serum proteins such as human serum albumin, hemoglobin, and globulin was carried out using fluorescence and UV-visible spectroscopy. Interactions between NC and these proteins were estimated by Stern–Volmer and van't Hoff equations. The binding constants and the thermodynamic parameters, ΔH, ΔS, and ΔG at different temperatures were also determined by using these equations. Data showed that NC may exhibit a static quenching mechanism with all proteins. The thermodynamic parameters were calculated. Data showed that van der Waals interactions and hydrogen bonds are the main forces for human serum albumin and hemoglobin. Globulin, however, bound to NC via hydrophobic interaction. The spectral changes of synchronous fluorescence suggested that both the microenvironment of NC and the conformation of the proteins changed in relation to their concentrations during NC's binding.

  8. 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.

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

    Science.gov (United States)

    Kuchaiev, Oleksii; Rasajski, Marija; Higham, Desmond J; Przulj, Natasa

    2009-08-01

    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.

  10. Interaction of melanosomal proteins with melanin.

    Science.gov (United States)

    Donatien, P D; Orlow, S J

    1995-08-15

    Melanin is deposited in melanosomes upon a proteinaceous matrix enveloped by a melanosomal membrane. Since melanin is highly detergent insoluble, we hypothesized that the detergent solubility of proteins of the melanosomal matrix might be inversely related to the state of melanosomal melanization. Immunoblotting analyses were performed on extracts of albino and black melanocytes to test this hypothesis. The protein products of the silver (si) and the pink-eyed-dilution (p) loci as well as other matrix constituents were present at twofold higher levels in extracts of albino cells. When black cells were rendered amelanotic by growing cultures in the presence of the tyrosinase inhibitor phenylthiourea, the apparent levels of these proteins were also increased. To obviate the potential role of different levels of synthesis in contributing to these differences, we developed a cell-free melanosomal melanization assay. Upon incubation of a melanosome-rich fraction with the melanin precursor L-3,4-dihydroxyphenylalanine (Dopa) followed by immunoblot analysis, the si locus protein, the p locus protein, and other putative matrix constituents became rapidly insoluble in SDS when compared with the members of the tyrosinase-related family of melanosomal membrane proteins. Our results suggest that melanosomal proteins that interact with melanin may be identified by their relative insolubility in SDS under conditions of increasing melanization. In addition to the si locus protein and other putative melanosomal matrix proteins, the membrane-bound p locus protein may also interact closely with melanin.

  11. Yeast Interacting Proteins Database: YBR135W, YBR252W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available tes proteolysis of M-phase targets through interactions with the proteasome; role in transcriptional regulat...yclin-dependent protein kinase regulatory subunit and adaptor; modulates proteolysis of M-phase targets through interactions

  12. Mapping of protein-protein interaction network of Alexander disease.

    Science.gov (United States)

    Saxena, A K; Saxena, V L; Dixit, S

    2016-05-30

    Alexander disease (ALXD) is slowly progressive neurodegenerative disorder which affects white matter of the central nervous system. The main cause of disorder is mutation in GFAP gene and mutation in some other genes were also reported. This study was aimed at getting a better insight into ALXD pathogenesis and identifying the important functional and highly interconnected nodes in human protein interaction network, identifying the important sub-networks in the system could be helpful in understanding the underlying molecular mechanism. The topological analysis of human protein interaction network strategy to identify highly interconnected sub-network modules from which six proteins are found i.e. GFAP, PLEC, CRYAB, NDUFV1, CASP3 and MAPK14 plays important role in disease. Further, the enrichment analysis of interaction network identifies crucial pathways in which most of the diseased proteins overlaps. Through system biology approach, the undirected human protein interaction network of ALXD is buildup with the help of Cytoscape tool and its various plugins helps to investigate network further. The systematic approach suggests the finding of previously known proteins, GFAP, PLEC, CRYAB, NDUFV1, CASP3 and MAPK14 can be used as a drug targets and potential treatment discovered also enrichment analysis will provide guidance for the future study on Alexander disease.

  13. Modeling disordered protein interactions from biophysical principles.

    Directory of Open Access Journals (Sweden)

    Lenna X Peterson

    2017-04-01

    Full Text Available Disordered protein-protein interactions (PPIs, those involving a folded protein and an intrinsically disordered protein (IDP, are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs.

  14. Analysis of leukocyte membrane protein interactions using protein microarrays

    Directory of Open Access Journals (Sweden)

    Foster-Cuevas Mildred

    2005-03-01

    Full Text Available Abstract Background Protein microarrays represent an emerging class of proteomic tools to investigate multiple protein-protein interactions in parallel. A sufficient proportion of immobilized proteins must maintain an active conformation and an orientation that allows for the sensitive and specific detection of antibody and ligand binding. In order to establish protein array technology for the characterization of the weak interactions between leukocyte membrane proteins, we selected the human leukocyte membrane protein CD200 (OX2 and its cell surface receptor (hCD200R as a model system. As antibody-antigen reactions are generally of higher affinity than receptor-ligand binding, we first analyzed the reactivity of monoclonal antibodies (mAb to normal and mutant forms of immobilized CD200R. Results Fluorescently labelled mAb DX147, DX136 and OX108 were specifically reactive with immobilized recombinant hCD200R extracellular region, over a range of 0.1–40 μg ml-1 corresponding to a limit of sensitivity of 0.01–0.05 femtomol per spot. Orientating hCD200R using capture antibodies, showed that DX147 reacts with an epitope spatially distinct from the more closely related DX136 and OX108 epitopes. A panel of soluble recombinant proteins with mutations in hCD200R domain 1 produced by transiently transfected cells, was arrayed directly without purification and screened for binding to the three mAb. Several showed decreased binding to the blocking mAb DX136 and OX108, suggesting close proximity of these epitopes to the CD200 binding site. Binding of hCD200 to directly immobilized rat, mouse, and hCD200R was achieved with multimeric ligands, in the form of biotinylated-hCD200 coupled to FITC-labelled avidin coated beads. Conclusion We have achieved sensitive, specific and reproducible detection of immobilized CD200R with different antibodies and mapped antigenic epitopes for two mAb in the vicinity of the ligand binding site using protein microarrays

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

    Science.gov (United States)

    Chong, Yuan

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

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

    Directory of Open Access Journals (Sweden)

    Melody G Campbell

    2011-01-01

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

  17. 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

  18. 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

  19. KFC Server: interactive forecasting of protein interaction hot spots.

    Science.gov (United States)

    Darnell, Steven J; LeGault, Laura; Mitchell, Julie C

    2008-07-01

    The KFC Server is a web-based implementation of the KFC (Knowledge-based FADE and Contacts) model-a machine learning approach for the prediction of binding hot spots, or the subset of residues that account for most of a protein interface's; binding free energy. The server facilitates the automated analysis of a user submitted protein-protein or protein-DNA interface and the visualization of its hot spot predictions. For each residue in the interface, the KFC Server characterizes its local structural environment, compares that environment to the environments of experimentally determined hot spots and predicts if the interface residue is a hot spot. After the computational analysis, the user can visualize the results using an interactive job viewer able to quickly highlight predicted hot spots and surrounding structural features within the protein structure. The KFC Server is accessible at http://kfc.mitchell-lab.org.

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

    Directory of Open Access Journals (Sweden)

    Barkallah Insaf

    2009-04-01

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

  1. PIWI Proteins and PIWI-Interacting RNA

    DEFF Research Database (Denmark)

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

    2017-01-01

    P-Element induced wimpy testis (PIWI)-interacting RNAs (piRNAs) are a type of noncoding RNAs (ncRNAs) and interact with PIWI proteins. piRNAs were primarily described in the germline, but emerging evidence revealed that piRNAs are expressed in a tissue-specific manner among multiple human somatic......-cell maintenance, self-renewal, retrotransposons silencing and the male germline mobility control. A growing number of studies have demonstrated that several piRNA and PIWI proteins are aberrantly expressed in various kinds of cancers and may probably serve as a novel biomarker and therapeutic target for cancer...... treatment. Nevertheless, their specific mechanisms and functions need further investigation. In this review, we discuss about the biogenesis, functions and the emerging role of piRNAs and PIWI proteins in cancer, providing novel insights into the possible applications of piRNAs and PIWI proteins in cancer...

  2. PIWI Proteins and PIWI-Interacting RNA

    DEFF Research Database (Denmark)

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

    2017-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-06-14

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

  4. 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.

  5. A method for predicting protein-protein interaction types.

    Directory of Open Access Journals (Sweden)

    Yael Silberberg

    Full Text Available Protein-protein interactions (PPIs govern basic cellular processes through signal transduction and complex formation. The diversity of those processes gives rise to a remarkable diversity of interactions types, ranging from transient phosphorylation interactions to stable covalent bonding. Despite our increasing knowledge on PPIs in humans and other species, their types remain relatively unexplored and few annotations of types exist in public databases. Here, we propose the first method for systematic prediction of PPI type based solely on the techniques by which the interaction was detected. We show that different detection methods are better suited for detecting specific types. We apply our method to ten interaction types on a large scale human PPI dataset. We evaluate the performance of the method using both internal cross validation and external data sources. In cross validation, we obtain an area under receiver operating characteristic (ROC curve ranging from 0.65 to 0.97 with an average of 0.84 across the predicted types. Comparing the predicted interaction types to external data sources, we obtained significant agreements for phosphorylation and ubiquitination interactions, with hypergeometric p-value = 2.3e(-54 and 5.6e(-28 respectively. We examine the biological relevance of our predictions using known signaling pathways and chart the abundance of interaction types in cell processes. Finally, we investigate the cross-relations between different interaction types within the network and characterize the discovered patterns, or motifs. We expect the resulting annotated network to facilitate the reconstruction of process-specific subnetworks and assist in predicting protein function or interaction.

  6. Prediction of Protein-Protein Interacting Sites: How to Bridge Molecular Events to Large Scale Protein Interaction Networks

    Science.gov (United States)

    Bartoli, Lisa; Martelli, Pier Luigi; Rossi, Ivan; Fariselli, Piero; Casadio, Rita

    Most of the cellular functions are the result of the concerted action of protein complexes forming pathways and networks. For this reason, efforts were devoted to the study of protein-protein interactions. Large-scale experiments on whole genomes allowed the identification of interacting protein pairs. However residues involved in the interaction are generally not known and the majority of the interactions still lack a structural characterization. A crucial step towards the deciphering of the interaction mechanism of proteins is the recognition of their interacting surfaces, particularly in those structures for which also the most recent interaction network resources do not contain information. To this purpose, we developed a neural network-based method that is able to characterize protein complexes, by predicting amino acid residues that mediate the interactions. All the Protein Data Bank (PDB) chains, both in the unbound and in the complexed form, are predicted and the results are stored in a database of interaction surfaces (http://gpcr.biocomp.unibo.it/zenpatches). Finally, we performed a survey on the different computational methods for protein-protein interaction prediction and on their training/testing sets in order to highlight the most informative properties of protein interfaces.

  7. Yeast Interacting Proteins Database: YOR097C, YML008C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YOR097C - Putative protein of unknown function; identified as interacting with Hsp82p in a high-throughpu... description Putative protein of unknown function; identified as interacting with... Hsp82p in a high-throughput two-hybrid screen; YOR097C is not an essential gene Rows with this bait as bait

  8. Yeast Interacting Proteins Database: YLR223C, YOR247W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YLR223C IFH1 Essential protein with a highly acidic N-terminal domain; IFH1 exhibits genetic interactions...ion Essential protein with a highly acidic N-terminal domain; IFH1 exhibits genetic interactions with FHL1,

  9. Yeast Interacting Proteins Database: YBR187W, YNR032W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available null mutant; GFP-fusion protein localizes to the vacuole; expression pattern and physical interactions sugge...expression is reduced in a gcr1 null mutant; GFP-fusion protein localizes to the vacuole; expression pattern and physical interaction

  10. Yeast Interacting Proteins Database: YOR180C, YGL153W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available central component of the peroxisomal protein import machinery; interacts with both PTS1 (Pex5p) and PTS2...central component of the peroxisomal protein import machinery; interacts with both PTS1 (Pex5p) and PTS2

  11. Yeast Interacting Proteins Database: YCR036W, YGL153W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available central component of the peroxisomal protein import machinery; interacts with both PTS1 (Pex5p) and PTS2...central component of the peroxisomal protein import machinery; interacts with both PTS1 (Pex5p) and PTS2

  12. Yeast Interacting Proteins Database: YDR256C, YGL153W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available central component of the peroxisomal protein import machinery; interacts with both PTS1 (Pex5p) and PTS2...central component of the peroxisomal protein import machinery; interacts with both PTS1 (Pex5p) and PTS2

  13. 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

  14. Yeast Interacting Proteins Database: YGR173W, YDR152W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available plasmic RWD domain-containing protein of unknown function; interacts with Rbg1p and Gcn1p; associates with translating...slating ribosomes; putative intrinsically unstructured p...ion Highly-acidic cytoplasmic RWD domain-containing protein of unknown function; interacts with Rbg1p and Gcn1p; associates with tran

  15. 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

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

    Lifescience Database Archive (English)

    Full Text Available -purification experiments; Myb-like DNA-binding protein that may bind to the Ter region of rDNA; interacts physically...n experiments; Myb-like DNA-binding protein that may bind to the Ter region of rDNA; interacts physically wi

  17. Yeast Interacting Proteins Database: YMR047C, YNL078W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available L078W NIS1 Protein localized in the bud neck at G2/M phase; physically interacts ...ene name NIS1 Prey description Protein localized in the bud neck at G2/M phase; physically interacts with se

  18. Yeast Interacting Proteins Database: YPR040W, YNR032W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YPR040W TIP41 Protein that interacts physically and genetically with Tap42p, which ... 0 0 0 0 0 - - - - - 0 0 3 - Show YPR040W Bait ORF YPR040W Bait gene name TIP41 Bait description Protein that interacts physically

  19. 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

  20. HCVpro: hepatitis C virus protein interaction database.

    Science.gov (United States)

    Kwofie, Samuel K; Schaefer, Ulf; Sundararajan, Vijayaraghava S; Bajic, Vladimir B; Christoffels, Alan

    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/. Copyright © 2011 Elsevier B.V. All rights reserved.

  1. Quantitative Interaction Proteomics of Neurodegenerative Disease Proteins

    Directory of Open Access Journals (Sweden)

    Fabian Hosp

    2015-05-01

    Full Text Available Several proteins have been linked to neurodegenerative disorders (NDDs, but their molecular function is not completely understood. Here, we used quantitative interaction proteomics to identify binding partners of Amyloid beta precursor protein (APP and Presenilin-1 (PSEN1 for Alzheimer’s disease (AD, Huntingtin (HTT for Huntington’s disease, Parkin (PARK2 for Parkinson’s disease, and Ataxin-1 (ATXN1 for spinocerebellar ataxia type 1. Our network reveals common signatures of protein degradation and misfolding and recapitulates known biology. Toxicity modifier screens and comparison to genome-wide association studies show that interaction partners are significantly linked to disease phenotypes in vivo. Direct comparison of wild-type proteins and disease-associated variants identified binders involved in pathogenesis, highlighting the value of differential interactome mapping. Finally, we show that the mitochondrial protein LRPPRC interacts preferentially with an early-onset AD variant of APP. This interaction appears to induce mitochondrial dysfunction, which is an early phenotype of AD.

  2. 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

  3. Targeting protein-protein interactions for parasite control.

    Directory of Open Access Journals (Sweden)

    Christina M Taylor

    2011-04-01

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

  4. Quantitative study of protein-protein interactions by quartz nanopipettes

    Science.gov (United States)

    Tiwari, Purushottam Babu; Astudillo, Luisana; Miksovska, Jaroslava; Wang, Xuewen; Li, Wenzhi; Darici, Yesim; He, Jin

    2014-08-01

    In this report, protein-modified quartz nanopipettes were used to quantitatively study protein-protein interactions in attoliter sensing volumes. As shown by numerical simulations, the ionic current through the conical-shaped nanopipette is very sensitive to the surface charge variation near the pore mouth. With the appropriate modification of negatively charged human neuroglobin (hNgb) onto the inner surface of a nanopipette, we were able to detect concentration-dependent current change when the hNgb-modified nanopipette tip was exposed to positively charged cytochrome c (Cyt c) with a series of concentrations in the bath solution. Such current change is due to the adsorption of Cyt c to the inner surface of the nanopipette through specific interactions with hNgb. In contrast, a smaller current change with weak concentration dependence was observed when Cyt c was replaced with lysozyme, which does not specifically bind to hNgb. The equilibrium dissociation constant (KD) for the Cyt c-hNgb complex formation was derived and the value matched very well with the result from surface plasmon resonance measurement. This is the first quantitative study of protein-protein interactions by a conical-shaped nanopore based on charge sensing. Our results demonstrate that nanopipettes can potentially be used as a label-free analytical tool to quantitatively characterize protein-protein interactions.In this report, protein-modified quartz nanopipettes were used to quantitatively study protein-protein interactions in attoliter sensing volumes. As shown by numerical simulations, the ionic current through the conical-shaped nanopipette is very sensitive to the surface charge variation near the pore mouth. With the appropriate modification of negatively charged human neuroglobin (hNgb) onto the inner surface of a nanopipette, we were able to detect concentration-dependent current change when the hNgb-modified nanopipette tip was exposed to positively charged cytochrome c (Cyt c) with

  5. Interactions affected by arginine methylation in the yeast protein-protein interaction network.

    Science.gov (United States)

    Erce, Melissa A; Abeygunawardena, Dhanushi; Low, Jason K K; Hart-Smith, Gene; Wilkins, Marc R

    2013-11-01

    Protein-protein interactions can be modulated by the methylation of arginine residues. As a means of testing this, we recently described a conditional two-hybrid system, based on the bacterial adenylate cyclase (BACTH) system. Here, we have used this conditional two-hybrid system to explore the effect of arginine methylation in modulating protein-protein interactions in a subset of the Saccharomyces cerevisiae arginine methylproteome network. Interactions between the yeast hub protein Npl3 and yeast proteins Air2, Ded1, Gbp2, Snp1, and Yra1 were first validated in the absence of methylation. The major yeast arginine methyltransferase Hmt1 was subsequently included in the conditional two-hybrid assay, initially to determine the degree of methylation that occurs. Proteins Snp1 and Yra1 were confirmed as Hmt1 substrates, with five and two novel arginine methylation sites mapped by ETD LC-MS/MS on these proteins, respectively. Proteins Ded1 and Gbp2, previously predicted but not confirmed as substrates of Hmt1, were also found to be methylated with five and seven sites mapped respectively. Air2 was found to be a novel substrate of Hmt1 with two sites mapped. Finally, we investigated the interactions of Npl3 with the five interaction partners in the presence of active Hmt1 and in the presence of Hmt1 with a G68R inactivation mutation. We found that the interaction between Npl3 and Air2, and Npl3 and Ded1, were significantly increased in the presence of active Hmt1; the interaction of Npl3 and Snp1 showed a similar degree of increase in interaction but this was not statistically significant. The interactions of Npl3 and Gbp2, along with Npl3 and Yra1, were not significantly increased or decreased by methylation. We conclude that methylarginine may be a widespread means by which the interactions of proteins are modulated.

  6. 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

  7. 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

  8. 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

  9. Motif mediated protein-protein interactions as drug targets.

    Science.gov (United States)

    Corbi-Verge, Carles; Kim, Philip M

    2016-03-02

    Protein-protein interactions (PPI) are involved in virtually every cellular process and thus represent an attractive target for therapeutic interventions. A significant number of protein interactions are frequently formed between globular domains and short linear peptide motifs (DMI). Targeting these DMIs has proven challenging and classical approaches to inhibiting such interactions with small molecules have had limited success. However, recent new approaches have led to the discovery of potent inhibitors, some of them, such as Obatoclax, ABT-199, AEG-40826 and SAH-p53-8 are likely to become approved drugs. These novel inhibitors belong to a wide range of different molecule classes, ranging from small molecules to peptidomimetics and biologicals. This article reviews the main reasons for limited success in targeting PPIs, discusses how successful approaches overcome these obstacles to discovery promising inhibitors for human protein double minute 2 (HDM2), B-cell lymphoma 2 (Bcl-2), X-linked inhibitor of apoptosis protein (XIAP), and provides a summary of the promising approaches currently in development that indicate the future potential of PPI inhibitors in drug discovery.

  10. A framework for protein and membrane interactions

    Directory of Open Access Journals (Sweden)

    Giorgio Bacci

    2009-11-01

    Full Text Available 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 instance of the calculus corresponds to a bigraphical reactive system, and vice versa (almost. Therefore, we can inherith the rich theory of bigraphs, such as the automatic construction of labelled transition systems and behavioural congruences.

  11. 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.

  12. 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.

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

    Science.gov (United States)

    Suter, Bernhard; Zhang, Xinmin; Pesce, C Gustavo; Mendelsohn, Andrew R; Dinesh-Kumar, Savithramma P; Mao, Jian-Hua

    2015-01-01

    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.

  14. Interaction of plant polyphenols with salivary proteins.

    Science.gov (United States)

    Bennick, Anders

    2002-01-01

    Tannins are polyphenols that occur widespread in plant-based food. They are considered to be part of the plant defense system against environmental stressors. Tannins have a number of effects on animals, including growth-rate depression and inhibition of digestive enzymes. Tannins also have an effect on humans: They are, for example, the cause of byssinosis, a condition that is due to exposure to airborne tannin. Their biological effect is related to the great efficiency by which tannins precipitate proteins, an interaction that occurs by hydrophobic forces and hydrogen bonding. Two groups of salivary proteins, proline-rich proteins and histatins, are highly effective precipitators of tannin, and there is evidence that at least proline-rich proteins act as a first line of defense against tannins, perhaps by precipitating tannins in food and preventing their absorption from the alimentary canal. Proline plays an important role in the interaction of proline-rich proteins with tannins. In contrast, it is primarily basic residues that are responsible for the binding of histatins to tannin. The high concentration of tannin-binding proteins in human saliva may be related to the fruit and vegetable diet of human ancestors.

