Science.gov

Sample records for aids protein-protein interaction

  1. Bacteriophage protein-protein interactions.

    PubMed

    Häuser, Roman; Blasche, Sonja; Dokland, Terje; Haggård-Ljungquist, Elisabeth; von Brunn, Albrecht; Salas, Margarita; Casjens, Sherwood; Molineux, Ian; Uetz, Peter

    2012-01-01

    Bacteriophages T7, λ, P22, and P2/P4 (from Escherichia coli), as well as ϕ29 (from Bacillus subtilis), are among the best-studied bacterial viruses. This chapter summarizes published protein interaction data of intraviral protein interactions, as well as known phage-host protein interactions of these phages retrieved from the literature. We also review the published results of comprehensive protein interaction analyses of Pneumococcus phages Dp-1 and Cp-1, as well as coliphages λ and T7. For example, the ≈55 proteins encoded by the T7 genome are connected by ≈43 interactions with another ≈15 between the phage and its host. The chapter compiles published interactions for the well-studied phages λ (33 intra-phage/22 phage-host), P22 (38/9), P2/P4 (14/3), and ϕ29 (20/2). We discuss whether different interaction patterns reflect different phage lifestyles or whether they may be artifacts of sampling. Phages that infect the same host can interact with different host target proteins, as exemplified by E. coli phage λ and T7. Despite decades of intensive investigation, only a fraction of these phage interactomes are known. Technical limitations and a lack of depth in many studies explain the gaps in our knowledge. Strategies to complete current interactome maps are described. Although limited space precludes detailed overviews of phage molecular biology, this compilation will allow future studies to put interaction data into the context of phage biology. PMID:22748812

  2. Imaging Protein-protein Interactions in vivo

    PubMed Central

    Seegar, Tom; Barton, William

    2010-01-01

    Protein-protein interactions are a hallmark of all essential cellular processes. However, many of these interactions are transient, or energetically weak, preventing their identification and analysis through traditional biochemical methods such as co-immunoprecipitation. In this regard, the genetically encodable fluorescent proteins (GFP, RFP, etc.) and their associated overlapping fluorescence spectrum have revolutionized our ability to monitor weak interactions in vivo using Förster resonance energy transfer (FRET)1-3. Here, we detail our use of a FRET-based proximity assay for monitoring receptor-receptor interactions on the endothelial cell surface. PMID:20972411

  3. PREFACE: Protein protein interactions: principles and predictions

    NASA Astrophysics Data System (ADS)

    Nussinov, Ruth; Tsai, Chung-Jung

    2005-06-01

    Proteins are the `workhorses' of the cell. Their roles span functions as diverse as being molecular machines and signalling. They carry out catalytic reactions, transport, form viral capsids, traverse membranes and form regulated channels, transmit information from DNA to RNA, making possible the synthesis of new proteins, and they are responsible for the degradation of unnecessary proteins and nucleic acids. They are the vehicles of the immune response and are responsible for viral entry into the cell. Given their importance, considerable effort has been centered on the prediction of protein function. A prime way to do this is through identification of binding partners. If the function of at least one of the components with which the protein interacts is known, that should let us assign its function(s) and the pathway(s) in which it plays a role. This holds since the vast majority of their chores in the living cell involve protein-protein interactions. Hence, through the intricate network of these interactions we can map cellular pathways, their interconnectivities and their dynamic regulation. Their identification is at the heart of functional genomics; their prediction is crucial for drug discovery. Knowledge of the pathway, its topology, length, and dynamics may provide useful information for forecasting side effects. The goal of predicting protein-protein interactions is daunting. Some associations are obligatory, others are continuously forming and dissociating. In principle, from the physical standpoint, any two proteins can interact, but under what conditions and at which strength? The principles of protein-protein interactions are general: the non-covalent interactions of two proteins are largely the outcome of the hydrophobic effect, which drives the interactions. In addition, hydrogen bonds and electrostatic interactions play important roles. Thus, many of the interactions observed in vitro are the outcome of experimental overexpression. Protein disorder

  4. Length, protein protein interactions, and complexity

    NASA Astrophysics Data System (ADS)

    Tan, Taison; Frenkel, Daan; Gupta, Vishal; Deem, Michael W.

    2005-05-01

    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 are more able to distinguish among a larger number of distinct interactions due to their greater average surface area. Annotated protein sequences available from the SWISS-PROT database were analyzed for 13 eukaryotes, eight bacteria, and two archaea species. The number of subcellular locations to which each protein is associated is used as a measure of the number of interactions to which a protein participates. Two databases of yeast protein-protein interactions were used as another measure of the number of interactions to which each S. cerevisiae protein participates. Protein length is shown to correlate with both number of subcellular locations to which a protein is associated and number of interactions as measured by yeast two-hybrid experiments. Protein length is also shown to correlate with the probability that the protein is encoded by an essential gene. Interestingly, average protein length and number of subcellular locations are not significantly different between all human proteins and protein targets of known, marketed drugs. Increased protein length appears to be a significant mechanism by which the increasing complexity of protein-protein interaction networks is accommodated within the natural evolution of species. Consideration of protein length may be a valuable tool in drug design, one that predicts different strategies for inhibiting interactions in aberrant and normal pathways.

  5. Protein-protein interactions as drug targets.

    PubMed

    Skwarczynska, Malgorzata; Ottmann, Christian

    2015-10-01

    Modulation of protein-protein interactions (PPIs) is becoming increasingly important in drug discovery and chemical biology. While a few years ago this 'target class' was deemed to be largely undruggable an impressing number of publications and success stories now show that targeting PPIs with small, drug-like molecules indeed is a feasible approach. Here, we summarize the current state of small-molecule inhibition and stabilization of PPIs and review the active molecules from a structural and medicinal chemistry angle, especially focusing on the key examples of iNOS, LFA-1 and 14-3-3. PMID:26510391

  6. PINT: Protein-protein Interactions Thermodynamic Database.

    PubMed

    Kumar, M D Shaji; Gromiha, M Michael

    2006-01-01

    The first release of Protein-protein Interactions Thermodynamic Database (PINT) contains >1500 data of several thermodynamic parameters along with sequence and structural information, experimental conditions and literature information. Each entry contains numerical data for the free energy change, dissociation constant, association constant, enthalpy change, heat capacity change and so on of the interacting proteins upon binding, which are important for understanding the mechanism of protein-protein interactions. PINT also includes the name and source of the proteins involved in binding, their Protein Information Resource, SWISS-PROT and Protein Data Bank (PDB) codes, secondary structure and solvent accessibility of residues at mutant positions, measuring methods, experimental conditions, such as buffers, ions and additives, and literature information. A WWW interface facilitates users to search data based on various conditions, feasibility to select the terms for output and different sorting options. Further, PINT is cross-linked with other related databases, PIR, SWISS-PROT, PDB and NCBI PUBMED literature database. The database is freely available at http://www.bioinfodatabase.com/pint/index.html. PMID:16381844

  7. Wiki-Pi: A Web-Server of Annotated Human Protein-Protein Interactions to Aid in Discovery of Protein Function

    PubMed Central

    Orii, Naoki; Ganapathiraju, Madhavi K.

    2012-01-01

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

  8. Biochemical Approaches for Discovering Protein-Protein Interactions

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Protein-protein interactions or protein complexes are indigenous to nearly all cellular processes, ranging from metabolism to structure. Elucidating both individual protein associations and complex protein interaction networks, while challenging, is an essential goal of functional genomics. For ex...

  9. Split-Protein Systems: Beyond Binary Protein-Protein Interactions

    PubMed Central

    Shekhawat, Sujan S.; Ghosh, Indraneel

    2011-01-01

    It has been estimated that 650,000 protein-protein interactions exist in the human interactome [1], a subset of all possible macromolecular partnerships that dictate life. Thus there is a continued need for the development of sensitive and user-friendly methods for cataloguing biomacromolecules in complex environments and for detecting their interactions, modifications, and cellular location. Such methods also allow for establishing differences in the interactome between a normal and diseased cellular state and for quantifying the outcome of therapeutic intervention. A promising approach for deconvoluting the role of macromolecular partnerships is split-protein reassembly, also called protein fragment complementation. This approach relies on the appropriate fragmentation of protein reporters, such as the green fluorescent protein or firefly luciferase, which when attached to possible interacting partners can reassemble and regain function, thereby confirming the partnership. Split-protein methods have been effectively utilized for detecting protein-protein interactions in cell-free systems, E. coli, yeast, mammalian cells, plants, and live animals. Herein, we present recent advances in engineering split-protein systems that allow for the rapid detection of ternary protein complexes, small molecule inhibitors, as well as a variety of macromolecules including nucleic acids, poly(ADP) ribose, and iron sulfur clusters. We also present advances that combine split-protein systems with chemical inducers of dimerization strategies that allow for regulating the activity of orthogonal split-proteases as well as aid in identifying enzyme inhibitors. Finally, we discuss autoinhibition strategies leading to turn-on sensors as well as future directions in split-protein methodology including possible therapeutic approaches. PMID:22070901