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

    Science.gov (United States)

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

    2012-01-01

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

  16. Specificity and stability of transient protein-protein interactions.

    Science.gov (United States)

    Vishwanath, Sneha; Sukhwal, Anshul; Sowdhamini, Ramanathan; Srinivasan, Narayanaswamy

    2017-06-01

    Remarkable features that are achieved in a protein-protein complex to precise levels are stability and specificity. Deviation from the normal levels of specificity and stability, which is often caused by mutations, could result in disease conditions. Chemical nature, 3-D arrangement and dynamics of interface residues code for both specificity and stability. This article reviews roles of interfacial residues in transient protein-protein complexes. It is proposed that aside from hotspot residues conferring stability to the complex, a small set of 'rigid' residues at the interface that maintain conformation between complexed and uncomplexed forms, play a major role in conferring specificity. Exceptionally, 'super hotspot' residues, which confer both stability and specificity, are attractive sites for interaction with small molecule inhibitors. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

    Science.gov (United States)

    Chen, Ching-Tai; Peng, Hung-Pin; Jian, Jhih-Wei; Tsai, Keng-Chang; Chang, Jeng-Yih; Yang, Ei-Wen; Chen, Jun-Bo; Ho, Shinn-Ying; Hsu, Wen-Lian; Yang, An-Suei

    2012-01-01

    Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with

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

    Directory of Open Access Journals (Sweden)

    Ching-Tai Chen

    Full Text Available Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins and were tested on an independent dataset (consisting of 142 proteins. The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted

  19. Geometric evolutionary dynamics of protein interaction networks.

    Science.gov (United States)

    Przulj, Natasa; Kuchaiev, Oleksii; Stevanović, Aleksandar; Hayes, Wayne

    2010-01-01

    Understanding the evolution and structure of protein-protein interaction (PPI) networks is a central problem of systems biology. Since most processes in the cell are carried out by groups of proteins acting together, a theoretical model of how PPI networks develop based on duplications and mutations is an essential ingredient for understanding the complex wiring of the cell. Many different network models have been proposed, from those that follow power-law degree distributions and those that model complementarity of protein binding domains, to those that have geometric properties. Here, we introduce a new model for PPI network (and thus gene) evolution that produces well-fitting network models for currently available PPI networks. The model integrates geometric network properties with evolutionary dynamics of PPI network evolution.

  20. 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.

  1. Analysis of interactions between intraflagellar transport proteins.

    Science.gov (United States)

    Behal, Robert H; Cole, Douglas G

    2013-01-01

    Intraflagellar transport (IFT) involves the movement of large proteinaceous particles or trains along the length of ciliary and flagellar axonemal microtubules. The particles contain multiple copies of two protein complexes. As isolated from the flagellated model organism, Chlamydomonas reinhardtii, IFT A contains 6 distinct gene products while IFT B contains at least 13 distinct gene products. To better understand the architecture of these two complexes, a multifaceted approach has been employed to identify subcomplexes and specific protein-protein interactions. The high biochemical yields afforded with Chlamydomonas preparations have allowed traditional biochemical approaches including chemical cross-linking and disruption of native complexes, which, in the case of IFT B, have revealed a core subcomplex retaining nine of the B subunits. Complementing these results are molecular approaches including two-hybrid screenings and heterologous expression that have identified specific protein-protein interactions. Lastly, genetic approaches utilizing Chlamydomonas IFT mutants have shown how the loss of specific subunits perturb the complexes and, in the case of IFT A, they have revealed a core subcomplex containing half of the A subunits. Copyright © 2013 Elsevier Inc. All rights reserved.

  2. PCorral—interactive mining of protein interactions from MEDLINE

    Science.gov (United States)

    Li, Chen; 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. PMID:23640984

  3. 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.

  4. 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 lar...gene name YIP4 Prey description Protein that interacts with Rab GTPases, localized to late Golgi vesicles; computational

  5. Yeast Interacting Proteins Database: YMR047C, YER107C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available repetitive GLFG motif that interacts with mRNA export factor Mex67p and with karyopherin Kap95p; homologous...nuclear pore complex required for polyadenylated RNA export but not for protein import, homologous to S. pombe...repetitive GLFG motif that interacts with mRNA export factor Mex67p and with karyopherin Kap95p; homologous...nuclear pore complex required for polyadenylated RNA export but not for protein import, homologous to S. pombe

  6. NPIDB: Nucleic acid-Protein Interaction DataBase

    National Research Council Canada - National Science Library

    Kirsanov, Dmitry D; Zanegina, Olga N; Aksianov, Evgeniy A; Spirin, Sergei A; Karyagina, Anna S; Alexeevski, Andrei V

    2013-01-01

    The Nucleic acid-Protein Interaction DataBase (http://npidb.belozersky.msu.ru/) contains information derived from structures of DNA-protein and RNA-protein complexes extracted from the Protein Data Bank...

  7. Frontal affinity chromatography: sugar-protein interactions.

    Science.gov (United States)

    Tateno, Hiroaki; Nakamura-Tsuruta, Sachiko; Hirabayashi, Jun

    2007-01-01

    Frontal affinity chromatography using fluorescence detection (FAC-FD) is a versatile technique for the precise determination of dissociation constants (Kd) between glycan-binding proteins (lectins) and fluorescent-labeled glycans. A series of glycan-containing solutions is applied to a lectin-immobilized column, and the elution profile of each glycan (termed the 'elution front', V) is compared with that (V0) for an appropriate control. Here we describe our standard protocol using an automated FAC system (FAC-1), consisting of two isocratic pumps, an autosampler, a column oven and two miniature columns connected to a fluorescence detector. Analysis time for 100 sugar-protein interactions is approximately 10 h, using as little as 2.5 pmol of pyridylaminated (PA) oligosaccharide per analysis. Using FAC-FD, we have so far obtained quantitative interaction data of >100 lectins for >100 PA oligosaccharides.

  8. Experimental evolution of protein?protein interaction networks

    OpenAIRE

    Ka?ar, Bet?l; Gaucher, Eric A.

    2013-01-01

    The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molec...

  9. Inferring protein function by domain context similarities in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Sun Zhirong

    2009-12-01

    Full Text Available Abstract Background Genome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown. The availability of large scale protein-protein interaction data sets makes it possible to develop new function prediction methods based on protein-protein interaction (PPI networks. Although several existing methods combine multiple information resources, there is no study that integrates protein domain information and PPI networks to predict protein functions. Results The domain context similarity can be a useful index to predict protein function similarity. The prediction accuracy of our method in yeast is between 63%-67%, which outperforms the other methods in terms of ROC curves. Conclusion This paper presents a novel protein function prediction method that combines protein domain composition information and PPI networks. Performance evaluations show that this method outperforms existing methods.

  10. Yeast Interacting Proteins Database: YNL189W, YKL130C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available teracts with She3p; part of the mRNA localization machinery that restricts accumulation of certain proteins ...A-binding protein that binds specific mRNAs and interacts with She3p; part of the mRNA localization machinery that restricts

  11. 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

  12. 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.

  13. Topology-function conservation in protein-protein interaction networks.

    Science.gov (United States)

    Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša

    2015-05-15

    Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.

  14. Yeast Interacting Proteins Database: YJR102C, YLR417W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available omain which is involved in interactions with ESCRT-I and ubiquitin-dependent sort...T-II complex; contains the GLUE (GRAM Like Ubiquitin binding in EAP45) domain which is involved in interac...tions with ESCRT-I and ubiquitin-dependent sorting of proteins into the endosome Ro

  15. Yeast Interacting Proteins Database: YPL002C, YLR417W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available RAM Like Ubiquitin binding in EAP45) domain which is involved in interactions with ESCRT-I and ubiquitin-dep...n which is involved in interactions with ESCRT-I and ubiquitin-dependent sorting of proteins into the endoso... ESCRT-II complex; contains the GLUE (GRAM Like Ubiquitin binding in EAP45) domai

  16. Yeast Interacting Proteins Database: YHR009C, YOR359W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ding protein containing a SAM domain; shows genetic interactions with Vti1p, which is a v-SNARE involved in ...aining a SAM domain; shows genetic interactions with Vti1p, which is a v-SNARE involved in cis-Golgi membran

  17. Yeast Interacting Proteins Database: YBR135W, YBR160W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available tes proteolysis of M-phase targets through interactions with the proteasome; role in transcriptional regulat... description Cyclin-dependent protein kinase regulatory subunit and adaptor; modulates proteolysis of M-phase targets through interac...tions with the proteasome; role in transcriptional regulation, recruiting proteasom

  18. Arabinogalactan proteins in root-microbe interactions.

    Science.gov (United States)

    Nguema-Ona, Eric; Vicré-Gibouin, Maïté; Cannesan, Marc-Antoine; Driouich, Azeddine

    2013-08-01

    Arabinogalactan proteins (AGPs) are among the most intriguing sets of macromolecules, specific to plants, structurally complex, and found abundantly in all plant organs including roots, as well as in root exudates. AGPs have been implicated in several fundamental plant processes such as development and reproduction. Recently, they have emerged as interesting actors of root-microbe interactions in the rhizosphere. Indeed, recent findings indicate that AGPs play key roles at various levels of interaction between roots and soil-borne microbes, either beneficial or pathogenic. Therefore, the focus of this review is the role of AGPs in the interactions between root cells and microbes. Understanding this facet of AGP function will undoubtedly improve plant health and crop protection. Copyright © 2013 Elsevier Ltd. All rights reserved.

  19. Channel-interacting PDZ protein, 'CIPP', interacts with proteins involved in cytoskeletal dynamics.

    Science.gov (United States)

    Alpi, Emanuele; Landi, Elena; Barilari, Manuela; Serresi, Michela; Salvadori, Piero; Bachi, Angela; Dente, Luciana

    2009-04-15

    Neuronal CIPP (channel-interacting PDZ protein) is a multivalent PDZ protein that interacts with specific channels and receptors highly expressed in the brain. It is composed of four PDZ domains that behave as a scaffold to clusterize functionally connected proteins. In the present study, we selected a set of potential CIPP interactors that are involved directly or indirectly in mechanisms of cytoskeletal remodelling and membrane protrusion formation. For some of these, we first proved the direct binding to specific CIPP PDZ domains considered as autonomous elements, and then confirmed the interaction with the whole protein. In particular, the small G-protein effector IRSp53 (insulin receptor tyrosine kinase substrate protein p53) specifically interacts with the second PDZ domain of CIPP and, when co-transfected in cultured mammalian cells with a tagged full-length CIPP, it induces a marked reorganization of CIPP cytoplasmic localization. Large punctate structures are generated as a consequence of CIPP binding to the IRSp53 C-terminus. Analysis of the puncta nature, using various endocytic markers, revealed that they are not related to cytoplasmic vesicles, but rather represent multi-protein assemblies, where CIPP can tether other potential interactors.

  20. Tetramer formation in Arabidopsis MADS domain proteins: analysis of a protein-protein interaction network

    NARCIS (Netherlands)

    Espinosa-Soto, C.; Immink, R.G.H.; Angenent, G.C.; Alvarez-Buylla, E.R.; Folter, de S.

    2014-01-01

    Background: MADS domain proteins are transcription factors that coordinate several important developmental processes in plants. These proteins interact with other MADS domain proteins to form dimers, and it has been proposed that they are able to associate as tetrameric complexes that regulate

  1. Discover Protein Complexes in Protein-Protein Interaction Networks Using Parametric Local Modularity

    Directory of Open Access Journals (Sweden)

    Tan Kai

    2010-10-01

    Full Text Available Abstract Background Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in multiple species and in both normal and diseased cells. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively distil network models from large-scale interactome data. Results We present an algorithm, miPALM (Module Inference by Parametric Local Modularity, to infer protein complexes in a protein-protein interaction network. The algorithm uses a novel graph theoretic measure, parametric local modularity, to identify highly connected sub-networks as candidate protein complexes. Using gold standard sets of protein complexes and protein function and localization annotations, we show our algorithm achieved an overall improvement over previous algorithms in terms of precision, recall, and biological relevance of the predicted complexes. We applied our algorithm to predict and characterize a set of 138 novel protein complexes in S. cerevisiae. Conclusions miPALM is a novel algorithm for detecting protein complexes from large protein-protein interaction networks with improved accuracy than previous methods. The software is implemented in Matlab and is freely available at http://www.medicine.uiowa.edu/Labs/tan/software.html.

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

    Science.gov (United States)

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

    2017-05-01

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

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

    Directory of Open Access Journals (Sweden)

    Vincent Vagenende

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

  4. Experimental evolution of protein-protein interaction networks.

    Science.gov (United States)

    Kaçar, Betül; Gaucher, Eric A

    2013-08-01

    The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks.

  5. A protein interaction network associated with asthma.

    Science.gov (United States)

    Hwang, Sohyun; Son, Seung-Woo; Kim, Sang Cheol; Kim, Young Joo; Jeong, Hawoong; Lee, Doheon

    2008-06-21

    Identifying candidate genes related to complex diseases or traits and mapping their relationships require a system-level analysis at a cellular scale. The objective of the present study is to systematically analyze the complex effects of interrelated genes and provide a framework for revealing their relationships in association with a specific disease (asthma in this case). We observed that protein-protein interaction (PPI) networks associated with asthma have a power-law connectivity distribution as many other biological networks have. The hub nodes and skeleton substructure of the result network are consistent with the prior knowledge about asthma pathways, and also suggest unknown candidate target genes associated with asthma, including GNB2L1, BRCA1, CBL, and VAV1. In particular, GNB2L1 appears to play a very important role in the asthma network through frequent interactions with key proteins in cellular signaling. This network-based approach represents an alternative method for analyzing the complex effects of candidate genes associated with complex diseases and suggesting a list of gene drug targets. The full list of genes and the analysis details are available in the following online supplementary materials: http://biosoft.kaist.ac.kr:8080/resources/asthma_ppi.

  6. 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

  7. 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. Copyright © 2013 Elsevier Ltd. All rights reserved.

  8. Discovering Protein-Protein Interactions Using Nucleic Acid Programmable Protein Arrays.

    Science.gov (United States)

    Tang, Yanyang; Qiu, Ji; Machner, Matthias; LaBaer, Joshua

    2017-03-03

    We have developed a protocol enabling the study of protein-protein interactions (PPIs) at the proteome level using in vitro-synthesized proteins. Assay preparation requires molecular cloning of the query gene into a vector that supports in vitro transcription/translation (IVTT) and appends a HaloTag to the query protein of interest. In parallel, protein microarrays are prepared by printing plasmids encoding glutathione S-transferase (GST)-tagged target proteins onto a carrier matrix/glass slide coated with antibody directed against GST. At the time of the experiment, the query protein and the target protein are produced separately through IVTT. The query protein is then applied to nucleic acid programmable protein arrays (NAPPA) that display thousands of freshly produced target proteins captured by anti-GST antibody. Interactions between the query and immobilized target proteins are detected through addition of a fluorophore-labeled HaloTag ligand. Our protocol allows the elucidation of PPIs in a high-throughput fashion using proteins produced in vitro, obviating the scientific challenges, high cost, and laborious work, as well as concerns about protein stability, which are usually present in protocols using conventional protein arrays. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

  9. Modularity detection in protein-protein interaction networks.

    Science.gov (United States)

    Narayanan, Tejaswini; Gersten, Merril; Subramaniam, Shankar; Grama, Ananth

    2011-12-29

    Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated. We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, C.Elegans and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient. We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the application of those measures to

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

    OpenAIRE

    Habibi, Mahnaz; Eslahchi, Changiz; Wong, Limsoon

    2010-01-01

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

  11. Yeast Three-Hybrid System for the Detection of Protein-Protein Interactions.

    Science.gov (United States)

    Maruta, Natsumi; Trusov, Yuri; Botella, Jose R

    2016-01-01

    Protein-protein interaction studies provide useful insights into biological processes taking place within the living cell. A number of techniques are available to unravel large structural protein complexes, functional protein modules, and temporary protein associations occurring during signal transduction. The choice of method depends on the nature of the proteins and the interaction being studied. Here we present an optimized and simplified yeast three-hybrid method for analysis of protein interactions involving three components.

  12. 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.

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

    Science.gov (United States)

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

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

  14. Channel-interacting PDZ protein “CIPP” interacts with proteins involved in cytoskeletal dynamics

    OpenAIRE

    Alpi, Emanuele; Landi, Elena; Barilari, Manuela; Serresi, Michela; Salvadori, Piero; Bachi, Angela; Dente, Luciana

    2009-01-01

    Abstract Neuronal CIPP is a multivalent PDZ protein that interacts with specific channels and receptors, highly expressed in the brain. It is composed of four PDZ domains that behave as a scaffold to clusterize functionally connected proteins. In this study, we selected a set of potential CIPP interactors that are directly or indirectly involved in mechanisms of cytoskeletal remodeling and membrane protrusions formation. For some of these, we first proved the direct binding to spec...

  15. Fragment molecular orbital method for studying lanthanide interactions with proteins

    Energy Technology Data Exchange (ETDEWEB)

    Tsushima, Satoru [Helmholtz-Zentrum Dresden-Rossendorf e.V., Dresden (Germany). Biophysics; Komeiji, Y. [National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba (Japan); Mochizuki, Y. [Rikkyo Univ., Tokyo (Japan)

    2017-06-01

    The binding affinity of the calcium-binding protein calmodulin towards Eu{sup 3+} was studied as a model for lanthanide protein interactions in the large family of ''EF-hand'' calcium-binding proteins.

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

    Directory of Open Access Journals (Sweden)

    Meijing Li

    2015-01-01

    Full Text Available Many researchers focus on developing protein-named entity recognition (Protein-NER or PPI extraction systems. However, the studies about these two topics cannot be merged well; then existing PPI extraction systems’ Protein-NER still needs to improve. In this paper, we developed the protein-protein interaction extraction system named PPIMiner based on Support Vector Machine (SVM and parsing tree. PPIMiner consists of three main models: natural language processing (NLP model, Protein-NER model, and PPI discovery model. The Protein-NER model, which is named ProNER, identifies the protein names based on two methods: dictionary-based method and machine learning-based method. ProNER is capable of identifying more proteins than dictionary-based Protein-NER model in other existing systems. The final discovered PPIs extracted via PPI discovery model are represented in detail because we showed the protein interaction types and the occurrence frequency through two different methods. In the experiments, the result shows that the performances achieved by our ProNER and PPI discovery model are better than other existing tools. PPIMiner applied this protein-named entity recognition approach and parsing tree based PPI extraction method to improve the performance of PPI extraction. We also provide an easy-to-use interface to access PPIs database and an online system for PPIs extraction and Protein-NER.

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

    Science.gov (United States)

    Geng, Haijiang; Lu, Tao; Lin, Xiao; Liu, Yu; Yan, Fangrong

    2015-01-01

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

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

    Science.gov (United States)

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

    2012-07-01

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

  19. Assessment and significance of protein-protein interactions during development of protein biopharmaceuticals.

    Science.gov (United States)

    Yadav, Sandeep; Liu, Jun; Scherer, Thomas M; Gokarn, Yatin; Demeule, Barthélemy; Kanai, Sonoko; Andya, James D; Shire, Steven J

    2013-06-01

    Early development of protein biotherapeutics using recombinant DNA technology involved progress in the areas of cloning, screening, expression and recovery/purification. As the biotechnology industry matured, resulting in marketed products, a greater emphasis was placed on development of formulations and delivery systems requiring a better understanding of the chemical and physical properties of newly developed protein drugs. Biophysical techniques such as analytical ultracentrifugation, dynamic and static light scattering, and circular dichroism were used to study protein-protein interactions during various stages of development of protein therapeutics. These studies included investigation of protein self-association in many of the early development projects including analysis of highly glycosylated proteins expressed in mammalian CHO cell cultures. Assessment of protein-protein interactions during development of an IgG1 monoclonal antibody that binds to IgE were important in understanding the pharmacokinetics and dosing for this important biotherapeutic used to treat severe allergic IgE-mediated asthma. These studies were extended to the investigation of monoclonal antibody-antigen interactions in human serum using the fluorescent detection system of the analytical ultracentrifuge. Analysis by sedimentation velocity analytical ultracentrifugation was also used to investigate competitive binding to monoclonal antibody targets. Recent development of high concentration protein formulations for subcutaneous administration of therapeutics posed challenges, which resulted in the use of dynamic and static light scattering, and preparative analytical ultracentrifugation to understand the self-association and rheological properties of concentrated monoclonal antibody solutions.

  20. Reuse of structural domain–domain interactions in protein networks

    Science.gov (United States)

    Schuster-Böckler, Benjamin; Bateman, Alex

    2007-01-01

    Background Protein interactions are thought to be largely mediated by interactions between structural domains. Databases such as iPfam relate interactions in protein structures to known domain families. Here, we investigate how the domain interactions from the iPfam database are distributed in protein interactions taken from the HPRD, MPact, BioGRID, DIP and IntAct databases. Results We find that known structural domain interactions can only explain a subset of 4–19% of the available protein interactions, nevertheless this fraction is still significantly bigger than expected by chance. There is a correlation between the frequency of a domain interaction and the connectivity of the proteins it occurs in. Furthermore, a large proportion of protein interactions can be attributed to a small number of domain interactions. We conclude that many, but not all, domain interactions constitute reusable modules of molecular recognition. A substantial proportion of domain interactions are conserved between E. coli, S. cerevisiae and H. sapiens. These domains are related to essential cellular functions, suggesting that many domain interactions were already present in the last universal common ancestor. Conclusion Our results support the concept of domain interactions as reusable, conserved building blocks of protein interactions, but also highlight the limitations currently imposed by the small number of available protein structures. PMID:17640363

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

    Lifescience Database Archive (English)

    Full Text Available Rvs161p to regulate actin cytoskeleton, endocytosis, and viability following star...0) YDR388W RVS167 Actin-associated protein, interacts with Rvs161p to regulate actin cytoskeleton, endocytosis, and viability followi...ng starvation or osmotic stress; homolog of mammalian am

  2. Crosslinking Studies of Protein-Protein Interactions in Nonribosomal Peptide Biosynthesis

    National Research Council Canada - National Science Library

    Hur, Gene H; Meier, Jordan L; Baskin, Jeremy; Codelli, Julian A; Bertozzi, Carolyn R; Marahiel, Mohamed A; Burkart, Michael D

    2009-01-01

    .... In this study, we developed a crosslinking assay, utilizing bioorthogonal probes compatible with carrier protein modification, for probing the protein interactions between COM domains of NRPS enzymes...