  10. Split-protein systems: beyond binary protein-protein interactions.

    PubMed

    Shekhawat, Sujan S; Ghosh, Indraneel

    2011-12-01

    It has been estimated that 650,000 protein-protein interactions exist in the human interactome (Stumpf et al., 2008), a subset of all possible macromolecular partnerships that dictate life. Thus there is a continued need for the development of sensitive and user-friendly methods for cataloguing biomacromolecules in complex environments and for detecting their interactions, modifications, and cellular location. Such methods also allow for establishing differences in the interactome between a normal and diseased cellular state and for quantifying the outcome of therapeutic intervention. A promising approach for deconvoluting the role of macromolecular partnerships is split-protein reassembly, also called protein fragment complementation. This approach relies on the appropriate fragmentation of protein reporters, such as the green fluorescent protein or firefly luciferase, which when attached to possible interacting partners can reassemble and regain function, thereby confirming the partnership. Split-protein methods have been effectively utilized for detecting protein-protein interactions in cell-free systems, Escherichia coli, yeast, mammalian cells, plants, and live animals. Herein, we present recent advances in engineering split-protein systems that allow for the rapid detection of ternary protein complexes, small molecule inhibitors, as well as a variety of macromolecules including nucleic acids, poly(ADP) ribose, and iron sulfur clusters. We also present advances that combine split-protein systems with chemical inducers of dimerization strategies that allow for regulating the activity of orthogonal split-proteases as well as aid in identifying enzyme inhibitors. Finally, we discuss autoinhibition strategies leading to turn-on sensors as well as future directions in split-protein methodology including possible therapeutic approaches. PMID:22070901

  11. Computational drug design targeting protein-protein interactions.

    PubMed

    Bienstock, Rachelle J

    2012-01-01

    Novel discoveries in molecular disease pathways within the cell, combined with increasing information regarding protein binding partners has lead to a new approach in drug discovery. There is interest in designing drugs to modulate protein-protein interactions as opposed to solely targeting the catalytic active site within a single enzyme or protein. There are many challenges in this new approach to drug discovery, particularly since the protein-protein interface has a larger surface area, can comprise a discontinuous epitope, and is more amorphous and less well defined than the typical drug design target, a small contained enzyme-binding pocket. Computational methods to predict modes of protein-protein interaction, as well as protein interface hot spots, have garnered significant interest, in order to facilitate the development of drugs to successfully disrupt and inhibit protein-protein interactions. This review summarizes some current methods available for computational protein-protein docking, as well as tabulating some examples of the successful design of antagonists and small molecule inhibitors for protein-protein interactions. Several of these drugs are now beginning to appear in the clinic. PMID:22316151

  12. Prediction of protein-protein interactions based on protein-protein correlation using least squares regression.

    PubMed

    Huang, De-Shuang; Zhang, Lei; Han, Kyungsook; Deng, Suping; Yang, Kai; Zhang, Hongbo

    2014-01-01

    In order to transform protein sequences into the feature vectors, several works have been done, such as computing auto covariance (AC), conjoint triad (CT), local descriptor (LD), moran autocorrelation (MA), normalized moreaubroto autocorrelation (NMB) and so on. In this paper, we shall adopt these transformation methods to encode the proteins, respectively, where AC, CT, LD, MA and NMB are all represented by '+' in a unified manner. A new method, i.e. the combination of least squares regression with '+' (abbreviated as LSR(+)), will be introduced for encoding a protein-protein correlation-based feature representation and an interacting protein pair. Thus there are totally five different combinations for LSR(+), i.e. LSRAC, LSRCT, LSRLD, LSRMA and LSRNMB. As a result, we combined a support vector machine (SVM) approach with LSR(+) to predict protein-protein interactions (PPI) and PPI networks. The proposed method has been applied on four datasets, i.e. Saaccharomyces cerevisiae, Escherichia coli, Homo sapiens and Caenorhabditis elegans. The experimental results demonstrate that all LSR(+) methods outperform many existing representative algorithms. Therefore, LSR(+) is a powerful tool to characterize the protein-protein correlations and to infer PPI, whilst keeping high performance on prediction of PPI networks. PMID:25059329

  13. Direct Probing of Protein-Protein Interactions

    SciTech Connect

    Noy, A; Sulchek, T A; Friddle, R W

    2005-03-10

    This project aimed to establish feasibility of using experimental techniques based on direct measurements of interaction forces on the single molecule scale to characterize equilibrium interaction potentials between individual biological molecules. Such capability will impact several research areas, ranging from rapid interaction screening capabilities to providing verifiable inputs for computational models. It should be one of the enabling technologies for modern proteomics research. This study used a combination of Monte-Carlo simulations, theoretical considerations, and direct experimental measurements to investigate two model systems that represented typical experimental situations: force-induced melting of DNA rigidly attached to the tip, and force-induced unbinding of a protein-antibody pair connected to flexible tethers. Our results establish that for both systems researchers can use force spectroscopy measurements to extract reliable information about equilibrium interaction potentials. However, the approaches necessary to extract these potentials in each case--Jarzynski reconstruction and Dynamic Force Spectroscopy--are very different. We also show how the thermodynamics and kinetics of unbinding process dictates the choice between in each case.

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

    NASA Astrophysics Data System (ADS)

    Freire, Ernesto

    2010-03-01

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

  15. Transient protein-protein interactions visualized by solution NMR.

    PubMed

    Liu, Zhu; Gong, Zhou; Dong, Xu; Tang, Chun

    2016-01-01

    Proteins interact with each other to establish their identities in cell. The affinities for the interactions span more than ten orders of magnitude, and KD values in μM-mM regimen are considered transient and are important in cell signaling. Solution NMR including diamagnetic and paramagnetic techniques has enabled atomic-resolution depictions of transient protein-protein interactions. Diamagnetic NMR allows characterization of protein complexes with KD values up to several mM, whereas ultraweak and fleeting complexes can be modeled with the use of paramagnetic NMR especially paramagnetic relaxation enhancement (PRE). When tackling ever-larger protein complexes, PRE can be particularly useful in providing long-range intermolecular distance restraints. As NMR measurements are averaged over the ensemble of complex structures, structural information for dynamic protein-protein interactions besides the stereospecific one can often be extracted. Herein the protein interaction dynamics are exemplified by encounter complexes, alternative binding modes, and coupled binding/folding of intrinsically disordered proteins. Further integration of NMR with other biophysical techniques should allow better visualization of transient protein-protein interactions. In particular, single-molecule data may facilitate the interpretation of ensemble-averaged NMR data. Though same structures of proteins and protein complexes were found in cell as in diluted solution, we anticipate that the dynamics of transient protein protein-protein interactions be different, which awaits awaits exploration by NMR. This article is part of a Special Issue entitled: Physiological Enzymology and Protein Functions. This article is part of a Special Issue entitled: Physiological Enzymology and Protein Functions. PMID:25896389

  16. How Many Protein-Protein Interactions Types Exist in Nature?

    PubMed Central

    Mitra, Pralay; Zhang, Yang

    2012-01-01

    “Protein quaternary structure universe” refers to the ensemble of all protein-protein complexes across all organisms in nature. The number of quaternary folds thus corresponds to the number of ways proteins physically interact with other proteins. This study focuses on answering two basic questions: Whether the number of protein-protein interactions is limited and, if yes, how many different quaternary folds exist in nature. By all-to-all sequence and structure comparisons, we grouped the protein complexes in the protein data bank (PDB) into 3,629 families and 1,761 folds. A statistical model was introduced to obtain the quantitative relation between the numbers of quaternary families and quaternary folds in nature. The total number of possible protein-protein interactions was estimated around 4,000, which indicates that the current protein repository contains only 42% of quaternary folds in nature and a full coverage needs approximately a quarter century of experimental effort. The results have important implications to the protein complex structural modeling and the structure genomics of protein-protein interactions. PMID:22719985

  17. How many protein-protein interactions types exist in nature?

    PubMed

    Garma, Leonardo; Mukherjee, Srayanta; Mitra, Pralay; Zhang, Yang

    2012-01-01

    "Protein quaternary structure universe" refers to the ensemble of all protein-protein complexes across all organisms in nature. The number of quaternary folds thus corresponds to the number of ways proteins physically interact with other proteins. This study focuses on answering two basic questions: Whether the number of protein-protein interactions is limited and, if yes, how many different quaternary folds exist in nature. By all-to-all sequence and structure comparisons, we grouped the protein complexes in the protein data bank (PDB) into 3,629 families and 1,761 folds. A statistical model was introduced to obtain the quantitative relation between the numbers of quaternary families and quaternary folds in nature. The total number of possible protein-protein interactions was estimated around 4,000, which indicates that the current protein repository contains only 42% of quaternary folds in nature and a full coverage needs approximately a quarter century of experimental effort. The results have important implications to the protein complex structural modeling and the structure genomics of protein-protein interactions. PMID:22719985