  3. 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.

  4. 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.

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

    Directory of Open Access Journals (Sweden)

    Wuchty Stefan

    2006-05-01

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

  6. 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.

  7. Interaction of Proteins Identified in Human Thyroid Cells

    Science.gov (United States)

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

    2013-01-01

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

  8. Methods for detection of protein-protein and protein-DNA interactions using HaloTag.

    Science.gov (United States)

    Urh, Marjeta; Hartzell, Danette; Mendez, Jacqui; Klaubert, Dieter H; Wood, Keith

    2008-01-01

    HaloTag is a protein fusion tag which was genetically engineered to covalently bind a series of specific synthetic ligands. All ligands carry two groups, the reactive group and the functional/reporter group. The reactive group, the choloroalkane, is the same in all the ligands and is involved in binding to the HaloTag. The functional reporter group is variable and can carry many different moieties including fluorescent dyes, affinity handles like biotin or solid surfaces such as agarose beads. Thus, HaloTag can serve either as a labeling tag or as a protein immobilization tag depending on which ligand is bound to it. Here, we describe a procedure for immobilization of HaloTag fusion proteins and how immobilized proteins can be used to study protein-protein and protein-DNA interactions in vivo and in vitro.

  9. Mammalian CHORD-containing protein 1 is a novel heat shock protein 90-interacting protein.

    Science.gov (United States)

    Wu, Jianchun; Luo, Shouqing; Jiang, Hai; Li, Honglin

    2005-01-17

    With two tandem repeated cysteine- and histidine-rich domains (designated as CHORD), CHORD-containing proteins (CHPs) are a novel family of highly conserved proteins that play important roles in plant disease resistance and animal development. Through interacting with suppressor of the G2 allele of Skp1 (SGT1) and Hsp90, plant CHORD-containing protein RAR1 (required for Mla resistance 1) plays a critical role in disease resistance mediated by multiple R genes. Yet, the physiological function of vertebrate CHORD-containing protein-1 (Chp-1) has been poorly investigated. In this study, we provide the first biochemical evidence demonstrating that mammalian Chp-1 is a novel Hsp90-interacting protein. Mammalian Chp-1 contains two CHORD domains (I and II) and one CS domain (a domain shared by CHORD-containing proteins and SGT1). With sequence and structural similarity to Hsp90 co-chaperones p23 and SGT1, Chp-1 binds to the ATPase domain of Hsp90, but the biochemical property of the interaction is unique. The Chp-1-Hsp90 interaction is independent of ATP and ATPase-coupled conformational change of Hsp90, a feature that distinguishes Chp-1 from p23. Furthermore, it appears that multiple domains of Chp-1 are required for stable Chp-1-Hsp90 interaction. Unlike SGT1 whose CS domain is sufficient for Hsp90 binding, the CS domain of Chp-1 is essential but not sufficient for Hsp90 binding. While the CHORD-I domain of Chp-1 is dispensable for Hsp90 binding, the CHORD-II domain and the linker region are essential. Interestingly, the CHORD-I domain of plant RAR1 protein is solely responsible for Hsp90 binding. The unique Chp-1-Hsp90 interaction may be indicative of a distinct biological activity of Chp-1 and functional diversification of CHORD-containing proteins during evolution.

  10. In silico study of interaction between rice proteins enhanced disease ...

    Indian Academy of Sciences (India)

    To study this interaction, a model of EDS1 and PAD4 proteins from rice was generated and validated with Accelrys DS software version 3.1 using bioinformatics interface. The in silico docking between the two proteins showed a significant protein–protein interaction between rice EDS1 and PAD4, suggesting that they form a ...

  11. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

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

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

    Science.gov (United States)

    Park, Kyunghyun; Kim, Docyong; Ha, Suhyun; Lee, Doheon

    2015-01-01

    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.

  13. Ensemble learning prediction of protein-protein interactions using proteins functional annotations.

    Science.gov (United States)

    Saha, Indrajit; Zubek, Julian; Klingström, Tomas; Forsberg, Simon; Wikander, Johan; Kierczak, Marcin; Maulik, Ujjwal; Plewczynski, Dariusz

    2014-04-01

    Protein-protein interactions are important for the majority of biological processes. A significant number of computational methods have been developed to predict protein-protein interactions using protein sequence, structural and genomic data. Vast experimental data is publicly available on the Internet, but it is scattered across numerous databases. This fact motivated us to create and evaluate new high-throughput datasets of interacting proteins. We extracted interaction data from DIP, MINT, BioGRID and IntAct databases. Then we constructed descriptive features for machine learning purposes based on data from Gene Ontology and DOMINE. Thereafter, four well-established machine learning methods: Support Vector Machine, Random Forest, Decision Tree and Naïve Bayes, were used on these datasets to build an Ensemble Learning method based on majority voting. In cross-validation experiment, sensitivity exceeded 80% and classification/prediction accuracy reached 90% for the Ensemble Learning method. We extended the experiment to a bigger and more realistic dataset maintaining sensitivity over 70%. These results confirmed that our datasets are suitable for performing PPI prediction and Ensemble Learning method is well suited for this task. Both the processed PPI datasets and the software are available at .

  14. Flow Cytometric FRET Analysis of Protein Interactions.

    Science.gov (United States)

    Ujlaky-Nagy, László; Nagy, Péter; Szöllősi, János; Vereb, György

    2018-01-01

    In the past decades, investigation of protein-protein interactions in situ in living or intact cells has gained expanding importance as structure/function relationships proposed from bulk biochemistry and molecular modeling experiments required confirmation at the cellular level. Förster (fluorescence) resonance energy transfer (FRET)-based methods are excellent tools for determining proximity and supramolecular organization of biomolecules at the cell surface or inside the cell. This could well be the basis for the increasing popularity of FRET. In fact, the number of publications exploiting FRET has exploded since the turn of the millennium. Interestingly, most applications are microscope-based, and only a fraction employs flow cytometry, even though the latter offers great statistical power owed to the potentially huge number of individually measured cells. However, with the increased availability of multi-laser flow cytometers, strategies to obtain absolute FRET efficiencies can now be relatively facilely implemented. In this chapter, we intend to provide generally useable protocols for measuring FRET in flow cytometry. After a concise theoretical introduction, recipes are provided for successful labeling techniques and measurement approaches. The simple, quenching-based population-level measurement, the classic ratiometric, intensity-based technique providing cell-by-cell actual FRET efficiencies, and a more advanced version of the latter, allowing for cell-by-cell autofluorescence correction are described. An Excel macro pre-loaded with spectral data of the most commonly used fluorophores is also provided for easy calculation of average FRET efficiencies. Finally, points of caution are given to help design proper experiments and critically interpret the results.

  15. A Least Square Method Based Model for Identifying Protein Complexes in Protein-Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Qiguo Dai

    2014-01-01

    Full Text Available 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.

  16. A Brief Review of RNA-Protein Interaction Database Resources

    Directory of Open Access Journals (Sweden)

    Ying Yi

    2017-01-01

    Full Text Available RNA-protein interactions play critical roles in various biological processes. By collecting and analyzing the RNA-protein interactions and binding sites from experiments and predictions, RNA-protein interaction databases have become an essential resource for the exploration of the transcriptional and post-transcriptional regulatory network. Here, we briefly review several widely used RNA-protein interaction database resources developed in recent years to provide a guide of these databases. The content and major functions in databases are presented. The brief description of database helps users to quickly choose the database containing information they interested. In short, these RNA-protein interaction database resources are continually updated, but the current state shows the efforts to identify and analyze the large amount of RNA-protein interactions.

  17. 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

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

    NARCIS (Netherlands)

    Rahmani, Hossein

    2012-01-01

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

  19. 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

  20. 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.

  1. Interactions of proteins in gels, solutions and on surfaces

    Science.gov (United States)

    Ramasamy, Radha Perumal

    2006-12-01

    The study of protein interaction, identification and separation has applications in various fields relating to Biotechnology. In this research these aspects were investigated. The proteins albumin, casein, poly-L-lysine were studied. FITC and TRITC were used to fluorescently tag the proteins. Confocal microscopy was used to image the interaction of proteins. The migration of fluorescently tagged protein-salt aggregates on solid surfaces during electrophoresis was investigated using Confocal microscopy. The secondary structural modifications of proteins in solutions were investigated using FTIR micro spectroscopic imaging. The size of the colloids formed due to protein-protein interactions as a function of the protein concentrations were studied using DLS and their charges were found using zeta potential measurements. Based on DL.S and zeta potential measurements, a model is proposed for interactions of oppositely charged proteins. The nature of interaction was found using UV - Visual spectroscopy. It was found that oppositely charged proteins formed ionic bonds. It was also found that FITC molecule influenced the surface charge of albumin more than TRITC molecule. The effects of the influence of cell geometries upon Electro Osmotic Flow (EOF) were studied using neutrally charged fluorescent Polystyrene beads. Results showed that tagging proteins with fluorescent molecules influenced their mobility and interactions with other proteins. However no secondary structural modifications of the proteins were observed when oppositely charged proteins interacted. It was also observed that electrostatic interactions made oppositely charged proteins form large aggregates. The EOF was found to be dependent upon the ionic strength of the buffer, conductivity of the solid surfaces, distance from the surface and position of the electrodes in the electrophoretic cell.

  2. Protein-lipid interactions: paparazzi hunting for snap-shots

    NARCIS (Netherlands)

    Haberkant, P.|info:eu-repo/dai/nl/311488749; van Meer, G.|info:eu-repo/dai/nl/068570368

    2009-01-01

    Photoactivatable groups meeting the criterion of minimal perturbance allow the investigation of interactions in biological samples. Here, we review the application of photoactivatable groups in lipids enabling the study of protein-lipid interactions in (biological) membranes. The chemistry of

  3. RAIN: RNA-protein Association and Interaction Networks

    DEFF Research Database (Denmark)

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

    2017-01-01

    is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data...... web interface and all interaction data can be downloaded.......Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) as well. However, integration of ncRNAs into protein association networks...

  4. Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks.

    Science.gov (United States)

    Shen, Xianjun; Yi, Li; Yi, Yang; Yang, Jincai; He, Tingting; Hu, Xiaohua

    2015-01-01

    The identification of protein functional modules would be a great aid in furthering our knowledge of the principles of cellular organization. Most existing algorithms for identifying protein functional modules have a common defect -- once a protein node is assigned to a functional module, there is no chance to move the protein to the other functional modules during the follow-up processes, which lead the erroneous partitioning occurred at previous step to accumulate till to the end. In this paper, we design a new algorithm ADM (Adaptive Density Modularity) to detect protein functional modules based on adaptive density modularity. In ADM algorithm, according to the comparison between external closely associated degree and internal closely associated degree, the partitioning of a protein-protein interaction network into functional modules always evolves quickly to increase the density modularity of the network. The integration of density modularity into the new algorithm not only overcomes the drawback mentioned above, but also contributes to identifying protein functional modules more effectively. The experimental result reveals that the performance of ADM algorithm is superior to many state-of-the-art protein functional modules detection techniques in aspect of the accuracy of prediction. Moreover, the identified protein functional modules are statistically significant in terms of "Biological Process" annotated in Gene Ontology, which provides substantial support for revealing the principles of cellular organization.

  5. Modularity in the evolution of yeast protein interaction network.

    Science.gov (United States)

    Ogishima, Soichi; Tanaka, Hiroshi; Nakaya, Jun

    2015-01-01

    Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution.

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

    Science.gov (United States)

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

    2017-08-01

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

  7. A cytoplasmic protein-protein interaction detection method based on reporter translation.

    Science.gov (United States)

    Renaut, Laurence; Bouayadi, Khalil; Kharrat, Hakim; Mondon, Philippe

    2009-01-15

    One approach to drug discovery involves the targeting of abnormal protein-protein interactions that lead to pathology. We present a new technology allowing the detection of such interactions within the cytoplasm in a yeast-based system. The interaction detection is based on the sequestration of a translation termination factor involved in stop codon recognition. This sequestration inhibits the activity of the factor, thereby permitting the translation of a reporter gene harboring a premature stop codon. This novel cytoplasmic protein-protein interaction (CPPI) detection system should prove to be useful in the characterization of proteins as well as in partner identification, interaction mapping, and drug discovery applications.

  8. Course 1: Physics of Protein-DNA Interaction

    Science.gov (United States)

    Bruinsma, R. F.

    1 Introduction 1.1 The central dogma and bacterial gene expression 1.2 Molecular structure 2 Thermodynamics and kinetics of repressor-DNA interaction 2.1 Thermodynamics and the lac repressor 2.2 Kinetics of repressor-DNA interaction 3 DNA deformability and protein-DNA interaction 3.1 Introduction 3.2 The worm-like chain 3.3 The RST model 4 Electrostatics in water and protein-DNA interaction 4.1 Macro-ions and aqueous electrostatics 4.2 The primitive model 4.3 Manning condensation 4.4 Counter-ion release and non-specific protein-DNA interaction

  9. The dynamic multisite interactions between two intrinsically disordered proteins

    KAUST Repository

    Wu, Shaowen

    2017-05-11

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

  10. Preferential interactions and the effect of protein PEGylation

    DEFF Research Database (Denmark)

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

    2015-01-01

    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.......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...

  11. Receptors, G proteins, and their interactions

    NARCIS (Netherlands)

    Hollmann, Markus W.; Strumper, Danja; Herroeder, Susanne; Durieux, Marcel E.

    2005-01-01

    Membrane receptors coupling to intracellular G proteins (G protein-coupled receptors) form one of the major classes of membrane signaling proteins. They are of great importance to the practice of anesthesiology because they are involved in many systems of relevance to the specialty (cardiovascular

  12. 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.

  13. 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

  14. 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 strong determinant for their function. This has fostered the notion that IDP's bind with low affinity but high specificity. Here we have analyzed available detailed thermodynamic data for protein-protein interactions to put to the test if the thermodynamic profiles of IDP interactions differ from those...... of other protein-protein interactions. We find that ordered proteins and the disordered ones act as non-identical twins operating by similar principles but where the disordered proteins complexes are on average less stable by 2.5 kcal mol(-1)....

  15. 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.

  16. Interactions of carbohydrates and proteins by fluorophore-assisted ...

    Indian Academy of Sciences (India)

    A sensitive, specific, and rapid method for the detection of carbohydrate-protein interactions is demonstrated by fluorophore-assisted carbohydrate electrophoresis (FACE). The procedure is simple and the cost is low. The advantage of this method is that carbohydrate-protein interactions can be easily displayed by FACE, ...

  17. Prediction of protein and nucleic acid interactions

    OpenAIRE

    Cirillo, Davide

    2016-01-01

    The purpose of my doctoral studies has been the development of bioinformatics methods to quantitatively evaluate associations between proteins and nucleic acids (NAs). This thesis aims at providing insights into molecular features and still relatively unknown mechanisms of protein-NAs associations, such as RNA-binding proteins and long noncoding RNAs as well as transcription factors and regulatory DNA elements. In this work, I present two algorithms, catRAPID omics express and PAnDA, for the ...

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

    DEFF Research Database (Denmark)

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

    2017-01-01

    Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap,......Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (In......Web_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism....

  19. 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

  20. 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

  1. 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...... a quantitative characterization of the kinetics and affinity of interactions between GPCRs and one of the best characterized PDZ scaffold proteins, postsynaptic density protein 95 (PSD-95), using fluorescence polarization (FP) and surface plasmon resonance (SPR). By comparing these in vitro findings....... The approach can easily be transferred to other receptors and scaffold proteins and this could help accelerate the discovery and quantitative characterization of GPCR-PDZ interactions....

  2. Protein-lipid interactions: from membrane domains to cellular networks

    National Research Council Canada - National Science Library

    Tamm, Lukas K

    2005-01-01

    ... membranes is the lipid bilayer. Embedded in the fluid lipid bilayer are proteins of various shapes and traits. This volume illuminates from physical, chemical and biological angles the numerous - mostly quite weak - interactions between lipids, proteins, and proteins and lipids that define the delicate, highly dynamic and yet so stable fabri...

  3. Understanding protein–protein interactions by genetic suppression

    Indian Academy of Sciences (India)

    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 ...

  4. Targeting Protein-Protein Interactions with Trimeric Ligands: High Affinity Inhibitors of the MAGUK Protein Family

    DEFF Research Database (Denmark)

    Nissen, Klaus B; Kedström, Linda Maria Haugaard; Wilbek, Theis S

    2015-01-01

    and the related MAGUK proteins contain three consecutive PDZ domains, hence we envisioned that targeting all three PDZ domains simultaneously would lead to more potent and potentially more specific interactions with the MAGUK proteins. Here we describe the design, synthesis and characterization of a series...... of trimeric ligands targeting all three PDZ domains of PSD-95 and the related MAGUK proteins, PSD-93, SAP-97 and SAP-102. Using our dimeric ligands targeting the PDZ1-2 tandem as starting point, we designed novel trimeric ligands by introducing a PDZ3-binding peptide moiety via a cysteine-derivatized NPEG...... linker. The trimeric ligands generally displayed increased affinities compared to the dimeric ligands in fluorescence polarization binding experiments and optimized trimeric ligands showed low nanomolar inhibition towards the four MAGUK proteins, thus being the most potent inhibitors described. Kinetic...

  5. NPIDB: Nucleic acid-Protein Interaction DataBase.

    Science.gov (United States)

    Kirsanov, Dmitry D; Zanegina, Olga N; Aksianov, Evgeniy A; Spirin, Sergei A; Karyagina, Anna S; Alexeevski, Andrei V

    2013-01-01

    The Nucleic acid-Protein Interaction DataBase (http://npidb.belozersky.msu.ru/) contains information derived from structures of DNA-protein and RNA-protein complexes extracted from the Protein Data Bank (3846 complexes in October 2012). It provides a web interface and a set of tools for extracting biologically meaningful characteristics of nucleoprotein complexes. The content of the database is updated weekly. The current version of the Nucleic acid-Protein Interaction DataBase is an upgrade of the version published in 2007. The improvements include a new web interface, new tools for calculation of intermolecular interactions, a classification of SCOP families that contains DNA-binding protein domains and data on conserved water molecules on the DNA-protein interface.

  6. 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.

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

    Science.gov (United States)

    Park, Byungkyu; Han, Kyungsook

    2010-01-18

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

  8. Casein - whey protein interactions in heated milk

    NARCIS (Netherlands)

    Vasbinder, Astrid Jolanda

    2002-01-01

    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

  9. 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...

  10. Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks.

    Science.gov (United States)

    Cao, Renzhi; Cheng, Jianlin

    2016-01-15

    Protein function prediction is an important and challenging problem in bioinformatics and computational biology. Functionally relevant biological information such as protein sequences, gene expression, and protein-protein interactions has been used mostly separately for protein function prediction. One of the major challenges is how to effectively integrate multiple sources of both traditional and new information such as spatial gene-gene interaction networks generated from chromosomal conformation data together to improve protein function prediction. In this work, we developed three different probabilistic scores (MIS, SEQ, and NET score) to combine protein sequence, function associations, and protein-protein interaction and spatial gene-gene interaction networks for protein function prediction. The MIS score is mainly generated from homologous proteins found by PSI-BLAST search, and also association rules between Gene Ontology terms, which are learned by mining the Swiss-Prot database. The SEQ score is generated from protein sequences. The NET score is generated from protein-protein interaction and spatial gene-gene interaction networks. These three scores were combined in a new Statistical Multiple Integrative Scoring System (SMISS) to predict protein function. We tested SMISS on the data set of 2011 Critical Assessment of Function Annotation (CAFA). The method performed substantially better than three base-line methods and an advanced method based on protein profile-sequence comparison, profile-profile comparison, and domain co-occurrence networks according to the maximum F-measure. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. Protein interaction discovery using parallel analysis of translated ORFs (PLATO).

    Science.gov (United States)

    Zhu, Jian; Larman, H Benjamin; Gao, Geng; Somwar, Romel; Zhang, Zijuan; Laserson, Uri; Ciccia, Alberto; Pavlova, Natalya; Church, George; Zhang, Wei; Kesari, Santosh; Elledge, Stephen J

    2013-04-01

    Identifying physical interactions between proteins and other molecules is a critical aspect of biological analysis. Here we describe PLATO, an in vitro method for mapping such interactions by affinity enrichment of a library of full-length open reading frames displayed on ribosomes, followed by massively parallel analysis using DNA sequencing. We demonstrate the broad utility of the method for human proteins by identifying known and previously unidentified interacting partners of LYN kinase, patient autoantibodies, and the small-molecules gefitinib and dasatinib.

  12. Interactome Data and Databases: Different Types of Protein Interaction

    Directory of Open Access Journals (Sweden)

    Alberto de Luis

    2006-04-01

    Full Text Available In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological high-throughput experimental methods and bioinformatic genome-wide computational methods. Such breakthroughs are producing huge amounts of new data that need to be carefully analysed to obtain correct and useful scientific knowledge. One of the fields where this advance has become more intense is the study of the network of ‘protein–protein interactions’, i.e. the ‘interactome’. In this short review we comment on the main data and databases produced in this field in last 5 years. We also present a rationalized scheme of biological definitions that will be useful for a better understanding and interpretation of ‘what a protein–protein interaction is’ and ‘which types of protein–protein interactions are found in a living cell’. Finally, we comment on some assignments of interactome data to defined types of protein interaction and we present a new bioinformatic tool called APIN (Agile Protein Interaction Network browser, which is in development and will be applied to browsing protein interaction databases.