  18. Energy design for protein-protein interactions

    PubMed Central

    Ravikant, D. V. S.; Elber, Ron

    2011-01-01

    Proteins bind to other proteins efficiently and specifically to carry on many cell functions such as signaling, activation, transport, enzymatic reactions, and more. To determine the geometry and strength of binding of a protein pair, an energy function is required. An algorithm to design an optimal energy function, based on empirical data of protein complexes, is proposed and applied. Emphasis is made on negative design in which incorrect geometries are presented to the algorithm that learns to avoid them. For the docking problem the search for plausible geometries can be performed exhaustively. The possible geometries of the complex are generated on a grid with the help of a fast Fourier transform algorithm. A novel formulation of negative design makes it possible to investigate iteratively hundreds of millions of negative examples while monotonically improving the quality of the potential. Experimental structures for 640 protein complexes are used to generate positive and negative examples for learning parameters. The algorithm designed in this work finds the correct binding structure as the lowest energy minimum in 318 cases of the 640 examples. Further benchmarks on independent sets confirm the significant capacity of the scoring function to recognize correct modes of interactions. PMID:21842951

  19. Energy design for protein-protein interactions

    NASA Astrophysics Data System (ADS)

    Ravikant, D. V. S.; Elber, Ron

    2011-08-01

    Proteins bind to other proteins efficiently and specifically to carry on many cell functions such as signaling, activation, transport, enzymatic reactions, and more. To determine the geometry and strength of binding of a protein pair, an energy function is required. An algorithm to design an optimal energy function, based on empirical data of protein complexes, is proposed and applied. Emphasis is made on negative design in which incorrect geometries are presented to the algorithm that learns to avoid them. For the docking problem the search for plausible geometries can be performed exhaustively. The possible geometries of the complex are generated on a grid with the help of a fast Fourier transform algorithm. A novel formulation of negative design makes it possible to investigate iteratively hundreds of millions of negative examples while monotonically improving the quality of the potential. Experimental structures for 640 protein complexes are used to generate positive and negative examples for learning parameters. The algorithm designed in this work finds the correct binding structure as the lowest energy minimum in 318 cases of the 640 examples. Further benchmarks on independent sets confirm the significant capacity of the scoring function to recognize correct modes of interactions.

  20. PPIM: A Protein-Protein Interaction Database for Maize.

    PubMed

    Zhu, Guanghui; Wu, Aibo; Xu, Xin-Jian; Xiao, Pei-Pei; Lu, Le; Liu, Jingdong; Cao, Yongwei; Chen, Luonan; Wu, Jun; Zhao, Xing-Ming

    2016-02-01

    Maize (Zea mays) is one of the most important crops worldwide. To understand the biological processes underlying various traits of the crop (e.g. yield and response to stress), a detailed protein-protein interaction (PPI) network is highly demanded. Unfortunately, there are very few such PPIs available in the literature. Therefore, in this work, we present the Protein-Protein Interaction Database for Maize (PPIM), which covers 2,762,560 interactions among 14,000 proteins. The PPIM contains not only accurately predicted PPIs but also those molecular interactions collected from the literature. The database is freely available at http://comp-sysbio.org/ppim with a user-friendly powerful interface. We believe that the PPIM resource can help biologists better understand the maize crop. PMID:26620522

  1. Protein-protein interactions and genetic diseases: The Interactome

    PubMed Central

    Lage, Kasper

    2014-01-01

    Protein-protein interactions mediate essentially all biological processes. Despite the quality of these data being widely questioned a decade ago, the reproducibility of large-scale protein interaction data is now much improved and there is little question that the latest screens are of high quality. Moreover, common data standards and coordinated curation practices between the databases that collect the interactions have made these valuable data available to a wide group of researchers. Here, I will review how protein-protein interactions are measured, collected and quality controlled. I discuss how the architecture of molecular protein networks have informed disease biology, and how these data are now being computationally integrated with the newest genomic technologies, in particular genome-wide association studies and exome-sequencing projects, to improve our understanding of molecular processes perturbed by genetics in human diseases. PMID:24892209