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

    Science.gov (United States)

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

    2017-04-01

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

  14. 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

  15. 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.

  16. Quantitative affinity purification mass spectrometry: a versatile technology to study protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Katrina eMeyer

    2015-07-01

    Full Text Available While the genomic revolution has dramatically accelerated the discovery of disease-associated genes, the functional characterization of the corresponding proteins lags behind. Most proteins fulfill their tasks in complexes with other proteins, and analysis of Protein-Protein Interactions (PPIs can therefore provide insights into protein function. Several methods can be used to generate large-scale protein interaction networks. However, most of these approaches are not quantitative and therefore cannot reveal how perturbations affect the network. Here, we illustrate how a clever combination of quantitative mass spectrometry with different biochemical methods provides a rich toolkit to study different aspects of PPIs including topology, subunit stoichiometry, and dynamic behavior.

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

    Energy Technology Data Exchange (ETDEWEB)

    Tsapikouni, Theodora S. [Laboratory of Biomechanics and Biomedical Engineering, Mechanical Engineering and Aeronautics Department, University of Patras, Patras 26504 (Greece); Missirlis, Yannis F. [Laboratory of Biomechanics and Biomedical Engineering, Mechanical Engineering and Aeronautics Department, University of Patras, Patras 26504 (Greece)], E-mail: misirlis@mech.upatras.gr

    2008-08-25

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

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

    Directory of Open Access Journals (Sweden)

    Kurt A. Jellinger

    2011-01-01

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

  19. (S)Pinning down protein interactions by NMR

    DEFF Research Database (Denmark)

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

    2017-01-01

    all types of protein reactions, which can span orders of magnitudes in affinities, reaction rates and lifetimes of states. As the more versatile technique, solution NMR spectroscopy offers a remarkable catalogue of methods that can be successfully applied to the quantitative as well as qualitative...... descriptions of protein interactions. In this review we provide an easy-access approach to NMR for the non-NMR specialist and describe how and when solution state NMR spectroscopy is the method of choice for addressing protein ligand interaction. We describe very briefly the theoretical background...... and illustrate simple protein-ligand interactions and as well as typical strategies for measuring binding constants using NMR spectroscopy. Finally, this review provides examples of caveats of the method as well as the options to improve the outcome of an NMR analysis of a protein interaction reaction...

  20. Protein Charge and Mass Contribute to the Spatio-temporal Dynamics of Protein-Protein Interactions in a Minimal Proteome

    Science.gov (United States)

    Xu, Yu; Wang, Hong; Nussinov, Ruth; Ma, Buyong

    2013-01-01

    We constructed and simulated a ‘minimal proteome’ model using Langevin dynamics. It contains 206 essential protein types which were compiled from the literature. For comparison, we generated six proteomes with randomized concentrations. We found that the net charges and molecular weights of the proteins in the minimal genome are not random. The net charge of a protein decreases linearly with molecular weight, with small proteins being mostly positively charged and large proteins negatively charged. The protein copy numbers in the minimal genome have the tendency to maximize the number of protein-protein interactions in the network. Negatively charged proteins which tend to have larger sizes can provide large collision cross-section allowing them to interact with other proteins; on the other hand, the smaller positively charged proteins could have higher diffusion speed and are more likely to collide with other proteins. Proteomes with random charge/mass populations form less stable clusters than those with experimental protein copy numbers. Our study suggests that ‘proper’ populations of negatively and positively charged proteins are important for maintaining a protein-protein interaction network in a proteome. It is interesting to note that the minimal genome model based on the charge and mass of E. Coli may have a larger protein-protein interaction network than that based on the lower organism M. pneumoniae. PMID:23420643

  1. Mirin: identifying microRNA regulatory modules in protein-protein interaction networks

    National Research Council Canada - National Science Library

    Yang, Ken-Chi; Hsu, Chia-Lang; Lin, Chen-Ching; Juan, Hsueh-Fen; Huang, Hsuan-Cheng

    2014-01-01

    .... Mirin is a web-based application suitable for identifying functional modules from protein-protein interaction networks regulated by aberrant miRNAs under user-defined biological conditions such as cancers...

  2. 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

  3. Multitask Matrix Completion for Learning Protein Interactions Across Diseases.

    Science.gov (United States)

    Kshirsagar, Meghana; Murugesan, Keerthiram; Carbonell, Jaime G; Klein-Seetharaman, Judith

    2017-06-01

    Disease-causing pathogens such as viruses introduce their proteins into the host cells in which they interact with the host's proteins, enabling the virus to replicate inside the host. These interactions between pathogen and host proteins are key to understanding infectious diseases. Often multiple diseases involve phylogenetically related or biologically similar pathogens. Here we present a multitask learning method to jointly model interactions between human proteins and three different but related viruses: Hepatitis C, Ebola virus, and Influenza A. Our multitask matrix completion-based model uses a shared low-rank structure in addition to a task-specific sparse structure to incorporate the various interactions. We obtain between 7 and 39 percentage points improvement in predictive performance over prior state-of-the-art models. We show how our model's parameters can be interpreted to reveal both general and specific interaction-relevant characteristics of the viruses. Our code is available online.

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

    Directory of Open Access Journals (Sweden)

    Emilie eGauliard

    2015-02-01

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

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

    Directory of Open Access Journals (Sweden)

    Irene Sendiña-Nadal

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

  6. 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....

  7. A protein domain interaction interface database: InterPare.

    Science.gov (United States)

    Gong, Sungsam; Park, Changbum; Choi, Hansol; Ko, Junsu; Jang, Insoo; Lee, Jungsul; Bolser, Dan M; Oh, Donghoon; Kim, Deok-Soo; Bhak, Jong

    2005-08-25

    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. 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. 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-domain interaction interfaces and 12,758 intra

  8. 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

  9. 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

  10. Electrostatic Control of Protein-Surface Interactions

    Science.gov (United States)

    2013-10-21

    noncovalently -assembled superstructures from the controlled aggregation of β-strand peptides into fibrils and fibers. These structures are predicted to...interactions (such as hydrogen bonding ) with important small molecule nutrients This could be accomplished by synthesizing unnatural amino acids into the...in Figure 3, which demonstrates how the noncovalent interaction of peptides with various functional groups on the surface will impact the adsorption

  11. Discovering novel protein-protein interactions by measuring the protein semantic similarity from the biomedical literature.

    Science.gov (United States)

    Chiang, Jung-Hsien; Ju, Jiun-Huang

    2014-12-01

    Protein-protein interactions (PPIs) are involved in the majority of biological processes. Identification of PPIs is therefore one of the key aims of biological research. Although there are many databases of PPIs, many other unidentified PPIs could be buried in the biomedical literature. Therefore, automated identification of PPIs from biomedical literature repositories could be used to discover otherwise hidden interactions. Search engines, such as Google, have been successfully applied to measure the relatedness among words. Inspired by such approaches, we propose a novel method to identify PPIs through semantic similarity measures among protein mentions. We define six semantic similarity measures as features based on the page counts retrieved from the MEDLINE database. A machine learning classifier, Random Forest, is trained using the above features. The proposed approach achieve an averaged micro-F of 71.28% and an averaged macro-F of 64.03% over five PPI corpora, an improvement over the results of using only the conventional co-occurrence feature (averaged micro-F of 68.79% and an averaged macro-F of 60.49%). A relation-word reinforcement further improves the averaged micro-F to 71.3% and averaged macro-F to 65.12%. Comparing the results of the current work with other studies on the AIMed corpus (ranging from 77.58% to 85.1% in micro-F, 62.18% to 76.27% in macro-F), we show that the proposed approach achieves micro-F of 81.88% and macro-F of 64.01% without the use of sophisticated feature extraction. Finally, we manually examine the newly discovered PPI pairs based on a literature review, and the results suggest that our approach could extract novel protein-protein interactions.

  12. Machine Learning Analysis Identifies Drosophila Grunge/Atrophin as an Important Learning and Memory Gene Required for Memory Retention and Social Learning

    Directory of Open Access Journals (Sweden)

    Balint Z. Kacsoh

    2017-11-01

    Full Text Available High-throughput experiments are becoming increasingly common, and scientists must balance hypothesis-driven experiments with genome-wide data acquisition. We sought to predict novel genes involved in Drosophila learning and long-term memory from existing public high-throughput data. We performed an analysis using PILGRM, which analyzes public gene expression compendia using machine learning. We evaluated the top prediction alongside genes involved in learning and memory in IMP, an interface for functional relationship networks. We identified Grunge/Atrophin (Gug/Atro, a transcriptional repressor, histone deacetylase, as our top candidate. We find, through multiple, distinct assays, that Gug has an active role as a modulator of memory retention in the fly and its function is required in the adult mushroom body. Depletion of Gug specifically in neurons of the adult mushroom body, after cell division and neuronal development is complete, suggests that Gug function is important for memory retention through regulation of neuronal activity, and not by altering neurodevelopment. Our study provides a previously uncharacterized role for Gug as a possible regulator of neuronal plasticity at the interface of memory retention and memory extinction.

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

    Science.gov (United States)

    Meckes, David G

    2014-01-01

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

  14. 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.

  15. 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.

  16. Yeast Interacting Proteins Database: YDL239C, YGR268C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

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

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

    Science.gov (United States)

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

    2006-04-15

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

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

    Directory of Open Access Journals (Sweden)

    Hongbo Zhu

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

  19. Prediction of virus-host protein-protein interactions mediated by short linear motifs.

    Science.gov (United States)

    Becerra, Andrés; Bucheli, Victor A; Moreno, Pedro A

    2017-03-09

    Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1). We implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them - with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID). The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions. With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions. We have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry.

  20. 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.

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    Mu Gao

    2009-03-01

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

  3. Gene essentiality and the topology of protein interaction networks

    Science.gov (United States)

    Coulomb, Stéphane; Bauer, Michel; Bernard, Denis; Marsolier-Kergoat, Marie-Claude

    2005-01-01

    The mechanistic bases for gene essentiality and for cell mutational resistance have long been disputed. The recent availability of large protein interaction databases has fuelled the analysis of protein interaction networks and several authors have proposed that gene dispensability could be strongly related to some topological parameters of these networks. However, many results were based on protein interaction data whose biases were not taken into account. In this article, we show that the essentiality of a gene in yeast is poorly related to the number of interactants (or degree) of the corresponding protein and that the physiological consequences of gene deletions are unrelated to several other properties of proteins in the interaction networks, such as the average degrees of their nearest neighbours, their clustering coefficients or their relative distances. We also found that yeast protein interaction networks lack degree correlation, i.e. a propensity for their vertices to associate according to their degrees. Gene essentiality and more generally cell resistance against mutations thus seem largely unrelated to many parameters of protein network topology. PMID:16087428

  4. Specificity and evolvability in eukaryotic protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Pedro Beltrao

    2007-02-01

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

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

    Science.gov (United States)

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

    2017-07-19

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

  6. 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.

  7. Evolutionary analysis and interaction prediction for protein-protein interaction network in geometric space.

    Science.gov (United States)

    Huang, Lei; Liao, Li; Wu, Cathy H

    2017-01-01

    Prediction of protein-protein interaction (PPI) remains a central task in systems biology. With more PPIs identified, forming PPI networks, it has become feasible and also imperative to study PPIs at the network level, such as evolutionary analysis of the networks, for better understanding of PPI networks and for more accurate prediction of pairwise PPIs by leveraging the information gained at the network level. In this work we developed a novel method that enables us to incorporate evolutionary information into geometric space to improve PPI prediction, which in turn can be used to select and evaluate various evolutionary models. The method is tested with cross-validation using human PPI network and yeast PPI network data. The results show that the accuracy of PPI prediction measured by ROC score is increased by up to 14.6%, as compared to a baseline without using evolutionary information. The results also indicate that our modified evolutionary model DANEOsf-combining a gene duplication/neofunctionalization model and scale-free model-has a better fitness and prediction efficacy for these two PPI networks. The improved PPI prediction performance may suggest that our DANEOsf evolutionary model can uncover the underlying evolutionary mechanism for these two PPI networks better than other tested models. Consequently, of particular importance is that our method offers an effective way to select evolutionary models that best capture the underlying evolutionary mechanisms, evaluating the fitness of evolutionary models from the perspective of PPI prediction on real PPI networks.

  8. Interaction of puroindolines with gluten proteins

    Science.gov (United States)

    The effect of puroindolines (PINs) on structural characteristics of gluten proteins was investigated in Triticum turgidum ssp. durum (cv. Svevo) and Triticum aestivum (cv. Alpowa) and from their respective derivatives in which PIN genes were expressed (Soft Svevo) or the distal end of the short arm ...

  9. compartment-specific interactions of Hox proteins

    Indian Academy of Sciences (India)

    Unknown

    was on understanding gene sequences and function. These studies showed that important regulatory proteins are highly conserved in sequence and, often, in function, in organisms as diverse as mice and worms. Thus, novel genes alone do not make one organism different from another. Instead, many findings have made ...

  10. 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.

  11. Direct Identification of Protein-Protein Interactions by Single-Molecule Force Spectroscopy.

    Science.gov (United States)

    Vera, Andrés M; Carrión-Vázquez, Mariano

    2016-11-02

    Single-molecule force spectroscopy based on atomic force microscopy (AFM-SMFS) has allowed the measurement of the intermolecular forces involved in protein-protein interactions at the molecular level. While intramolecular interactions are routinely identified directly by the use of polyprotein fingerprinting, there is a lack of a general method to directly identify single-molecule intermolecular unbinding events. Here, we have developed an internally controlled strategy to measure protein-protein interactions by AFM-SMFS that allows the direct identification of dissociation force peaks while ensuring single-molecule conditions. Single-molecule identification is assured by polyprotein fingerprinting while the intermolecular interaction is reported by a characteristic increase in contour length released after bond rupture. The latter is due to the exposure to force of a third protein that covalently connects the interacting pair. We demonstrate this strategy with a cohesin-dockerin interaction. © 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Progress and potential of Drosophila protein interaction maps.

    Science.gov (United States)

    Stanyon, C A; Finley, R L

    2000-11-01

    Protein-protein interactions mediate many important cellular processes and are central to the mechanisms by which most proteins function. Charting the interactions among the proteins involved in a process has been an essential step in characterising the function of proteins and pathways. The yeast two-hybrid system is one approach to detecting protein interactions that can now be scaled-up to assay large sets of proteins systematically, such as those being identified from genome sequencing efforts. The system has already been extensively used to acquire data that have enabled construction of large protein interaction maps (PIMs). When combined with other data, including data being generated by other functional genomics approaches, PIMs help assign function to new proteins and delineate functional networks. Hypotheses generated in such a manner often must be tested by additional experimentation, preferably in vivo. The model organism Drosophila melanogaster has a wealth of genetic and bioinformatic tools available for such analyses. The proteome predicted from the recently sequenced Drosophila genome indicates that humans have more genes in common with Drosophila than with any other invertebrate model organism characterised to date. Thus, the construction and characterisation of Drosophila PIMs will help define the functions of many conserved genes and pathways, and will provide the pharmaceutical research industry with invaluable data to assist with drug target identification and validation.

  13. Protein function prediction using guilty by association from interaction networks.

    Science.gov (United States)

    Piovesan, Damiano; Giollo, Manuel; Ferrari, Carlo; Tosatto, Silvio C E

    2015-12-01

    Protein function prediction from sequence using the Gene Ontology (GO) classification is useful in many biological problems. It has recently attracted increasing interest, thanks in part to the Critical Assessment of Function Annotation (CAFA) challenge. In this paper, we introduce Guilty by Association on STRING (GAS), a tool to predict protein function exploiting protein-protein interaction networks without sequence similarity. The assumption is that whenever a protein interacts with other proteins, it is part of the same biological process and located in the same cellular compartment. GAS retrieves interaction partners of a query protein from the STRING database and measures enrichment of the associated functional annotations to generate a sorted list of putative functions. A performance evaluation based on CAFA metrics and a fair comparison with optimized BLAST similarity searches is provided. The consensus of GAS and BLAST is shown to improve overall performance. The PPI approach is shown to outperform similarity searches for biological process and cellular compartment GO predictions. Moreover, an analysis of the best practices to exploit protein-protein interaction networks is also provided.

  14. A split luciferase complementation assay for studying in vivo protein-protein interactions in filamentous ascomycetes.

    Science.gov (United States)

    Kim, Hee-Kyoung; Cho, Eun Ji; Jo, Seong mi; Sung, Bo Reum; Lee, Seunghoon; Yun, Sung-Hwan

    2012-06-01

    Protein-protein interactions play important roles in controlling many cellular events. To date, several techniques have been developed for detection of protein-protein interactions in living cells, among which split luciferase complementation has been applied in animal and plant cells. Here, we examined whether the split luciferase assay could be used in filamentous ascomycetes, such as Gibberella zeae and Cochliobolus heterostrophus. The coding sequences of two strongly interacting proteins (the F-box protein, FBP1, and its partner SKP1) in G. zeae, under the control of the cryparin promoter from Cryphonectria parasitica, were translationally fused to the C- and N-terminal fragments of firefly luciferase (luc), respectively. Each fusion product inserted into a fungal transforming vector carrying the gene for resistance to either geneticin or hygromycin B, was transformed into both fungi. We detected complementation of split luciferase proteins driven by interaction of the two fungal proteins with a high luminescence intensity-to-background ratio only in the fungal transformants expressing both N-luc and C-luc fusion constructs. Using this system, we also confirmed a novel protein interaction between transcription factors, GzMCM1 and FST12 in G. zeae, which could hardly be proven by the yeast two-hybrid method. This is the first study demonstrating that monitoring of split luciferase complementation is a sensitive and efficient method of studying in vivo protein-protein interactions in filamentous ascomycetes.

  15. Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction

    Science.gov (United States)

    Yeger-Lotem, Esti; Sattath, Shmuel; Kashtan, Nadav; Itzkovitz, Shalev; Milo, Ron; Pinter, Ron Y.; Alon, Uri; Margalit, Hanah

    2004-04-01

    Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.

  16. An Evolutionarily Conserved Innate Immunity Protein Interaction Network*

    Science.gov (United States)

    De Arras, Lesly; Seng, Amara; Lackford, Brad; Keikhaee, Mohammad R.; Bowerman, Bruce; Freedman, Jonathan H.; Schwartz, David A.; Alper, Scott

    2013-01-01

    The innate immune response plays a critical role in fighting infection; however, innate immunity also can affect the pathogenesis of a variety of diseases, including sepsis, asthma, cancer, and atherosclerosis. To identify novel regulators of innate immunity, we performed comparative genomics RNA interference screens in the nematode Caenorhabditis elegans and mouse macrophages. These screens have uncovered many candidate regulators of the response to lipopolysaccharide (LPS), several of which interact physically in multiple species to form an innate immunity protein interaction network. This protein interaction network contains several proteins in the canonical LPS-responsive TLR4 pathway as well as many novel interacting proteins. Using RNAi and overexpression studies, we show that almost every gene in this network can modulate the innate immune response in mouse cell lines. We validate the importance of this network in innate immunity regulation in vivo using available mutants in C. elegans and mice. PMID:23209288

  17. Inferring High-Confidence Human Protein-Protein Interactions

    Science.gov (United States)

    2012-01-01

    bound 2 44 33.2 15615 9129.8 FANCA 217 Fanconi anemia , complementation A FANCG 143 Fanconi anemia , complementation G 43 117.3 1808 2226.3 EGFR 626...Degree), as well as the overall de - gree distribution for the entire network (All). Selecting highly ranked subsets of PPIs, using either IDBOS or...help clarify the de - pendence on confidence on topological and biological prop- erties associated with human protein networks. Materials and methods

  18. 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...

  19. AAV Vectors for FRET-Based Analysis of Protein-Protein Interactions in Photoreceptor Outer Segments.

    Science.gov (United States)

    Becirovic, Elvir; Böhm, Sybille; Nguyen, Ong N P; Riedmayr, Lisa M; Hammelmann, Verena; Schön, Christian; Butz, Elisabeth S; Wahl-Schott, Christian; Biel, Martin; Michalakis, Stylianos

    2016-01-01

    Fluorescence resonance energy transfer (FRET) is a powerful method for the detection and quantification of stationary and dynamic protein-protein interactions. Technical limitations have hampered systematic in vivo FRET experiments to study protein-protein interactions in their native environment. Here, we describe a rapid and robust protocol that combines adeno-associated virus (AAV) vector-mediated in vivo delivery of genetically encoded FRET partners with ex vivo FRET measurements. The method was established on acutely isolated outer segments of murine rod and cone photoreceptors and relies on the high co-transduction efficiency of retinal photoreceptors by co-delivered AAV vectors. The procedure can be used for the systematic analysis of protein-protein interactions of wild type or mutant outer segment proteins in their native environment. Conclusively, our protocol can help to characterize the physiological and pathophysiological relevance of photoreceptor specific proteins and, in principle, should also be transferable to other cell types.

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

    Science.gov (United States)

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

    2017-09-18

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

  1. Detecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Hao Wu

    Full Text Available In this paper, we present a novel rough-fuzzy clustering (RFC method to detect overlapping protein complexes in protein-protein interaction (PPI networks. RFC focuses on fuzzy relation model rather than graph model by integrating fuzzy sets and rough sets, employs the upper and lower approximations of rough sets to deal with overlapping complexes, and calculates the number of complexes automatically. Fuzzy relation between proteins is established and then transformed into fuzzy equivalence relation. Non-overlapping complexes correspond to equivalence classes satisfying certain equivalence relation. To obtain overlapping complexes, we calculate the similarity between one protein and each complex, and then determine whether the protein belongs to one or multiple complexes by computing the ratio of each similarity to maximum similarity. To validate RFC quantitatively, we test it in Gavin, Collins, Krogan and BioGRID datasets. Experiment results show that there is a good correspondence to reference complexes in MIPS and SGD databases. Then we compare RFC with several previous methods, including ClusterONE, CMC, MCL, GCE, OSLOM and CFinder. Results show the precision, sensitivity and separation are 32.4%, 42.9% and 81.9% higher than mean of the five methods in four weighted networks, and are 0.5%, 11.2% and 66.1% higher than mean of the six methods in five unweighted networks. Our method RFC works well for protein complexes detection and provides a new insight of network division, and it can also be applied to identify overlapping community structure in social networks and LFR benchmark networks.

  2. 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.

  3. Screening of cellular proteins that interact with the classical swine ...

    Indian Academy of Sciences (India)

    2014-01-27

    Jan 27, 2014 ... to screen for CSFV NS5A interactive proteins in the cDNA library of the swine umbilical vein endothelial cell. (SUVEC). Alignment with the NCBI database revealed 16 interactive proteins: DDX5, PSMC3, NAV1, PHF5A,. GNB2L1, CSDE1, HSPA8, BRMS1, PPP2R3C, AIP, TMED10, POLR1C, TMEM70, ...

  4. Redundancies in Large-scale Protein Interaction Networks

    OpenAIRE

    Samanta, Manoj Pratim; Liang, Shoudan

    2003-01-01

    Understanding functional associations among genes discovered in sequencing projects is a key issue in post-genomic biology. However, reliable interpretation of the protein interaction data has been difficult. In this work, we show that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals more than 2800 reliable functional associations, 29%...

  5. 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

  6. 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.

  7. Yeast Interacting Proteins Database: YML109W, YGL190C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available sential regulatory subunit B of protein phosphatase 2A, which has multiple roles ...-essential regulatory subunit B of protein phosphatase 2A, which has multiple roles in mitosis and protein b

  8. 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; computational...rotein of unknown function containing a Vps9 domain; computational analysis of large-scale protein-protein i

  9. DockAnalyse: an application for the analysis of protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Cedano Juan

    2010-10-01

    Full Text Available Abstract Background Is it possible to identify what the best solution of a docking program is? The usual answer to this question is the highest score solution, but interactions between proteins are dynamic processes, and many times the interaction regions are wide enough to permit protein-protein interactions with different orientations and/or interaction energies. In some cases, as in a multimeric protein complex, several interaction regions are possible among the monomers. These dynamic processes involve interactions with surface displacements between the proteins to finally achieve the functional configuration of the protein complex. Consequently, there is not a static and single solution for the interaction between proteins, but there are several important configurations that also have to be analyzed. Results To extract those representative solutions from the docking output datafile, we have developed an unsupervised and automatic clustering application, named DockAnalyse. This application is based on the already existing DBscan clustering method, which searches for continuities among the clusters generated by the docking output data representation. The DBscan clustering method is very robust and, moreover, solves some of the inconsistency problems of the classical clustering methods like, for example, the treatment of outliers and the dependence of the previously defined number of clusters. Conclusions DockAnalyse makes the interpretation of the docking solutions through graphical and visual representations easier by guiding the user to find the representative solutions. We have applied our new approach to analyze several protein interactions and model the dynamic protein interaction behavior of a protein complex. DockAnalyse might also be used to describe interaction regions between proteins and, therefore, guide future flexible dockings. The application (implemented in the R package is accessible.

  10. 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

  11. 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.

  12. SLIDER: A Generic Metaheuristic for the Discovery of Correlated Motifs in Protein-Protein Interaction Networks

    NARCIS (Netherlands)

    Boyen, P.; Dyck, van D.; Neven, F.; Ham, van R.C.H.J.; Dijk, van A.D.J.

    2011-01-01

    Correlated motif mining (CMM) is the problem of finding overrepresented pairs of patterns, called motifs, in sequences of interacting proteins. Algorithmic solutions for CMM thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a

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

    DEFF Research Database (Denmark)

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

    2003-01-01

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

  14. 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. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. GPCR-interacting proteins (GIPs): nature and functions.

    Science.gov (United States)

    Bockaert, J; Roussignol, G; Bécamel, C; Gavarini, S; Joubert, L; Dumuis, A; Fagni, L; Marin, P

    2004-11-01

    The simplistic idea that seven transmembrane receptors are single monomeric proteins that interact with heterotrimeric G-proteins after agonist binding is definitively out of date. Indeed, GPCRs (G-protein-coupled receptors) are part of multiprotein networks organized around scaffolding proteins. These GIPs (GPCR-interacting proteins) are either transmembrane or cytosolic proteins. Proteomic approaches can be used to get global pictures of these 'receptosomes'. This approach allowed us to identify direct but also indirect binding partners of serotonin receptors. GIPs are involved in a wide range of functions including control of the targeting, trafficking and signalling of GPCRs. One of them, Shank, which is a secondary and tertiary partner of metabotropic and ionotropic glutamate receptors, respectively, can induce the formation of a whole functional glutamate 'receptosome' and the structure to which it is associated, the dendritic spine.

  16. Mimicking Intermolecular Interactions of Tight Protein-Protein Complexes for Small-Molecule Antagonists.

    Science.gov (United States)

    Xu, David; Bum-Erdene, Khuchtumur; Si, Yubing; Zhou, Donghui; Ghozayel, Mona K; Meroueh, Samy O

    2017-11-08

    Tight protein-protein interactions (Kd 1000 Å2 ) are highly challenging to disrupt with small molecules. Historically, the design of small molecules to inhibit protein-protein interactions has focused on mimicking the position of interface protein ligand side chains. Here, we explore mimicry of the pairwise intermolecular interactions of the native protein ligand with residues of the protein receptor to enrich commercial libraries for small-molecule inhibitors of tight protein-protein interactions. We use the high-affinity interaction (Kd =1 nm) between the urokinase receptor (uPAR) and its ligand urokinase (uPA) to test our methods. We introduce three methods for rank-ordering small molecules docked to uPAR: 1) a new fingerprint approach that represents uPA's pairwise interaction energies with uPAR residues; 2) a pharmacophore approach to identify small molecules that mimic the position of uPA interface residues; and 3) a combined fingerprint and pharmacophore approach. Our work led to small molecules with novel chemotypes that inhibited a tight uPAR⋅uPA protein-protein interaction with single-digit micromolar IC50 values. We also report the extensive work that identified several of the hits as either lacking stability, thiol reactive, or redox active. This work suggests that mimicking the binding profile of the native ligand and the position of interface residues can be an effective strategy to enrich commercial libraries for small-molecule inhibitors of tight protein-protein interactions. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

    Lifescience Database Archive (English)

    Full Text Available ling Golgi proteins, forming lumenal membranes and sorting ubiquitinated proteins destined for degradation; ..., as well as for recycling of Golgi proteins and formation of lumenal membranes Rows with this prey as prey ...1p; required for recycling Golgi proteins, forming lumenal membranes and sorting ubiquitinated proteins dest...degradation, as well as for recycling of Golgi proteins and formation of lumenal membranes

  18. 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...

  19. Diverse role of CBL-interacting protein kinases in plant

    Indian Academy of Sciences (India)

    admin

    Debasis Chattopadhyay, NIPGR, New Delhi. Diverse role of CBL-interacting protein kinases in plant. Most of the extracellular and intrinsic signals elicit changes in cellular calcium ion. (Ca2+) in plants and animals. Ca2+ sensor proteins transmit signals in Ca2+-dependent manner. In addition to several such Ca2+ sensors, ...

  20. Prediction of localization and interactions of apoptotic proteins

    Directory of Open Access Journals (Sweden)

    Matula Pavel

    2009-07-01

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

  1. 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...

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

    Science.gov (United States)

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

    2013-03-01

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

  3. Yeast Interacting Proteins Database: YNL086W, YKL061W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available fluorescent protein (GFP)-fusion protein localizes to endosomes Rows with this bait as bait (3) Rows with this...fluorescent protein (GFP)-fusion protein localizes to the endosome Rows with this prey as prey (2) Rows with this...fluorescent protein (GFP)-fusion protein localizes to endosomes Rows with this bait as bait Rows with this bait...fluorescent protein (GFP)-fusion protein localizes to the endosome Rows with this prey as prey Rows with this prey

  4. Evaluation of physical and functional protein-protein interaction prediction methods for detecting biological pathways.

    Science.gov (United States)

    Muley, Vijaykumar Yogesh; Ranjan, Akash

    2013-01-01

    Cellular activities are governed by the physical and the functional interactions among several proteins involved in various biological pathways. With the availability of sequenced genomes and high-throughput experimental data one can identify genome-wide protein-protein interactions using various computational techniques. Comparative assessments of these techniques in predicting protein interactions have been frequently reported in the literature but not their ability to elucidate a particular biological pathway. Towards the goal of understanding the prediction capabilities of interactions among the specific biological pathway proteins, we report the analyses of 14 biological pathways of Escherichia coli catalogued in KEGG database using five protein-protein functional linkage prediction methods. These methods are phylogenetic profiling, gene neighborhood, co-presence of orthologous genes in the same gene clusters, a mirrortree variant, and expression similarity. Our results reveal that the prediction of metabolic pathway protein interactions continues to be a challenging task for all methods which possibly reflect flexible/independent evolutionary histories of these proteins. These methods have predicted functional associations of proteins involved in amino acids, nucleotide, glycans and vitamins & co-factors pathways slightly better than the random performance on carbohydrate, lipid and energy metabolism. We also make similar observations for interactions involved among the environmental information processing proteins. On the contrary, genetic information processing or specialized processes such as motility related protein-protein linkages that occur in the subset of organisms are predicted with comparable accuracy. Metabolic pathways are best predicted by using neighborhood of orthologous genes whereas phyletic pattern is good enough to reconstruct central dogma pathway protein interactions. We have also shown that the effective use of a particular prediction

  5. Evaluation of physical and functional protein-protein interaction prediction methods for detecting biological pathways.

    Directory of Open Access Journals (Sweden)

    Vijaykumar Yogesh Muley

    Full Text Available BACKGROUND: Cellular activities are governed by the physical and the functional interactions among several proteins involved in various biological pathways. With the availability of sequenced genomes and high-throughput experimental data one can identify genome-wide protein-protein interactions using various computational techniques. Comparative assessments of these techniques in predicting protein interactions have been frequently reported in the literature but not their ability to elucidate a particular biological pathway. METHODS: Towards the goal of understanding the prediction capabilities of interactions among the specific biological pathway proteins, we report the analyses of 14 biological pathways of Escherichia coli catalogued in KEGG database using five protein-protein functional linkage prediction methods. These methods are phylogenetic profiling, gene neighborhood, co-presence of orthologous genes in the same gene clusters, a mirrortree variant, and expression similarity. CONCLUSIONS: Our results reveal that the prediction of metabolic pathway protein interactions continues to be a challenging task for all methods which possibly reflect flexible/independent evolutionary histories of these proteins. These methods have predicted functional associations of proteins involved in amino acids, nucleotide, glycans and vitamins & co-factors pathways slightly better than the random performance on carbohydrate, lipid and energy metabolism. We also make similar observations for interactions involved among the environmental information processing proteins. On the contrary, genetic information processing or specialized processes such as motility related protein-protein linkages that occur in the subset of organisms are predicted with comparable accuracy. Metabolic pathways are best predicted by using neighborhood of orthologous genes whereas phyletic pattern is good enough to reconstruct central dogma pathway protein interactions. We have also

  6. Identification of In Planta Protein-Protein Interactions Using IP-MS.

    Science.gov (United States)

    Jamge, Suraj; Angenent, Gerco C; Bemer, Marian

    2018-01-01

    Gene regulation by transcription factors involves complex protein interaction networks, which include chromatin remodeling and modifying proteins as an integral part. Decoding these protein interactions is crucial for our understanding of chromatin-mediated gene regulation. Here, we describe a method for the immunoprecipitation of in planta nuclear protein complexes followed by mass spectrometry (IP-MS) to identify interactions between transcription factors and chromatin remodelers/modifiers in plants. In addition to a step-by-step bench protocol for immunoprecipitation and subsequent mass spectrometry, we provide guidelines and pointers on necessary controls and data analysis approaches.

  7. Water-mediated ionic interactions in protein structures

    Indian Academy of Sciences (India)

    is defined as when one or more water molecules mediate an interaction between a pair of charged residues. For example, disruption of surface salt bridges (a class of ionic interactions) by water molecules in proteins permits protein–DNA inter- actions (Grove 2003) because it creates the cationic surface complementary to ...

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

    Directory of Open Access Journals (Sweden)

    Greenblatt Jack

    2006-07-01

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

  9. Exosome engineering for efficient intracellular delivery of soluble proteins using optically reversible protein-protein interaction module.

    Science.gov (United States)

    Yim, Nambin; Ryu, Seung-Wook; Choi, Kyungsun; Lee, Kwang Ryeol; Lee, Seunghee; Choi, Hojun; Kim, Jeongjin; Shaker, Mohammed R; Sun, Woong; Park, Ji-Ho; Kim, Daesoo; Heo, Won Do; Choi, Chulhee

    2016-07-22

    Nanoparticle-mediated delivery of functional macromolecules is a promising method for treating a variety of human diseases. Among nanoparticles, cell-derived exosomes have recently been highlighted as a new therapeutic strategy for the in vivo delivery of nucleotides and chemical drugs. Here we describe a new tool for intracellular delivery of target proteins, named 'exosomes for protein loading via optically reversible protein-protein interactions' (EXPLORs). By integrating a reversible protein-protein interaction module controlled by blue light with the endogenous process of exosome biogenesis, we are able to successfully load cargo proteins into newly generated exosomes. Treatment with protein-loaded EXPLORs is shown to significantly increase intracellular levels of cargo proteins and their function in recipient cells in vitro and in vivo. These results clearly indicate the potential of EXPLORs as a mechanism for the efficient intracellular transfer of protein-based therapeutics into recipient cells and tissues.

  10. CRF-based models of protein surfaces improve protein-protein interaction site predictions.

    Science.gov (United States)

    Dong, Zhijie; Wang, Keyu; Dang, Truong Khanh Linh; Gültas, Mehmet; Welter, Marlon; Wierschin, Torsten; Stanke, Mario; Waack, Stephan

    2014-08-13

    The identification of protein-protein interaction sites is a computationally challenging task and important for understanding the biology of protein complexes. There is a rich literature in this field. A broad class of approaches assign to each candidate residue a real-valued score that measures how likely it is that the residue belongs to the interface. The prediction is obtained by thresholding this score.Some probabilistic models classify the residues on the basis of the posterior probabilities. In this paper, we introduce pairwise conditional random fields (pCRFs) in which edges are not restricted to the backbone as in the case of linear-chain CRFs utilized by Li et al. (2007). In fact, any 3D-neighborhood relation can be modeled. On grounds of a generalized Viterbi inference algorithm and a piecewise training process for pCRFs, we demonstrate how to utilize pCRFs to enhance a given residue-wise score-based protein-protein interface predictor on the surface of the protein under study. The features of the pCRF are solely based on the interface predictions scores of the predictor the performance of which shall be improved. We performed three sets of experiments with synthetic scores assigned to the surface residues of proteins taken from the data set PlaneDimers compiled by Zellner et al. (2011), from the list published by Keskin et al. (2004) and from the very recent data set due to Cukuroglu et al. (2014). That way we demonstrated that our pCRF-based enhancer is effective given the interface residue score distribution and the non-interface residue score are unimodal.Moreover, the pCRF-based enhancer is also successfully applicable, if the distributions are only unimodal over a certain sub-domain. The improvement is then restricted to that domain. Thus we were able to improve the prediction of the PresCont server devised by Zellner et al. (2011) on PlaneDimers. Our results strongly suggest that pCRFs form a methodological framework to improve residue-wise score

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

    Directory of Open Access Journals (Sweden)

    Wang Yong

    2011-10-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-05-15

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

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

    Directory of Open Access Journals (Sweden)

    David Talavera

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

  14. Extracting protein-protein interaction based on discriminative training of the Hidden Vctor State model

    OpenAIRE

    Zhou, Deyu; He, Yulan

    2008-01-01

    The knowledge about gene clusters and protein interactions is important for biological researchers to unveil the mechanism of life. However, large quantity of the knowledge often hides in the literature, such as journal articles, reports, books and so on. Many approaches focusing on extracting information from unstructured text, such as pattern matching, shallow and deep parsing, have been proposed especially for extracting protein-protein interactions (Zhou and He, 2008). A semantic parser b...

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

    Science.gov (United States)

    2016-07-01

    protein- protein interactions inhibiting innate immunity Distribution Statement A. Approved for public release; distribution is unlimited. July 2016...protein interactions inhibiting innate immunity Sb. GRANT NUMBER HDTRA1-13-1-0017 Sc. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Sd. PROJECT NUMBER Timothy...first-line innate immunity response against viral infection. The inhibition or avoidance of this initial innate immune response is a commonly occurring

  16. New partner proteins containing novel internal recognition motif for human glutaminase interacting protein (hGIP).

    Science.gov (United States)

    Zencir, Sevil; Banerjee, Monimoy; Dobson, Melanie J; Ayaydin, Ferhan; Fodor, Elfrieda Ayaydin; Topcu, Zeki; Mohanty, Smita

    2013-03-01

    Regulation of gene expression in cells is mediated by protein-protein, DNA-protein and receptor-ligand interactions. PDZ (PSD-95/Discs-large/ZO-1) domains are protein-protein interaction modules. PDZ-containing proteins function in the organization of multi-protein complexes controlling spatial and temporal fidelity of intracellular signaling pathways. In general, PDZ proteins possess multiple domains facilitating distinct interactions. The human glutaminase interacting protein (hGIP) is an unusual PDZ protein comprising entirely of a single PDZ domain and plays pivotal roles in many cellular processes through its interaction with the C-terminus of partner proteins. Here, we report the identification by yeast two-hybrid screening of two new hGIP-interacting partners, DTX1 and STAU1. Both proteins lack the typical C-terminal PDZ recognition motif but contain a novel internal hGIP recognition motif recently identified in a phage display library screen. Fluorescence resonance energy transfer and confocal microscopy analysis confirmed the in vivo association of hGIP with DTX1 and STAU1 in mammalian cells validating the previous discovery of S/T-X-V/L-D as a consensus internal motif for hGIP recognition. Similar to hGIP, DTX1 and STAU1 have been implicated in neuronal function. Identification of these new interacting partners furthers our understanding of GIP-regulated signaling cascades and these interactions may represent potential new drug targets in humans. Copyright © 2013 Elsevier Inc. All rights reserved.

  17. 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.

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

    Science.gov (United States)

    Nakamura, Robert L; Landt, Stephen G; Mai, Emily; Nejim, Jemiel; Chen, Lily; Frankel, Alan D

    2012-01-01

    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.

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

    Directory of Open Access Journals (Sweden)

    Sony Malhotra

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

  20. SPPS: a sequence-based method for predicting probability of protein-protein interaction partners.

    Directory of Open Access Journals (Sweden)

    Xinyi Liu

    Full Text Available The molecular network sustained by different types of interactions among proteins is widely manifested as the fundamental driving force of cellular operations. Many biological functions are determined by the crosstalk between proteins rather than by the characteristics of their individual components. Thus, the searches for protein partners in global networks are imperative when attempting to address the principles of biology.We have developed a web-based tool "Sequence-based Protein Partners Search" (SPPS to explore interacting partners of proteins, by searching over a large repertoire of proteins across many species. SPPS provides a database containing more than 60,000 protein sequences with annotations and a protein-partner search engine in two modes (Single Query and Multiple Query. Two interacting proteins of human FBXO6 protein have been found using the service in the study. In addition, users can refine potential protein partner hits by using annotations and possible interactive network in the SPPS web server.SPPS provides a new type of tool to facilitate the identification of direct or indirect protein partners which may guide scientists on the investigation of new signaling pathways. The SPPS server is available to the public at http://mdl.shsmu.edu.cn/SPPS/.

  1. The Rift Valley Fever virus protein NSm and putative cellular protein interactions

    Directory of Open Access Journals (Sweden)

    Engdahl Cecilia

    2012-07-01

    Full Text Available Abstract Rift Valley Fever is an infectious viral disease and an emerging problem in many countries of Africa and on the Arabian Peninsula. The causative virus is predominantly transmitted by mosquitoes and high mortality and abortion rates characterize outbreaks in animals while symptoms ranging from mild to life-threatening encephalitis and hemorrhagic fever are noticed among infected humans. For a better prevention and treatment of the infection, an increased knowledge of the infectious process of the virus is required. The focus of this work was to identify protein-protein interactions between the non-structural protein (NSm, encoded by the M-segment of the virus, and host cell proteins. This study was initiated by screening approximately 26 million cDNA clones of a mouse embryonic cDNA library for interactions with the NSm protein using a yeast two-hybrid system. We have identified nine murine proteins that interact with NSm protein of Rift Valley Fever virus, and the putative protein-protein interactions were confirmed by growth selection procedures and β-gal activity measurements. Our results suggest that the cleavage and polyadenylation specificity factor subunit 2 (Cpsf2, the peptidyl-prolyl cis-trans isomerase (cyclophilin-like 2 protein (Ppil2, and the synaptosome-associated protein of 25 kDa (SNAP-25 are the most promising targets for the NSm protein of the virus during an infection.