  2. Current Experimental Methods for Characterizing Protein-Protein Interactions.

    PubMed

    Zhou, Mi; Li, Qing; Wang, Renxiao

    2016-04-19

    Protein molecules often interact with other partner protein molecules in order to execute their vital functions in living organisms. Characterization of protein-protein interactions thus plays a central role in understanding the molecular mechanism of relevant protein molecules, elucidating the cellular processes and pathways relevant to health or disease for drug discovery, and charting large-scale interaction networks in systems biology research. A whole spectrum of methods, based on biophysical, biochemical, or genetic principles, have been developed to detect the time, space, and functional relevance of protein-protein interactions at various degrees of affinity and specificity. This article presents an overview of these experimental methods, outlining the principles, strengths and limitations, and recent developments of each type of method. PMID:26864455

  3. How do oncoprotein mutations rewire protein-protein interaction networks?

    PubMed

    Bowler, Emily H; Wang, Zhenghe; Ewing, Rob M

    2015-01-01

    The acquisition of mutations that activate oncogenes or inactivate tumor suppressors is a primary feature of most cancers. Mutations that directly alter protein sequence and structure drive the development of tumors through aberrant expression and modification of proteins, in many cases directly impacting components of signal transduction pathways and cellular architecture. Cancer-associated mutations may have direct or indirect effects on proteins and their interactions and while the effects of mutations on signaling pathways have been widely studied, how mutations alter underlying protein-protein interaction networks is much less well understood. Systematic mapping of oncoprotein protein interactions using proteomics techniques as well as computational network analyses is revealing how oncoprotein mutations perturb protein-protein interaction networks and drive the cancer phenotype. PMID:26325016

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

    NASA Astrophysics Data System (ADS)

    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.

  5. Signature Product Code for Predicting Protein-Protein Interactions

    SciTech Connect

    Martin, Shawn B.; Brown, William M.

    2004-09-25

    The SigProdV1.0 software consists of four programs which together allow the prediction of protein-protein interactions using only amino acid sequences and experimental data. The software is based on the use of tensor products of amino acid trimers coupled with classifiers known as support vector machines. Essentially the program looks for amino acid trimer pairs which occur more frequently in protein pairs which are known to interact. These trimer pairs are then used to make predictions about unknown protein pairs. A detailed description of the method can be found in the paper: S. Martin, D. Roe, J.L. Faulon. "Predicting protein-protein interactions using signature products," Bioinformatics, available online from Advance Access, Aug. 19, 2004.

  6. Signature Product Code for Predicting Protein-Protein Interactions

    Energy Science and Technology Software Center (ESTSC)

    2004-09-25

    The SigProdV1.0 software consists of four programs which together allow the prediction of protein-protein interactions using only amino acid sequences and experimental data. The software is based on the use of tensor products of amino acid trimers coupled with classifiers known as support vector machines. Essentially the program looks for amino acid trimer pairs which occur more frequently in protein pairs which are known to interact. These trimer pairs are then used to make predictionsmore » about unknown protein pairs. A detailed description of the method can be found in the paper: S. Martin, D. Roe, J.L. Faulon. "Predicting protein-protein interactions using signature products," Bioinformatics, available online from Advance Access, Aug. 19, 2004.« less

  7. Protein-protein interactions: methods for detection and analysis.

    PubMed Central

    Phizicky, E M; Fields, S

    1995-01-01

    The function and activity of a protein are often modulated by other proteins with which it interacts. This review is intended as a practical guide to the analysis of such protein-protein interactions. We discuss biochemical methods such as protein affinity chromatography, affinity blotting, coimmunoprecipitation, and cross-linking; molecular biological methods such as protein probing, the two-hybrid system, and phage display: and genetic methods such as the isolation of extragenic suppressors, synthetic mutants, and unlinked noncomplementing mutants. We next describe how binding affinities can be evaluated by techniques including protein affinity chromatography, sedimentation, gel filtration, fluorescence methods, solid-phase sampling of equilibrium solutions, and surface plasmon resonance. Finally, three examples of well-characterized domains involved in multiple protein-protein interactions are examined. The emphasis of the discussion is on variations in the approaches, concerns in evaluating the results, and advantages and disadvantages of the techniques. PMID:7708014

  8. Solid State NMR and Protein-Protein Interactions in Membranes

    PubMed Central

    Miao, Yimin; Cross, Timothy A.