  2. Protein complex detection with semi-supervised learning in protein interaction networks

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    Zhang Aidong

    2011-10-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs play fundamental roles in nearly all biological processes. The systematic analysis of PPI networks can enable a great understanding of cellular organization, processes and function. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. However, protein complexes are likely to overlap and the interaction data are very noisy. It is a great challenge to effectively analyze the massive data for biologically meaningful protein complex detection. Results Many people try to solve the problem by using the traditional unsupervised graph clustering methods. Here, we stand from a different point of view, redefining the properties and features for protein complexes and designing a “semi-supervised” method to analyze the problem. In this paper, we utilize the neural network with the “semi-supervised” mechanism to detect the protein complexes. By retraining the neural network model recursively, we could find the optimized parameters for the model, in such a way we can successfully detect the protein complexes. The comparison results show that our algorithm could identify protein complexes that are missed by other methods. We also have shown that our method achieve better precision and recall rates for the identified protein complexes than other existing methods. In addition, the framework we proposed is easy to be extended in the future. Conclusions Using a weighted network to represent the protein interaction network is more appropriate than using a traditional unweighted network. In addition, integrating biological features and topological features to represent protein complexes is more meaningful than using dense subgraphs. Last, the “semi-supervised” learning model is a promising model to detect protein complexes with more biological and topological

  3. Yeast Interacting Proteins Database: YNL086W, YGL172W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available fluorescent protein (GFP)-fusion protein localizes to endosomes Rows with this bait as bait (3) Rows with this...fluorescent protein (GFP)-fusion protein localizes to endosomes Rows with this bait as bait Rows with this bait

  4. Yeast Interacting Proteins Database: YEL005C, YNL086W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available fluorescent protein (GFP)-fusion protein localizes to endosomes Rows with this prey as prey (2) Rows with this...fluorescent protein (GFP)-fusion protein localizes to endosomes Rows with this prey as prey Rows with this prey

  5. Yeast Interacting Proteins Database: YGR119C, YKL061W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available fluorescent protein (GFP)-fusion protein localizes to the endosome Rows with this prey as prey (2) Rows with this...fluorescent protein (GFP)-fusion protein localizes to the endosome Rows with this prey as prey Rows with this prey

  6. 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.

  7. Sequence-based prediction of protein-protein interaction sites with L1-logreg classifier.

    Science.gov (United States)

    Dhole, Kaustubh; Singh, Gurdeep; Pai, Priyadarshini P; Mondal, Sukanta

    2014-05-07

    Protein-protein interactions are of central importance for virtually every process in a living cell. Information about the interaction sites in proteins improves our understanding of disease mechanisms and can provide the basis for new therapeutic approaches. Since a multitude of unique residue-residue contacts facilitate the interactions, protein-protein interaction sites prediction has become one of the most important and challenging problems of computational biology. Although much progress in this field has been reported, this problem is yet to be satisfactorily solved. Here, a novel method (LORIS: L1-regularized LOgistic Regression based protein-protein Interaction Sites predictor) is proposed, that identifies interaction residues, using sequence features and is implemented via the L1-logreg classifier. Results show that LORIS is not only quite effective, but also, performs better than existing state-of-the art methods. LORIS, available as standalone package, can be useful for facilitating drug-design and targeted mutation related studies, which require a deeper knowledge of protein interactions sites. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Protein-Protein Interactions Inferred from Domain-Domain Interactions in Genogroup II Genotype 4 Norovirus Sequences

    Directory of Open Access Journals (Sweden)

    Chuan-Ching Huang

    2013-01-01

    Full Text Available Severe gastroenteritis and foodborne illness caused by Noroviruses (NoVs during the winter are a worldwide phenomenon. Vulnerable populations including young children and elderly and immunocompromised people often require hospitalization and may die. However, no efficient vaccine for NoVs exists because of their variable genome sequences. This study investigates the infection processes in protein-protein interactions between hosts and NoVs. Protein-protein interactions were collected from related Pfam NoV domains. The related Pfam domains were accumulated incrementally from the protein domain interaction database. To examine the influence of domain intimacy, the 7 NoV domains were grouped by depth. The number of domain-domain interactions increased exponentially as the depth increased. Many protein-protein interactions were relevant; therefore, cloud techniques were used to analyze data because of their computational capacity. The infection relationship between hosts and NoVs should be used in clinical applications and drug design.

  9. The role of protein interaction networks in systems biomedicine

    Directory of Open Access Journals (Sweden)

    Tuba Sevimoglu

    2014-08-01

    Full Text Available The challenging task of studying and modeling complex dynamics of biological systems in order to describe various human diseases has gathered great interest in recent years. Major biological processes are mediated through protein interactions, hence there is a need to understand the chaotic network that forms these processes in pursuance of understanding human diseases. The applications of protein interaction networks to disease datasets allow the identification of genes and proteins associated with diseases, the study of network properties, identification of subnetworks, and network-based disease gene classification. Although various protein interaction network analysis strategies have been employed, grand challenges are still existing. Global understanding of protein interaction networks via integration of high-throughput functional genomics data from different levels will allow researchers to examine the disease pathways and identify strategies to control them. As a result, it seems likely that more personalized, more accurate and more rapid disease gene diagnostic techniques will be devised in the future, as well as novel strategies that are more personalized. This mini-review summarizes the current practice of protein interaction networks in medical research as well as challenges to be overcome.

  10. 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; computational...tein of unknown function; computational analysis of large-scale protein-protein i... HUA2 Prey description Cytoplasmic protein of unknown function; computational ana

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

    Lifescience Database Archive (English)

    Full Text Available ycling of Golgi proteins and formation of lumenal membranes Rows with this bait as bait (1) Rows with this b...required for recycling Golgi proteins, forming lumenal membranes and sorting ubiquitinated proteins destined...on, as well as for recycling of Golgi proteins and formation of lumenal membranes...ith Hse1p; required for recycling Golgi proteins, forming lumenal membranes and sorting ubiquitinated protei

  12. Ribosome Mediated Quinary Interactions Modulate In-Cell Protein Activities.

    Science.gov (United States)

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

    2017-08-15

    Ribosomes are present inside bacterial cells at micromolar concentrations and occupy up to 20% of the cell volume. Under these conditions, even weak quinary interactions between ribosomes and cytosolic proteins can affect protein activity. By using in-cell and in vitro NMR spectroscopy, and biophysical techniques, we show that the enzymes, adenylate kinase and dihydrofolate reductase, and the respective coenzymes, ATP and NADPH, bind to ribosomes with micromolar affinity, and that this interaction suppresses the enzymatic activities of both enzymes. Conversely, thymidylate synthase, which works together with dihydrofolate reductase in the thymidylate synthetic pathway, is activated by ribosomes. We also show that ribosomes impede diffusion of green fluorescent protein in vitro and contribute to the decrease in diffusion in vivo. These results strongly suggest that ribosome-mediated quinary interactions contribute to the differences between in vitro and in vivo protein activities and that ribosomes play a previously under-appreciated nontranslational role in regulating cellular biochemistry.

  13. Self-interaction chromatography of proteins on a microfluidic monolith

    Science.gov (United States)

    Martin, Cristina; Lenhoff, Abraham M.

    2010-01-01

    A novel miniaturized system has been developed for measuring protein-protein interactions in solution with high efficiency and speed, and minimal use of protein. A chromatographic monolith synthesized in a capillary is used in the method to make interaction measurements by self-interaction chromatography (SIC) in a manner that, compared to column methods, is more efficient as well as more readily practicable even if only small amounts of protein are available. The microfluidic monolith requires much less protein for both column synthesis and the chromatographic measurements than a conventional SIC system, and in addition offers improved mass transfer and hence higher chromatographic efficiency than for previous SIC miniaturization systems. Protein self-interactions for catalase as a model protein, quantified by measurement of second virial coefficients, B22, were determined by SIC and follow trends that are consistent with previously reported values. Different column derivatization conditions were studied in order to optimize the chromatographic behavior of the microfluidic system for SIC measurements. Chromatographic sensitivity can be further increased by using different column synthesis conditions. PMID:21258647

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

    Directory of Open Access Journals (Sweden)

    Peiying Ruan

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

  15. Interactions Affected by Arginine Methylation in the Yeast Protein–Protein Interaction Network*

    Science.gov (United States)

    Erce, Melissa A.; Abeygunawardena, Dhanushi; Low, Jason K. K.; Hart-Smith, Gene; Wilkins, Marc R.

    2013-01-01

    Protein–protein interactions can be modulated by the methylation of arginine residues. As a means of testing this, we recently described a conditional two-hybrid system, based on the bacterial adenylate cyclase (BACTH) system. Here, we have used this conditional two-hybrid system to explore the effect of arginine methylation in modulating protein–protein interactions in a subset of the Saccharomyces cerevisiae arginine methylproteome network. Interactions between the yeast hub protein Npl3 and yeast proteins Air2, Ded1, Gbp2, Snp1, and Yra1 were first validated in the absence of methylation. The major yeast arginine methyltransferase Hmt1 was subsequently included in the conditional two-hybrid assay, initially to determine the degree of methylation that occurs. Proteins Snp1 and Yra1 were confirmed as Hmt1 substrates, with five and two novel arginine methylation sites mapped by ETD LC-MS/MS on these proteins, respectively. Proteins Ded1 and Gbp2, previously predicted but not confirmed as substrates of Hmt1, were also found to be methylated with five and seven sites mapped respectively. Air2 was found to be a novel substrate of Hmt1 with two sites mapped. Finally, we investigated the interactions of Npl3 with the five interaction partners in the presence of active Hmt1 and in the presence of Hmt1 with a G68R inactivation mutation. We found that the interaction between Npl3 and Air2, and Npl3 and Ded1, were significantly increased in the presence of active Hmt1; the interaction of Npl3 and Snp1 showed a similar degree of increase in interaction but this was not statistically significant. The interactions of Npl3 and Gbp2, along with Npl3 and Yra1, were not significantly increased or decreased by methylation. We conclude that methylarginine may be a widespread means by which the interactions of proteins are modulated. PMID:23918811

  16. Contextual specificity in peptide-mediated protein interactions.

    Directory of Open Access Journals (Sweden)

    Amelie Stein

    Full Text Available Most biological processes are regulated through complex networks of transient protein interactions where a globular domain in one protein recognizes a linear peptide from another, creating a relatively small contact interface. Although sufficient to ensure binding, these linear motifs alone are usually too short to achieve the high specificity observed, and additional contacts are often encoded in the residues surrounding the motif (i.e. the context. Here, we systematically identified all instances of peptide-mediated protein interactions of known three-dimensional structure and used them to investigate the individual contribution of motif and context to the global binding energy. We found that, on average, the context is responsible for roughly 20% of the binding and plays a crucial role in determining interaction specificity, by either improving the affinity with the native partner or impeding non-native interactions. We also studied and quantified the topological and energetic variability of interaction interfaces, finding a much higher heterogeneity in the context residues than in the consensus binding motifs. Our analysis partially reveals the molecular mechanisms responsible for the dynamic nature of peptide-mediated interactions, and suggests a global evolutionary mechanism to maximise the binding specificity. Finally, we investigated the viability of non-native interactions and highlight cases of potential cross-reaction that might compensate for individual protein failure and establish backup circuits to increase the robustness of cell networks.

  17. Evidence for simultaneous protein interactions between human Rad51 paralogs.

    Science.gov (United States)

    Schild, D; Lio, Y C; Collins, D W; Tsomondo, T; Chen, D J

    2000-06-02

    In yeast, the Rad51-related proteins include Rad55 and Rad57, which form a heterodimer that interacts with Rad51. Five human Rad51 paralogs have been identified (XRCC2, XRCC3, Rad51B/Rad51L1, Rad51C/Rad51L2, and Rad51D/Rad51L3), and each interacts with one or more of the others. Previously we reported that HsRad51 interacts with XRCC3, and Rad51C interacts with XRCC3, Rad51B, and HsRad51. Here we report that in the yeast two-hybrid system, Rad51D interacts with XRCC2 and Rad51C. No other interactions, including self-interactions, were found, indicating that the observed interactions are specific. The yeast Rad51 interacts with human Rad51 and XRCC3, suggesting Rad51 conservation since the human yeast divergence. Data from yeast three-hybrid experiments indicate that a number of the pairs of interactions between human Rad51 paralogs can occur simultaneously. For example, Rad51B expression enhances the binding of Rad51C to XRCC3 and to HsRad51D, and Rad51C expression allows the indirect interaction of Rad51B with Rad51D. Experiments using 6xHis-tagged proteins in the baculovirus system confirm several of our yeast results, including Rad51B interaction with Rad51D only when Rad51C is simultaneously expressed and Rad51C interaction with XRCC2 only when Rad51D is present. These results suggest that these proteins may participate in one complex or multiple smaller ones.

  18. 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

  19. 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. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

    Directory of Open Access Journals (Sweden)

    Albrecht von Brunn

    2007-05-01

    Full Text Available The severe acute respiratory syndrome coronavirus (SARS-CoV genome is predicted to encode 14 functional open reading frames, leading to the expression of up to 30 structural and non-structural protein products. The functions of a large number of viral ORFs are poorly understood or unknown. In order to gain more insight into functions and modes of action and interaction of the different proteins, we cloned the viral ORFeome and performed a genome-wide analysis for intraviral protein interactions and for intracellular localization. 900 pairwise interactions were tested by yeast-two-hybrid matrix analysis, and more than 65 positive non-redundant interactions, including six self interactions, were identified. About 38% of interactions were subsequently confirmed by CoIP in mammalian cells. Nsp2, nsp8 and ORF9b showed a wide range of interactions with other viral proteins. Nsp8 interacts with replicase proteins nsp2, nsp5, nsp6, nsp7, nsp8, nsp9, nsp12, nsp13 and nsp14, indicating a crucial role as a major player within the replication complex machinery. It was shown by others that nsp8 is essential for viral replication in vitro, whereas nsp2 is not. We show that also accessory protein ORF9b does not play a pivotal role for viral replication, as it can be deleted from the virus displaying normal plaque sizes and growth characteristics in Vero cells. However, it can be expected to be important for the virus-host interplay and for pathogenicity, due to its large number of interactions, by enhancing the global stability of the SARS proteome network, or play some unrealized role in regulating protein-protein interactions. The interactions identified provide valuable material for future studies.

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

    Science.gov (United States)

    Franzosa, Eric A; Xia, Yu

    2011-06-28

    General properties of the antagonistic biomolecular interactions between viruses and their hosts (exogenous interactions) remain poorly understood, and may differ significantly from known principles governing the cooperative interactions within the host (endogenous interactions). Systems biology approaches have been applied to study the combined interaction networks of virus and human proteins, but such efforts have so far revealed only low-resolution patterns of host-virus interaction. Here, we layer curated and predicted 3D structural models of human-virus and human-human protein complexes on top of traditional interaction networks to reconstruct the human-virus structural interaction network. This approach reveals atomic resolution, mechanistic patterns of host-virus interaction, and facilitates systematic comparison with the host's endogenous interactions. We find that exogenous interfaces tend to overlap with and mimic endogenous interfaces, thereby competing with endogenous binding partners. The endogenous interfaces mimicked by viral proteins tend to participate in multiple endogenous interactions which are transient and regulatory in nature. While interface overlap in the endogenous network results largely from gene duplication followed by divergent evolution, viral proteins frequently achieve interface mimicry without any sequence or structural similarity to an endogenous binding partner. Finally, while endogenous interfaces tend to evolve more slowly than the rest of the protein surface, exogenous interfaces--including many sites of endogenous-exogenous overlap--tend to evolve faster, consistent with an evolutionary "arms race" between host and pathogen. These significant biophysical, functional, and evolutionary differences between host-pathogen and within-host protein-protein interactions highlight the distinct consequences of antagonism versus cooperation in biological networks.

  2. Predicting protein functions from redundancies in large-scale protein interaction networks

    Science.gov (United States)

    Samanta, Manoj Pratim; Liang, Shoudan

    2003-01-01

    Interpreting data from large-scale protein interaction experiments has been a challenging task because of the widespread presence of random false positives. Here, we present a network-based statistical algorithm that overcomes this difficulty and allows us to derive functions of unannotated proteins from large-scale interaction data. Our algorithm uses the insight that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals >2,800 reliable functional associations, 29% of which involve at least one unannotated protein. By further analyzing these associations, we derive tentative functions for 81 unannotated proteins with high certainty. Our method is not overly sensitive to the false positives present in the data. Even after adding 50% randomly generated interactions to the measured data set, we are able to recover almost all (approximately 89%) of the original associations.

  3. Application of amide proton exchange mass spectrometry for the study of protein-protein interactions.

    Science.gov (United States)

    Mandell, Jeffrey G; Baerga-Ortiz, Abel; Croy, Carrie H; Falick, Arnold M; Komives, Elizabeth A

    2005-06-01

    This protocol describes amide proton exchange experiments that probe for changes in solvent accessibility at protein-protein interfaces. The simplest version of the protocol, termed the "on-exchange" experiment, detects protein-protein interfaces by taking advantage of the fact that solvent deuterium oxide (D2O) molecules are excluded from the surface of a protein to which another protein is bound. A more complete version of the experiment can also be performed in which the rate of surface deuteration is initially measured separately for each of the proteins involved in the interaction, after which the deuterated proteins are allowed to complex and the rate of "off-exchange" (i.e., replacement of surface deuterons by protons from solvent H2O molecules) at the resulting protein-protein interface is measured. This version of the experiment yields additional kinetic information that can help to define the solvent-inaccessible "core" of the interface.

  4. Gγ recruitment system incorporating a novel signal amplification circuit to screen transient protein-protein interactions.

    Science.gov (United States)

    Fukuda, Nobuo; Ishii, Jun; Kondo, Akihiko

    2011-09-01

    Weak and transient protein-protein interactions are associated with biological processes, but many are still undefined because of the difficulties in their identification. Here, we describe a redesigned method to screen transient protein-protein interactions by using a novel signal amplification circuit, which is incorporated into yeast to artificially magnify the signal responding to the interactions. This refined method is based on the previously established Gγ recruitment system, which utilizes yeast G-protein signaling and mating growth selection to screen interacting protein pairs. In the current study, to test the capability of our method, we chose mutants of the Z-domain derived from Staphylococcus aureus protein A as candidate proteins, and the Fc region of human IgG as the counterpart. By introduction of an artificial signal amplifier into the previous Gγ recruitment system, the signal transduction responding to transient interactions between Z-domain mutants and the Fc region with significantly low affinity (8.0 × 10(3) M(-1)) was successfully amplified in recombinant haploid yeast cells. As a result of zygosis with the opposite mating type of wild-type haploid cells, diploid colonies were vigorously and selectively generated on the screening plates, whereas our previous system rarely produced positive colonies. This new approach will be useful for exploring the numerous transient interactions that remain undefined because of the lack of powerful screening tools for their identification. © 2011 The Authors Journal compilation © 2011 FEBS.

  5. Amyloid precursor protein interaction network in human testis: sentinel proteins for male reproduction.

    Science.gov (United States)

    Silva, Joana Vieira; Yoon, Sooyeon; Domingues, Sara; Guimarães, Sofia; Goltsev, Alexander V; da Cruz E Silva, Edgar Figueiredo; Mendes, José Fernando F; da Cruz E Silva, Odete Abreu Beirão; Fardilha, Margarida

    2015-01-16

    Amyloid precursor protein (APP) is widely recognized for playing a central role in Alzheimer's disease pathogenesis. Although APP is expressed in several tissues outside the human central nervous system, the functions of APP and its family members in other tissues are still poorly understood. APP is involved in several biological functions which might be potentially important for male fertility, such as cell adhesion, cell motility, signaling, and apoptosis. Furthermore, APP superfamily members are known to be associated with fertility. Knowledge on the protein networks of APP in human testis and spermatozoa will shed light on the function of APP in the male reproductive system. We performed a Yeast Two-Hybrid screen and a database search to study the interaction network of APP in human testis and sperm. To gain insights into the role of APP superfamily members in fertility, the study was extended to APP-like protein 2 (APLP2). We analyzed several topological properties of the APP interaction network and the biological and physiological properties of the proteins in the APP interaction network were also specified by gene ontologyand pathways analyses. We classified significant features related to the human male reproduction for the APP interacting proteins and identified modules of proteins with similar functional roles which may show cooperative behavior for male fertility. The present work provides the first report on the APP interactome in human testis. Our approach allowed the identification of novel interactions and recognition of key APP interacting proteins for male reproduction, particularly in sperm-oocyte interaction.

  6. 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.

  7. 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. 

  8. 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

  9. IDDI: integrated domain-domain interaction and protein interaction analysis system.

    Science.gov (United States)

    Kim, Yul; Min, Bumki; Yi, Gwan-Su

    2012-06-21

    Deciphering protein-protein interaction (PPI) in domain level enriches valuable information about binding mechanism and functional role of interacting proteins. The 3D structures of complex proteins are reliable source of domain-domain interaction (DDI) but the number of proven structures is very limited. Several resources for the computationally predicted DDI have been generated but they are scattered in various places and their prediction show erratic performances. A well-organized PPI and DDI analysis system integrating these data with fair scoring system is necessary. We integrated three structure-based DDI datasets and twenty computationally predicted DDI datasets and constructed an interaction analysis system, named IDDI, which enables to browse protein and domain interactions with their relationships. To integrate heterogeneous DDI information, a novel scoring scheme is introduced to determine the reliability of DDI by considering the prediction scores of each DDI and the confidence levels of each prediction method in the datasets, and independencies between predicted datasets. In addition, we connected this DDI information to the comprehensive PPI information and developed a unified interface for the interaction analysis exploring interaction networks at both protein and domain level. IDDI provides 204,705 DDIs among total 7,351 Pfam domains in the current version. The result presents that total number of DDIs is increased eight times more than that of previous studies. Due to the increment of data, 50.4% of PPIs could be correlated with DDIs which is more than twice of previous resources. Newly designed scoring scheme outperformed the previous system in its accuracy too. User interface of IDDI system provides interactive investigation of proteins and domains in interactions with interconnected way. A specific example is presented to show the efficiency of the systems to acquire the comprehensive information of target protein with PPI and DDI relationships

  10. Yeast Interacting Proteins Database: YPR083W, YMR294W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YPR083W MDM36 Protein required for normal mitochondrial morphology and inheritance ...description Protein required for normal mitochondrial morphology and inheritance Rows with this bait as bait

  11. 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

  12. Yeast Interacting Proteins Database: YLR373C, YGL190C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ase 2A, which has multiple roles in mitosis and protein biosynthesis; involved in regulation of mitotic exit...phosphatase 2A, which has multiple roles in mitosis and protein biosynthesis; involved in regulation of mito

  13. 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

  14. 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

  15. Yeast Interacting Proteins Database: YMR146C, YPL105C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available ts; authentic, non-tagged protein is detected in highly purified mitochondria in high-throughpu...tagged protein is detected in highly purified mitochondria in high-throughput studies Rows with this prey as

  16. Yeast Interacting Proteins Database: YGL181W, YHR177W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available y (0) YHR177W - Putative protein of unknown function; overexpression causes a cel...ative protein of unknown function; overexpression causes a cell cycle delay or arrest Rows with this prey as

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

    Lifescience Database Archive (English)

    Full Text Available YPL070W MUK1 Cytoplasmic protein of unknown function containing a Vps9 domain; computational...me MUK1 Bait description Cytoplasmic protein of unknown function containing a Vps9 domain; computational

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

    Lifescience Database Archive (English)

    Full Text Available YPL070W MUK1 Cytoplasmic protein of unknown function containing a Vps9 domain; computational...1 Bait description Cytoplasmic protein of unknown function containing a Vps9 domain; computational

  19. 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 computational

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

    Lifescience Database Archive (English)

    Full Text Available YPL070W MUK1 Cytoplasmic protein of unknown function containing a Vps9 domain; computational... name MUK1 Bait description Cytoplasmic protein of unknown function containing a Vps9 domain; computationa

  1. Yeast Interacting Proteins Database: YGL115W, YGL208W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available nine protein kinase complex involved in the response to glucose starvation; null mutants exhibit accelerated...serine/threonine protein kinase complex involved in the response to glucose starvation; null mutants exhibit accelerated

  2. 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; computational

  3. 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; computationa...ey description Cytoplasmic protein of unknown function containing a Vps9 domain; computational analysis of l

  4. 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

  5. Yeast Interacting Proteins Database: YNL092W, YML037C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available Putative protein of unknown function with some characteristics of a transcriptional activator; may be a target...Putative protein of unknown function with some characteristics of a transcriptional activator; may be a target

  6. 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

  7. 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

  8. 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

  9. Yeast Interacting Proteins Database: YPL114W, YMR133W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available as prey (0) YMR133W REC114 Protein involved in early stages of meiotic recombination; possibly involved...name REC114 Prey description Protein involved in early stages of meiotic recombination; possibly involved

  10. Yeast Interacting Proteins Database: YNL334C, YNL333W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YNL334C SNO2 Protein of unknown function, nearly identical to Sno3p; expression is induced before the...Bait description Protein of unknown function, nearly identical to Sno3p; expression is induced before

  11. Yeast Interacting Proteins Database: YDR394W, YGR232W [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 16 IST hit in the opposite bait/prey orientation 18 ...

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

    Lifescience Database Archive (English)

    Full Text Available YNL258C DSL1 Peripheral membrane protein required for Golgi-to-ER retrograde traffi...t description Peripheral membrane protein required for Golgi-to-ER retrograde traffic; component of the ER t

  13. 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

  14. Yeast Interacting Proteins Database: YCL046W, YGL115W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YCL046W - Dubious open reading frame unlikely to encode a protein, based on availab...ading frame unlikely to encode a protein, based on available experimental and comparative sequence data; par

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

    Lifescience Database Archive (English)

    Full Text Available 9C ADY3 Protein required for spore wall formation, thought to mediate assembly of...DY3 Prey description Protein required for spore wall formation, thought to mediate assembly of a Don1p-conta

  16. 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...239C Bait gene name ADY3 Bait description Protein required for spore wall formation, thought to mediate asse

  17. 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...cription Protein required for spore wall formation, thought to mediate assembly of a Don1p-containing struct

  18. 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...39C Bait ORF YDL239C Bait gene name ADY3 Bait description Protein required for spore wall formation, thought

  19. 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...ait ORF YDL239C Bait gene name ADY3 Bait description Protein required for spore wall formation, thought to m

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

    Lifescience Database Archive (English)

    Full Text Available YDL239C ADY3 Protein required for spore wall formation, thought to mediate assembly...iption Protein required for spore wall formation, thought to mediate assembly of a Don1p-containing structur

  1. 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...ption Protein required for spore wall formation, thought to mediate assembly of a Don1p-containing structure

  2. 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...it gene name ADY3 Bait description Protein required for spore wall formation, thought to mediate assembly of

  3. Yeast Interacting Proteins Database: YDL239C, YHR184W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YDL239C ADY3 Protein required for spore wall formation, thought to mediate assembly...C Bait ORF YDL239C Bait gene name ADY3 Bait description Protein required for spore wall formation, thought

  4. 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... Bait ORF YDL239C Bait gene name ADY3 Bait description Protein required for spore wall formation, thought

  5. Yeast Interacting Proteins Database: YPL059W, YIL105C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available oxidoreductase; mitochondrial matrix protein involved in the synthesis/assembly of iron-sulfur centers; mono...oreductase; mitochondrial matrix protein involved in the synthesis/assembly of iron-sulfur centers; monothio

  6. Yeast Interacting Proteins Database: YHR197W, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  7. Yeast Interacting Proteins Database: YGL127C, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  8. Yeast Interacting Proteins Database: YDR473C, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  9. Yeast Interacting Proteins Database: YNL182C, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  10. Yeast Interacting Proteins Database: YKL050C, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  11. Yeast Interacting Proteins Database: YPL159C, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  12. Yeast Interacting Proteins Database: YDR052C, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  13. Yeast Interacting Proteins Database: YGL237C, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  14. Yeast Interacting Proteins Database: YGR113W, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  15. Yeast Interacting Proteins Database: YNL092W, YOR329C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available SCD5 Protein required for normal cortical actin organization and endocytosis; multicopy suppressor of clathrin...description Protein required for normal cortical actin organization and endocytosis; multicopy suppressor of clathrin

  16. Yeast Interacting Proteins Database: YLR423C, YNL182C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  17. Yeast Interacting Proteins Database: YNR068C, YNR069C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available Protein of unknown function, ORF exhibits genomic organization compatible with a translational readthrough-dependent...Protein of unknown function, ORF exhibits genomic organization compatible with a translational readthrough-dependent

  18. Yeast Interacting Proteins Database: YJL061W, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  19. Yeast Interacting Proteins Database: YBR270C, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  20. Yeast Interacting Proteins Database: YCL063W, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  1. Yeast Interacting Proteins Database: YBR217W, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific...protein responsible for phagophore assembly site organization; regulatory subunit of an autophagy-specific

  2. Yeast Interacting Proteins Database: YGR218W, YGR178C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available involved in export of proteins, RNAs, and ribosomal subunits from the nucleus; exportin Rows with this... involved in export of proteins, RNAs, and ribosomal subunits from the nucleus; exportin Rows with this

  3. Yeast Interacting Proteins Database: YGR218W, YMR124W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available involved in export of proteins, RNAs, and ribosomal subunits from the nucleus; exportin Rows with this... involved in export of proteins, RNAs, and ribosomal subunits from the nucleus; exportin Rows with this

  4. Yeast Interacting Proteins Database: YGR218W, YDL065C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available involved in export of proteins, RNAs, and ribosomal subunits from the nucleus; exportin Rows with this... involved in export of proteins, RNAs, and ribosomal subunits from the nucleus; exportin Rows with this

  5. Yeast Interacting Proteins Database: YGR218W, YOL149W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available involved in export of proteins, RNAs, and ribosomal subunits from the nucleus; exportin Rows with this... involved in export of proteins, RNAs, and ribosomal subunits from the nucleus; exportin Rows with this

  6. 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

  7. Yeast Interacting Proteins Database: YJR091C, YKL002W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available g of integral membrane proteins into lumenal vesicles of multivesicular bodies, and for delivery of newly sy... integral membrane proteins into lumenal vesicles of multivesicular bodies, and for delivery of newly synthe

  8. 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

  9. Yeast Interacting Proteins Database: YOR117W, YJL184W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available GON7 Protein proposed to be involved in the modification of N-linked oligosaccharides, osmotic stress...description Protein proposed to be involved in the modification of N-linked oligosaccharides, osmotic stress

  10. Yeast Interacting Proteins Database: YPR106W, YBR038W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YPR106W ISR1 Predicted protein kinase, overexpression causes sensitivity to staurosporine, which is a potent...description Predicted protein kinase, overexpression causes sensitivity to staurosporine, which is a potent

  11. Yeast Interacting Proteins Database: YMR077C, YLR417W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available lumen; cytoplasmic protein recruited to endosomal membranes Rows with this bait as bait (3) Rows with this b...oplasmic protein recruited to endosomal membranes Rows with this bait as bait Row

  12. Yeast Interacting Proteins Database: YMR077C, YJR102C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available lumen; cytoplasmic protein recruited to endosomal membranes Rows with this bait as bait (3) Rows with this b...lar lumen; cytoplasmic protein recruited to endosomal membranes Rows with this bait as bait Rows with this b

  13. 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

  14. 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

  15. 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.

  16. Mirin: identifying microRNA regulatory modules in protein-protein interaction networks.

    Science.gov (United States)

    Yang, Ken-Chi; Hsu, Chia-Lang; Lin, Chen-Ching; Juan, Hsueh-Fen; Huang, Hsuan-Cheng

    2014-09-01

    Exploring microRNA (miRNA) regulations and protein-protein interactions could reveal the molecular mechanisms responsible for complex biological processes. Mirin is a web-based application suitable for identifying functional modules from protein-protein interaction networks regulated by aberrant miRNAs under user-defined biological conditions such as cancers. The analysis involves combining miRNA regulations, protein-protein interactions between target genes, as well as mRNA and miRNA expression profiles provided by users. Mirin has successfully uncovered oncomirs and their regulatory networks in various cancers, such as gastric and breast cancer. Mirin is freely available at http://mirin.ym.edu.tw/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  17. Going the distance for protein function prediction: a new distance metric for protein interaction networks.

    Science.gov (United States)

    Cao, Mengfei; Zhang, Hao; Park, Jisoo; Daniels, Noah M; Crovella, Mark E; Cowen, Lenore J; Hescott, Benjamin

    2013-01-01

    In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.

  18. Mapping Protein-Protein Interactions Using Affinity Purification and Mass Spectrometry.

    Science.gov (United States)

    Lee, Chin-Mei; Adamchek, Christopher; Feke, Ann; Nusinow, Dmitri A; Gendron, Joshua M

    2017-01-01

    The mapping of protein-protein interaction (PPI) networks and their dynamics are crucial steps to deciphering the function of a protein and its role in cellular pathways, making it critical to have comprehensive knowledge of a protein's interactome. Advances in affinity purification and mass spectrometry technology (AP-MS) have provided a powerful and unbiased method to capture higher-order protein complexes and decipher dynamic PPIs. However, the unbiased calling of nonspecific interactions and the ability to detect transient interactions remains challenging when using AP-MS, thereby hampering the detection of biologically meaningful complexes. Additionally, there are plant-specific challenges with AP-MS, such as a lack of protein-specific antibodies, which must be overcome to successfully identify PPIs. Here we discuss and describe a protocol designed to bypass the traditional challenges of AP-MS and provide a roadmap to identify bona fide PPIs in plants.

  19. 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.

  20. Yeast Interacting Proteins Database: YLR423C, YLR423C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YLR423C ATG17 Scaffold protein responsible for phagophore assembly site organizatio...se activity Rows with this bait as bait (9) Rows with this bait as prey (29) YLR423C ATG17 Scaffold protein responsible...LR423C Bait gene name ATG17 Bait description Scaffold protein responsible for pha...ene name ATG17 Prey description Scaffold protein responsible for phagophore assembly site organization; regu

  1. Yeast Interacting Proteins Database: YCL020W, YDR261W-A [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YA or TYA-TYB polyprotein; Gag is a nucleocapsid protein that is the structural constituent of virus-like particles... TYA or TYA-TYB polyprotein; Gag is a nucleocapsid protein that is the structural constituent of virus-like particles...lyprotein; Gag is a nucleocapsid protein that is the structural constituent of virus-like particles (VLPs); ...; Gag is a nucleocapsid protein that is the structural constituent of virus-like particles (VLPs); similar t

  2. Yeast Interacting Proteins Database: YCL019W, YDR261W-B [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available a nucleocapsid-like protein (Gag), reverse transcriptase (RT), protease (PR), and integrase (IN); similar...a nucleocapsid-like protein (Gag), reverse transcriptase (RT), protease (PR), and integrase (IN); similar...a nucleocapsid-like protein (Gag), reverse transcriptase (RT), protease (PR), and integrase (IN); similar...a nucleocapsid-like protein (Gag), reverse transcriptase (RT), protease (PR), and integrase (IN); similar

  3. 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.

  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. Modeling of metal interaction geometries for protein-ligand docking.

    Science.gov (United States)

    Seebeck, Birte; Reulecke, Ingo; Kämper, Andreas; Rarey, Matthias

    2008-05-15

    The accurate modeling of metal coordination geometries plays an important role for structure-based drug design applied to metalloenzymes. For the development of a new metal interaction model, we perform a statistical analysis of metal interaction geometries that are relevant to protein-ligand complexes. A total of 43,061 metal sites of the Protein Data Bank (PDB), containing amongst others magnesium, calcium, zinc, iron, manganese, copper, cadmium, cobalt, and nickel, were evaluated according to their metal coordination geometry. Based on statistical analysis, we derived a model for the automatic calculation and definition of metal interaction geometries for the purpose of molecular docking analyses. It includes the identification of the metal-coordinating ligands, the calculation of the coordination geometry and the superposition of ideal polyhedra to identify the optimal positions for free coordination sites. The new interaction model was integrated in the docking software FlexX and evaluated on a data set of 103 metalloprotein-ligand complexes, which were extracted from the PDB. In a first step, the quality of the automatic calculation of the metal coordination geometry was analyzed. In 74% of the cases, the correct prediction of the coordination geometry could be determined on the basis of the protein structure alone. Secondly, the new metal interaction model was tested in terms of predicting protein-ligand complexes. In the majority of test cases, the new interaction model resulted in an improved docking accuracy of the top ranking placements. 2007 Wiley-Liss, Inc.

  7. Discovering protein interactions and characterizing protein function using HaloTag technology.

    Science.gov (United States)

    Daniels, Danette L; Méndez, Jacqui; Benink, Hélène; Niles, Andrew; Murphy, Nancy; Ford, Michael; Jones, Richard; Amunugama, Ravi; Allen, David; Urh, Marjeta

    2014-07-12

    Research in proteomics has exploded in recent years with advances in mass spectrometry capabilities that have led to the characterization of numerous proteomes, including those from viruses, bacteria, and yeast. In comparison, analysis of the human proteome lags behind, partially due to the sheer number of proteins which must be studied, but also the complexity of networks and interactions these present. To specifically address the challenges of understanding the human proteome, we have developed HaloTag technology for protein isolation, particularly strong for isolation of multiprotein complexes and allowing more efficient capture of weak or transient interactions and/or proteins in low abundance. HaloTag is a genetically encoded protein fusion tag, designed for covalent, specific, and rapid immobilization or labelling of proteins with various ligands. Leveraging these properties, numerous applications for mammalian cells were developed to characterize protein function and here we present methodologies including: protein pull-downs used for discovery of novel interactions or functional assays, and cellular localization. We find significant advantages in the speed, specificity, and covalent capture of fusion proteins to surfaces for proteomic analysis as compared to other traditional non-covalent approaches. We demonstrate these and the broad utility of the technology using two important epigenetic proteins as examples, the human bromodomain protein BRD4, and histone deacetylase HDAC1. These examples demonstrate the power of this technology in enabling the discovery of novel interactions and characterizing cellular localization in eukaryotes, which will together further understanding of human functional proteomics.

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

    DEFF Research Database (Denmark)

    He, Li; Steinocher, Helena; Shelar, Ashish

    2017-01-01

    Transmembrane domains (TMDs) engage in protein-protein interactions that regulate many cellular processes, but the rules governing the specificity of these interactions are poorly understood. To discover these principles, we analyzed 26-residue model transmembrane proteins consisting exclusively...... productively with the TMD of the human EPOR, the mouse EPOR, or both receptors. Association of the traptamers with the EPOR induced EPOR oligomerization in an orientation that stimulated receptor activity. These results highlight the high intrinsic specificity of TMD interactions, demonstrate that a single...

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  10. 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.

  11. Arc Interacts with the Integral Endoplasmic Reticulum Protein, Calnexin

    Directory of Open Access Journals (Sweden)

    Craig Myrum

    2017-09-01

    Full Text Available Activity-regulated cytoskeleton-associated protein, Arc, is a major regulator of long-term synaptic plasticity and memory formation. Here we reveal a novel interaction partner of Arc, a resident endoplasmic reticulum transmembrane protein, calnexin. We show an interaction between recombinantly-expressed GST-tagged Arc and endogenous calnexin in HEK293, SH-SY5Y neuroblastoma and PC12 cells. The interaction was dependent on the central linker region of the Arc protein that is also required for endocytosis of AMPA-type glutamate receptors. High-resolution proximity-ligation assays (PLAs demonstrate molecular proximity of endogenous Arc with the cytosolic C-terminus, but not the lumenal N-terminus of calnexin. In hippocampal neuronal cultures treated with brain-derived neurotrophic factor (BDNF, Arc interacted with calnexin in the perinuclear cytoplasm and dendritic shaft. Arc also interacted with C-terminal calnexin in the adult rat dentate gyrus (DG. After induction of long-term potentiation (LTP in the perforant path projection to the DG of adult anesthetized rats, enhanced interaction between Arc and calnexin was obtained in the dentate granule cell layer (GCL. Although Arc and calnexin are both implicated in the regulation of receptor endocytosis, no modulation of endocytosis was detected in transferrin uptake assays. Previous work showed that Arc interacts with multiple protein partners to regulate synaptic transmission and nuclear signaling. The identification of calnexin as a binding partner further supports the role of Arc as a hub protein and extends the range of Arc function to the endoplasmic reticulum, though the function of the Arc/calnexin interaction remains to be defined.

  12. 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; Rousu, Juho

    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.

  13. Learning Cellular Sorting Pathways Using Protein Interactions and Sequence Motifs

    Science.gov (United States)

    Lin, Tien-Ho; Bar-Joseph, Ziv; Murphy, Robert F.

    Proper subcellular localization is critical for proteins to perform their roles in cellular functions. Proteins are transported by different cellular sorting pathways, some of which take a protein through several intermediate locations until reaching its final destination. The pathway a protein is transported through is determined by carrier proteins that bind to specific sequence motifs. In this paper we present a new method that integrates sequence, motif and protein interaction data to model how proteins are sorted through these targeting pathways. We use a hidden Markov model (HMM) to represent protein targeting pathways. The model is able to determine intermediate sorting states and to assign carrier proteins and motifs to the sorting pathways. In simulation studies, we show that the method can accurately recover an underlying sorting model. Using data for yeast, we show that our model leads to accurate prediction of subcellular localization. We also show that the pathways learned by our model recover many known sorting pathways and correctly assign proteins to the path they utilize. The learned model identified new pathways and their putative carriers and motifs and these may represent novel protein sorting mechanisms.

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

    Science.gov (United States)

    Wise, Roger P.; Dickerson, Julie A.

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Divya Mistry

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

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

    Science.gov (United States)

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

    2017-01-01

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

  17. 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

  18. The interaction of membrane DNA-binding protein with DNA

    Science.gov (United States)

    Gabrielyan, A. G.; Arakhelyan, H. H.; Zakharyan, R. A.

    1994-07-01

    A 31-kDa protein specifically binding to double-stranded DNA (ds-DNA) was isolated from plasmatic membranes of rat liver cells by means of affinity chromatography and high performance liquid chromatography (HPLC). Some of the properties of this protein were determined. Judging by the UV and circular dichroism spectroscopic data, the protein forms a complex with DNA, stabilizing its native structure, mainly in the regions rich in AT pairs. The 31-kDa protein-pAO3 plasmid DNA binding constant was determined by nitrocellulose filter analysis of protein labelled DNA complexes. The results obtained correspond to cooperative binding with DNA molecules of extended interacting ligands, having AT specificity. A possible role of the 31-kDa protein in DNA transmembrane transition processes is discussed.

  19. Structures and Interactions of Proteins in the Brain

    DEFF Research Database (Denmark)

    Nielsen, Lau Dalby

    coding for Arc protein has been domesticated from the same branch of genes that has given rise to retroviruses. We show that even despite the large evolutional distance between Arc and retroviruses. Despite large evolutionary distance Arc still self-assemble into higher order structures that resembles......The protein low density lipoprotein receptor related protein 1 (LRP1) plays multiple roles in the biology of amyloid β peptide (Aβ) and Alzheimer’s disease. LRP1 is very important for clearance of Aβ both in the brain and by facilitating Aβ export over the blood brain barrier. In spite...... the primary nucleation is increased. The data furthermore indicates that there is an interaction with Aβ oligomer state and possible also the fibrils. Another brain protein is the neuronal protein Activity-regulated cytoskeletonassociated protein (Arc) which is important for learning and memory. The gene...