    2013-01-01

    Solid state NMR spectroscopy has evolved rapidly in recent years into an excellent tool for the characterization of membrane proteins and their complexes. In the past few years it has also become clear that the structure of membrane proteins, especially helical membrane proteins is determined, in part, by the membrane environment. Therefore, the modeling of this environment by a liquid crystalline lipid bilayer for solid state NMR has generated a unique tool for the characterization of native conformational states, local and global dynamics, and high resolution structure for these proteins. Protein-protein interactions can also benefit from this solid state NMR capability to characterize membrane proteins in a native-like environment. These complexes take the form of oligomeric structures and hetero-protein interactions both with water soluble proteins and other membrane proteins. PMID:24034903

  9. Solid state NMR and protein-protein interactions in membranes.

    PubMed

    Miao, Yimin; Cross, Timothy A

    2013-12-01

    Solid state NMR spectroscopy has evolved rapidly in recent years into an excellent tool for the characterization of membrane proteins and their complexes. In the past few years it has also become clear that the structure of membrane proteins, especially helical membrane proteins is determined, in part, by the membrane environment. Therefore, the modeling of this environment by a liquid crystalline lipid bilayer for solid state NMR has generated a unique tool for the characterization of native conformational states, local and global dynamics, and high-resolution structure for these proteins. Protein-protein interactions can also benefit from this solid state NMR capability to characterize membrane proteins in a native-like environment. These complexes take the form of oligomeric structures and hetero-protein interactions both with water-soluble proteins and other membrane proteins. PMID:24034903

  10. [Chemical libraries dedicated to protein-protein interactions].

    PubMed

    Sperandio, Olivier; Villoutreix, Bruno O; Morelli, Xavier; Roche, Philippe

    2015-03-01

    The identification of complete networks of protein-protein interactions (PPI) within a cell has contributed to major breakthroughs in understanding biological pathways, host-pathogen interactions and cancer development. As a consequence, PPI have emerged as a new class of promising therapeutic targets. However, they are still considered as a challenging class of targets for drug discovery programs. Recent successes have allowed the characterization of structural and physicochemical properties of protein-protein interfaces leading to a better understanding of how they can be disrupted with small molecule compounds. In addition, characterization of the profiles of PPI inhibitors has allowed the development of PPI-focused libraries. In this review, we present the current efforts at developing chemical libraries dedicated to these innovative targets. PMID:25855285

  11. Protein-protein interactions and prediction: a comprehensive overview.

    PubMed

    Sowmya, Gopichandran; Ranganathan, Shoba

    2014-01-01

    Molecular function in cellular processes is governed by protein-protein interactions (PPIs) within biological networks. Selective yet specific association of these protein partners contributes to diverse functionality such as catalysis, regulation, assembly, immunity, and inhibition in a cell. Therefore, understanding the principles of protein-protein association has been of immense interest for several decades. We provide an overview of the experimental methods used to determine PPIs and the key databases archiving this information. Structural and functional information of existing protein complexes confers knowledge on the principles of PPI, based on which a classification scheme for PPIs is then introduced. Obtaining high-quality non-redundant datasets of protein complexes for interaction characterisation is an essential step towards deciphering their underlying binding principles. Analysis of physicochemical features and their documentation has enhanced our understanding of the molecular basis of protein-protein association. We describe the diverse datasets created/collected by various groups and their key findings inferring distinguishing features. The currently available interface databases and prediction servers have also been compiled. PMID:23855658

  12. Multilevel regulation of protein protein interactions in biological circuitry

    NASA Astrophysics Data System (ADS)

    Beckett, Dorothy

    2005-06-01

    Protein-protein interactions are central to biology and, in this 'post-genomic era', prediction of these interactions has become the goal of many computational efforts. Close inspection of even relatively simple biological regulatory circuitry reveals multiple levels of control of the contributing protein interactions. The fundamental probability that an interaction will occur under a given set of conditions is difficult to predict because the relationship between structure and energy is not known. Layered on this basic difficulty are allosteric control mechanisms involving post-translational modification or small ligand binding. In addition, many biological processes involve multiple protein-protein interactions, some of which may be cooperative or even competitive. Finally, although the emphasis in predicting protein interactions is based on equilibrium thermodynamic principles, kinetics can be a major controlling feature in these systems. This complexity reinforces the necessity of performing detailed quantitative studies of the component interactions of complex biological regulatory systems. Results of such studies will help us to bridge the gap between our knowledge of structure and our understanding of functional biology.