  20. Yeast Interacting Proteins Database: YBR135W, YGR108W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available tes proteolysis of M-phase targets through interactions with the proteasome; role in transcriptional regulat...it and adaptor; modulates proteolysis of M-phase targets through interactions with the proteasome; role in t

  1. 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 st... partially functionally redundant with Pex31p; genetic interactions suggest action at a step downstream of s

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

    Lifescience Database Archive (English)

    Full Text Available required for cytokinesis; regulates actomyosin ring dynamics and septin localization; interacts with the formins...required for cytokinesis; regulates actomyosin ring dynamics and septin localization; interacts with the formins

  3. Yeast Interacting Proteins Database: YMR032W, YER144C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available required for cytokinesis; regulates actomyosin ring dynamics and septin localization; interacts with the formins...required for cytokinesis; regulates actomyosin ring dynamics and septin localization; interacts with the formins

  4. Yeast Interacting Proteins Database: YER144C, YMR032W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available required for cytokinesis; regulates actomyosin ring dynamics and septin localization; interacts with the formins...required for cytokinesis; regulates actomyosin ring dynamics and septin localization; interacts with the formins

  5. Yeast Interacting Proteins Database: YER081W, YDR192C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available exclusively to the cytoplasmic side; involved in RNA export, most likely at a terminal step; interacts with...exclusively to the cytoplasmic side; involved in RNA export, most likely at a terminal step; interacts with

  6. Yeast Interacting Proteins Database: YML064C, YLR377C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available autophagy-mediated degradation depending on growth conditions; interacts with Vid30p Rows with this prey as...autophagy-mediated degradation depending on growth conditions; interacts with Vid30p Rows with this prey as

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

    Lifescience Database Archive (English)

    Full Text Available autophagy-mediated degradation depending on growth conditions; interacts with Vid30p Rows with this prey as...autophagy-mediated degradation depending on growth conditions; interacts with Vid30p Rows with this prey as

  8. Engineering High Affinity Protein-Protein Interactions Using a High-Throughput Microcapillary Array Platform.

    Science.gov (United States)

    Lim, Sungwon; Chen, Bob; Kariolis, Mihalis S; Dimov, Ivan K; Baer, Thomas M; Cochran, Jennifer R

    2017-02-17

    Affinity maturation of protein-protein interactions requires iterative rounds of protein library generation and high-throughput screening to identify variants that bind with increased affinity to a target of interest. We recently developed a multipurpose protein engineering platform, termed μSCALE (Microcapillary Single Cell Analysis and Laser Extraction). This technology enables high-throughput screening of libraries of millions of cell-expressing protein variants based on their binding properties or functional activity. Here, we demonstrate the first use of the μSCALE platform for affinity maturation of a protein-protein binding interaction. In this proof-of-concept study, we engineered an extracellular domain of the Axl receptor tyrosine kinase to bind tighter to its ligand Gas6. Within 2 weeks, two iterative rounds of library generation and screening resulted in engineered Axl variants with a 50-fold decrease in kinetic dissociation rate, highlighting the use of μSCALE as a new tool for directed evolution.

  9. 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. Copyright © 2015 Elsevier B.V. All rights reserved.

  10. Yeast Interacting Proteins Database: YDR479C, YLR324W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available peroxisomal size, number and distribution; genetic interactions suggest that Pex28p and Pex29p act at steps ...ative regulation of peroxisome number; partially functionally redundant with Pex31p; genetic interactions su...regulation of peroxisomal size, number and distribution; genetic interactions suggest that Pex28p and Pex29p...negative regulation of peroxisome number; partially functionally redundant with Pex31p; genetic interactions

  11. Yeast Interacting Proteins Database: YLR324W, YDR479C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available n of peroxisome number; partially functionally redundant with Pex31p; genetic interactions suggest action at...e regulation of peroxisomal size, number and distribution; genetic interactions suggest that Pex28p and Pex2...tive regulation of peroxisome number; partially functionally redundant with Pex31p; genetic interactions sug... the regulation of peroxisomal size, number and distribution; genetic interactions suggest that Pex28p and P

  12. Yeast Interacting Proteins Database: YLR324W, YLR324W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available n of peroxisome number; partially functionally redundant with Pex31p; genetic interactions suggest action at...gative regulation of peroxisome number; partially functionally redundant with Pex31p; genetic interactions s...ative regulation of peroxisome number; partially functionally redundant with Pex31p; genetic interactions su...n negative regulation of peroxisome number; partially functionally redundant with Pex31p; genetic interactions

  13. Yeast Interacting Proteins Database: YFR008W, YMR052W [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available arrest in response to pheromone, in a pathway different from the Far1p-dependent pathway; interacts with...arrest in response to pheromone, in a pathway different from the Far1p-dependent pathway; interacts with...arrest in response to pheromone, in a pathway different from the Far1p-dependent pathway; interacts with...arrest in response to pheromone, in a pathway different from the Far1p-dependent pathway; interacts with

  14. Yeast Interacting Proteins Database: YLR377C, YLR377C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available autophagy-mediated degradation depending on growth conditions; interacts with Vid30p Rows with this bait as...autophagy-mediated degradation depending on growth conditions; interacts with Vid30p Rows with this prey as...autophagy-mediated degradation depending on growth conditions; interacts with Vid30p Rows with this bait as...autophagy-mediated degradation depending on growth conditions; interacts with Vid30p Rows with this prey as

  15. Evolution and protein interactions of AP2 proteins in Brassicaceae: Evidence linking development and environmental responses.

    Science.gov (United States)

    Zeng, Liping; Yin, Yue; You, Chenjiang; Pan, Qianli; Xu, Duo; Jin, Taijie; Zhang, Bailong; Ma, Hong

    2016-06-01

    Plants have evolved a large number of transcription factors (TF), which are enriched among duplicate genes, highlighting their roles in complex regulatory networks. The APETALA2/EREBP-like genes constitute a large plant TF family and participate in development and stress responses. To probe the conservation and divergence of AP2/EREBP genes, we analyzed the duplication patterns of this family in Brassicaceae and identified interacting proteins of representative Arabidopsis AP2/EREBP proteins. We found that many AP2/EREBP duplicates generated early in Brassicaceae history were quickly lost, but many others were retained in all tested Brassicaceae species, suggesting early functional divergence followed by persistent conservation. In addition, the sequences of the AP2 domain and exon numbers were highly conserved in rosids. Furthermore, we used 16 A. thaliana AP2/EREBP proteins as baits in yeast screens and identified 1,970 potential AP2/EREBP-interacting proteins, with a small subset of interactions verified in planta. Many AP2 genes also exhibit reduced expression in an anther-defective mutant, providing a possible link to developmental regulation. The putative AP2-interacting proteins participate in many functions in development and stress responses, including photomorphogenesis, flower development, pathogenesis, drought and cold responses, abscisic acid and auxin signaling. Our results present the AP2/EREBP evolution patterns in Brassicaceae, and support a proposed interaction network of AP2/EREBP proteins and their putative interacting proteins for further study. © 2015 Institute of Botany, Chinese Academy of Sciences.

  16. Domain-Based Predictive Models for Protein-Protein Interaction Prediction

    Directory of Open Access Journals (Sweden)

    Chen Xue-Wen

    2006-01-01

    Full Text Available Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Recently, methods for predicting protein interactions using domain information are proposed and preliminary results have demonstrated their feasibility. In this paper, we develop two domain-based statistical models (neural networks and decision trees for protein interaction predictions. Unlike most of the existing methods which consider only domain pairs (one domain from one protein and assume that domain-domain interactions are independent of each other, the proposed methods are capable of exploring all possible interactions between domains and make predictions based on all the domains. Compared to maximum-likelihood estimation methods, our experimental results show that the proposed schemes can predict protein-protein interactions with higher specificity and sensitivity, while requiring less computation time. Furthermore, the decision tree-based model can be used to infer the interactions not only between two domains, but among multiple domains as well.

  17. Interaction of silver nanoparticles with proteins: a characteristic protein concentration dependent profile of SPR signal.

    Science.gov (United States)

    Banerjee, Victor; Das, K P

    2013-11-01

    Silver nanoparticles are finding increasing applications in biological systems, for example as antimicrobial agents and potential candidates for control drug release systems. In all such applications, silver nanoparticles interact with proteins and other biomolecules. Hence, the study of such interactions is of considerable importance. While BSA has been extensively used as a model protein for the study of interaction with the silver nanoparticles, studies using other proteins are rather limited. The interaction of silver nanoparticles with light leads to collective oscillation of the conducting electrons giving rise to surface plasmon resonance (SPR). Here, we have studied the protein concentration dependence of the SPR band profiles for a number of proteins. We found that for all the proteins, with increase in concentration, the SPR band intensity initially decreased, reaching minima and then increased again leading to a characteristic "dip and rise" pattern. Minimum point of the pattern appeared to be related to the isoelectric point of the proteins. Detailed dynamic light scattering and transmission electron microscopy studies revealed that the consistency of SPR profile was dependent on the average particle size and state of association of the silver nanoparticles with the change in the protein concentration. Fluorescence spectroscopic studies showed the binding constants of the proteins with the silver nanoparticles were in the nano molar range with more than one nanoparticle binding to protein molecule. Structural studies demonstrate that protein retains its native-like structure on the nanoparticle surface unless the molar ratio of silver nanoparticles to protein exceeds 10. Our study reveals that nature of the protein concentration dependent profile of SPR signal is a general phenomena and mostly independent of the size and structure of the proteins. Copyright © 2013 Elsevier B.V. All rights reserved.

  18. Reconstruction and Application of Protein–Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Tong Hao

    2016-06-01

    Full Text Available The protein-protein interaction network (PIN is a useful tool for systematic investigation of the complex biological activities in the cell. With the increasing interests on the proteome-wide interaction networks, PINs have been reconstructed for many species, including virus, bacteria, plants, animals, and humans. With the development of biological techniques, the reconstruction methods of PIN are further improved. PIN has gradually penetrated many fields in biological research. In this work we systematically reviewed the development of PIN in the past fifteen years, with respect to its reconstruction and application of function annotation, subsystem investigation, evolution analysis, hub protein analysis, and regulation mechanism analysis. Due to the significant role of PIN in the in-depth exploration of biological process mechanisms, PIN will be preferred by more and more researchers for the systematic study of the protein systems in various kinds of organisms.

  19. 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.

  20. 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. Copyright © 2014 Elsevier B.V. All rights reserved.

  1. Analytical techniques for the study of polyphenol-protein interactions.

    Science.gov (United States)

    Poklar Ulrih, Nataša

    2017-07-03

    This mini review focuses on advances in biophysical techniques to study polyphenol interactions with proteins. Polyphenols have many beneficial pharmacological properties, as a result of which they have been the subject of intensive studies. The most conventional techniques described here can be divided into three groups: (i) methods used for screening (in-situ methods); (ii) methods used to gain insight into the mechanisms of polyphenol-protein interactions; and (iii) methods used to study protein aggregation and precipitation. All of these methods used to study polyphenol-protein interactions are based on modifications to the physicochemical properties of the polyphenols or proteins after binding/complex formation in solution. To date, numerous review articles have been published in the field of polyphenols. This review will give a brief insight in computational methods and biosensors and cell-based methods, spectroscopic methods including fluorescence emission, UV-vis adsorption, circular dichroism, Fourier transform infrared and mass spectrometry, nuclear magnetic resonance, X-ray diffraction, and light scattering techniques including small-angle X-ray scattering and small-angle neutron scattering, and calorimetric techniques (isothermal titration calorimetry and differential scanning calorimetry), microscopy, the techniques which have been successfully used for polyphenol-protein interactions. At the end the new methods based on single molecule detection with high potential to study polyphenol-protein interactions will be presented. The advantages and disadvantages of each technique will be discussed as well as the thermodynamic, kinetic or structural parameters, which can be obtained. The other relevant biophysical experimental techniques that have proven to be valuable, such electrochemical methods, hydrodynamic techniques and chromatographic techniques will not be described here.

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

    Science.gov (United States)

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

    2018-01-05

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

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

    Science.gov (United States)

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

    2016-11-01

    Seminal plasma Binder of SPerm (BSP) proteins bind to sperm at ejaculation and promote capacitation. When in excess, however, BSP proteins damage the sperm membrane. It has been suggested that milk components of semen extenders associate with BSP proteins, potentially protecting sperm. Thus, this study was conducted to investigate if milk proteins interact with BSP proteins and reduce BSP binding to goat sperm. Using gel filtration chromatography, milk was incubated with goat seminal plasma proteins and loaded onto columns with and without calcium. Milk was also fractionated into parts containing mostly whey proteins or mostly caseins, incubated with seminal plasma proteins and subjected to gel filtration. Eluted fractions were evaluated by immunoblot using anti-goat BSP antibodies, confirming milk protein-BSP protein interactions. As determined by ELISA, milk proteins coated on polystyrene wells bound to increasing of goat BSP proteins. Far-western dot blots confirmed that BSP proteins bound to caseins and β-lactoglobulin in a concentration-dependent manner. Then, cauda epididymal sperm from five goats was incubated with seminal plasma; seminal plasma followed by milk; and milk followed by seminal plasma. Sperm membrane proteins were extracted and evaluated by immunoblotting. The pattern of BSP binding to sperm membrane proteins was reduced by 59.3 % when epididymal sperm were incubated with seminal plasma and then with skimmed milk (p sperm were treated with milk followed by seminal plasma, coating of sperm with BSP proteins was not significantly reduced (57.6 %; p > 0.05). In conclusion, goat BSP proteins have an affinity for caseins and whey proteins. Milk reduces BSP binding to goat sperm, depending whether or not sperm had been previously exposed to seminal plasma. Such events may explain the protective effect of milk during goat sperm preservation.

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

    Science.gov (United States)

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

    2012-01-01

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

  5. 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.

  6. 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.

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

    Science.gov (United States)

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

    2012-01-01

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

  8. Sorting of integral membrane proteins mediated by curvature-dependent protein-lipid bilayer interaction.

    Science.gov (United States)

    Božič, Bojan; Das, Sovan L; Svetina, Saša

    2015-03-28

    Cell membrane proteins, both bound and integral, are known to preferentially accumulate at membrane locations with curvatures favorable to their shape. This is mainly due to the curvature dependent interaction between membrane proteins and their lipid environment. Here, we analyze the effects of the protein-lipid bilayer interaction energy due to mismatch between the protein shape and the principal curvatures of the surrounding bilayer. The role of different macroscopic parameters that define the interaction energy term is elucidated in relation to recent experiment in which the lateral distribution of a membrane embedded protein potassium channel KvAP is measured on a giant unilamellar lipid vesicle (reservoir) and a narrow tubular extension - a tether - kept at constant length. The dependence of the sorting ratio, defined as the ratio between the areal density of the protein on the tether and on the vesicle, on the inverse tether radius is influenced by the strength of the interaction, the intrinsic shape of the membrane embedded protein, and its abundance in the reservoir. It is described how the values of these constants can be extracted from experiments. The intrinsic principal curvatures of a protein are related to the tether radius at which the sorting ratio attains its maximum value. The estimate of the principal intrinsic curvature of the protein KvAP, obtained by comparing the experimental and theoretical sorting behavior, is consistent with the available information on its structure.

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

    Directory of Open Access Journals (Sweden)

    Jillian L Blatti

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

  10. Circular Dichroism for the Analysis of Protein-DNA Interactions.

    Science.gov (United States)

    Scarlett, Garry; Siligardi, Giuliano; Kneale, Geoffrey G

    2015-01-01

    The aim of this chapter is to provide information on the practical aspects of circular dichroism (CD) and synchrotron radiation circular dichroism (SRCD) in protein-nucleic acids interaction solution studies. The chapter will describe the guidelines appropriate to designing experiments and conducting correct data interpretation, the use of both benchtop and synchrotron CD approaches is discussed and the advantages of SRCD outlined. Further information and a good general review of the field a can be found in Gray (Circular Dichroism of protein-nucleic acid interactions. In: Fasman GD (ed) Circular dichroism and the conformational analysis of biomolecules. Plenum Press, New York. pp 469-500, 1996).

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

    Science.gov (United States)

    Jakobek, Lidija

    2015-05-15

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

  12. Dynamics of DNA conformations and DNA-protein interaction

    DEFF Research Database (Denmark)

    Metzler, R.; Ambjörnsson, T.; Lomholt, Michael Andersen

    2005-01-01

    Optical tweezers, atomic force microscopes, patch clamping, or fluorescence techniques make it possible to study both the equilibrium conformations and dynamics of single DNA molecules as well as their interaction with binding proteins. In this paper we address the dynamics of local DNA denaturat......Optical tweezers, atomic force microscopes, patch clamping, or fluorescence techniques make it possible to study both the equilibrium conformations and dynamics of single DNA molecules as well as their interaction with binding proteins. In this paper we address the dynamics of local DNA...

  13. DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein interactions

    Directory of Open Access Journals (Sweden)

    Liu Guozhen

    2008-10-01

    Full Text Available Abstract Background Charting the interactions among genes and among their protein products is essential for understanding biological systems. A flood of interaction data is emerging from high throughput technologies, computational approaches, and literature mining methods. Quick and efficient access to this data has become a critical issue for biologists. Several excellent multi-organism databases for gene and protein interactions are available, yet most of these have understandable difficulty maintaining comprehensive information for any one organism. No single database, for example, includes all available interactions, integrated gene expression data, and comprehensive and searchable gene information for the important model organism, Drosophila melanogaster. Description DroID, the Drosophila Interactions Database, is a comprehensive interactions database designed specifically for Drosophila. DroID houses published physical protein interactions, genetic interactions, and computationally predicted interactions, including interologs based on data for other model organisms and humans. All interactions are annotated with original experimental data and source information. DroID can be searched and filtered based on interaction information or a comprehensive set of gene attributes from Flybase. DroID also contains gene expression and expression correlation data that can be searched and used to filter datasets, for example, to focus a study on sub-networks of co-expressed genes. To address the inherent noise in interaction data, DroID employs an updatable confidence scoring system that assigns a score to each physical interaction based on the likelihood that it represents a biologically significant link. Conclusion DroID is the most comprehensive interactions database available for Drosophila. To facilitate downstream analyses, interactions are annotated with original experimental information, gene expression data, and confidence scores. All data in

  14. 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

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

    Science.gov (United States)

    Nguyen, Thanh-Phuong; Jordán, Ferenc

    2010-07-29

    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. 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. 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 particular pathways. We have found that the mediators

  16. Yeast Interacting Proteins Database: YLR082C, YLR082C [Yeast Interacting Proteins Database

    Lifescience Database Archive (English)

    Full Text Available YLR082C SRL2 Protein of unknown function; overexpression suppresses the lethality cause...Protein of unknown function; overexpression suppresses the lethality caused by a rad53 null mutation Rows wi...; overexpression suppresses the lethality caused by a rad53 null mutation Rows with this bait as bait Rows w... (1) Prey ORF YLR082C Prey gene name SRL2 Prey description Protein of unknown function; overexpression suppresses the lethality cause

  17. Evaluation of two dependency parsers on biomedical corpus targeted at protein-protein interactions.

    Science.gov (United States)

    Pyysalo, Sampo; Ginter, Filip; Pahikkala, Tapio; Boberg, Jorma; Järvinen, Jouni; Salakoski, Tapio

    2006-06-01

    We present an evaluation of Link Grammar and Connexor Machinese Syntax, two major broad-coverage dependency parsers, on a custom hand-annotated corpus consisting of sentences regarding protein-protein interactions. In the evaluation, we apply the notion of an interaction subgraph, which is the subgraph of a dependency graph expressing a protein-protein interaction. We measure the performance of the parsers for recovery of individual dependencies, fully correct parses, and interaction subgraphs. For Link Grammar, an open system that can be inspected in detail, we further perform a comprehensive failure analysis, report specific causes of error, and suggest potential modifications to the grammar. We find that both parsers perform worse on biomedical English than previously reported on general English. While Connexor Machinese Syntax significantly outperforms Link Grammar, the failure analysis suggests specific ways in which the latter could be modified for better performance in the domain.

  18. Systematic discovery of new recognition peptides mediating protein interaction networks.

    Directory of Open Access Journals (Sweden)

    2005-12-01

    Full Text Available Many aspects of cell signalling, trafficking, and targeting are governed by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or "linear motif" (e.g., SH3 binding to PxxP. Many domains are known, though comparatively few linear motifs have been discovered. Their short length (three to eight residues, and the fact that they often reside in disordered regions in proteins makes them difficult to detect through sequence comparison or experiment. Nevertheless, each new motif provides critical molecular details of how interaction networks are constructed, and can explain how one protein is able to bind to very different partners. Here we show that binding motifs can be detected using data from genome-scale interaction studies, and thus avoid the normally slow discovery process. Our approach based on motif over-representation in non-homologous sequences, rediscovers known motifs and predicts dozens of others. Direct binding experiments reveal that two predicted motifs are indeed protein-binding modules: a DxxDxxxD protein phosphatase 1 binding motif with a KD of 22 microM and a VxxxRxYS motif that binds Translin with a KD of 43 microM. We estimate that there are dozens or even hundreds of linear motifs yet to be discovered that will give molecular insight into protein networks and greatly illuminate cellular processes.

  19. 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

  20. Comparative analysis of protein-protein interactions in the defense response of rice and wheat.

    Science.gov (United States)

    Cantu, Dario; Yang, Baoju; Ruan, Randy; Li, Kun; Menzo, Virginia; Fu, Daolin; Chern, Mawsheng; Ronald, Pamela C; Dubcovsky, Jorge

    2013-03-12

    Despite the importance of wheat as a major staple crop and the negative impact of diseases on its production worldwide, the genetic mechanisms and gene interactions involved in the resistance response in wheat are still poorly understood. The complete sequence of the rice genome has provided an extremely useful parallel road map for genetic and genomics studies in wheat. The recent construction of a defense response interactome in rice has the potential to further enhance the translation of advances in rice to wheat and other grasses. The objective of this study was to determine the degree of conservation in the protein-protein interactions in the rice and wheat defense response interactomes. As entry points we selected proteins that serve as key regulators of the rice defense response: the RAR1/SGT1/HSP90 protein complex, NPR1, XA21, and XB12 (XA21 interacting protein 12). Using available wheat sequence databases and phylogenetic analyses we identified and cloned the wheat orthologs of these four rice proteins, including recently duplicated paralogs, and their known direct interactors and tested 86 binary protein interactions using yeast-two-hybrid (Y2H) assays. All interactions between wheat proteins were further tested using in planta bimolecular fluorescence complementation (BiFC). Eighty three percent of the known rice interactions were confirmed when wheat proteins were tested with rice interactors and 76% were confirmed using wheat protein pairs. All interactions in the RAR1/SGT1/ HSP90, NPR1 and XB12 nodes were confirmed for the identified orthologous wheat proteins, whereas only forty four percent of the interactions were confirmed in the interactome node centered on XA21. We hypothesize that this reduction may be associated with a different sub-functionalization history of the multiple duplications that occurred in this gene family after the divergence of the wheat and rice lineages. The observed high conservation of interactions between proteins that