  13. A Microfluidic Platform for Characterization of Protein-Protein Interactions.

    PubMed

    Javanmard, Mehdi; Talasaz, Amirali H; Nemat-Gorgani, Mohsen; Huber, David E; Pease, Fabian; Ronaghi, Mostafa; Davis, Ronald W

    2009-08-01

    Traditionally, expensive and time consuming techniques such as mass spectrometry and Western Blotting have been used for characterization of protein-protein interactions. In this paper, we describe the design, fabrication, and testing of a rapid and inexpensive sensor, involving the use of microelectrodes in a microchannel, which can be used for real-time electrical detection of specific interactions between proteins. We have successfully demonstrated detection of target glycoprotein-glycoprotein interactions, antigen-antibody interactions, and glycoprotein-antigen interactions. We have also demonstrated the ability of this technique to distinguish between strong and weak interactions. Using this approach, it may be possible to multiplex an array of these sensors onto a chip and probe a complex mixture for various types of interactions involving protein molecules. PMID:20467571

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

    NASA Astrophysics Data System (ADS)

    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

  15. Methods for analyzing and quantifying protein-protein interaction.

    PubMed

    Syafrizayanti; Betzen, Christian; Hoheisel, Jörg D; Kastelic, Damjana

    2014-02-01

    Genome sequencing has led to the identification of many proteins, which had not been recognized before. In consequence, the basic set of human proteins is generally known. Far less information, however, exists about protein-protein interactions, which are required and responsible for cellular activities and their control. Many protein isoforms that result from mutations, splice-variations and post-translational modifications also come into play. Until recently, interactions of only few protein partners could be analyzed in a single experiment. However, this does not meet the challenge of investigating the highly complex interaction patterns in cellular systems. It is made even more demanding by the need to determine the intensity of interactions quantitatively in order to properly understand protein interplay. Currently available techniques vary with respect to accuracy, reliability, reproducibility and throughput and their performances range from a mere qualitative demonstration of binding to a quantitative characterization of affinities. In this article, an overview is given of the methodologies available for analysis of protein-protein interactions. PMID:24393018

  16. Protein-protein interactions in DNA mismatch repair.

    PubMed

    Friedhoff, Peter; Li, Pingping; Gotthardt, Julia

    2016-02-01

    The principal DNA mismatch repair proteins MutS and MutL are versatile enzymes that couple DNA mismatch or damage recognition to other cellular processes. Besides interaction with their DNA substrates this involves transient interactions with other proteins which is triggered by the DNA mismatch or damage and controlled by conformational changes. Both MutS and MutL proteins have ATPase activity, which adds another level to control their activity and interactions with DNA substrates and other proteins. Here we focus on the protein-protein interactions, protein interaction sites and the different levels of structural knowledge about the protein complexes formed with MutS and MutL during the mismatch repair reaction. PMID:26725162

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

    PubMed

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

    2016-06-14

    Graphene oxide (GO) is a promising novel nanomaterial with a wide range of potential biomedical applications due to its many intriguing properties. However, very little research has been conducted to study its possible adverse effects on protein-protein interactions (and thus subsequent toxicity to human). Here, the potential cytotoxicity of GO is investigated at molecular level using large-scale, all-atom molecular dynamics simulations to explore the interaction mechanism between a protein dimer and a GO nanosheet oxidized at different levels. Our theoretical results reveal that GO nanosheet could intercalate between the two monomers of HIV-1 integrase dimer, disrupting the protein-protein interactions and eventually lead to dimer disassociation as graphene does [B. Luan et al., ACS Nano 9(1), 663 (2015)], albeit its insertion process is slower when compared with graphene due to the additional steric and attractive interactions. This study helps to better understand the toxicity of GO to cell functions which could shed light on how to improve its biocompatibility and biosafety for its wide potential biomedical applications. PMID:27306022

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

    NASA Astrophysics Data System (ADS)

    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.

  19. Peptiderive server: derive peptide inhibitors from protein-protein interactions.

    PubMed

    Sedan, Yuval; Marcu, Orly; Lyskov, Sergey; Schueler-Furman, Ora

    2016-07-01

    The Rosetta Peptiderive protocol identifies, in a given structure of a protein-protein interaction, the linear polypeptide segment suggested to contribute most to binding energy. Interactions that feature a 'hot segment', a linear peptide with significant binding energy compared to that of the complex, may be amenable for inhibition and the peptide sequence and structure derived from the interaction provide a starting point for rational drug design. Here we present a web server for Peptiderive, which is incorporated within the ROSIE web interface for Rosetta protocols. A new feature of the protocol also evaluates whether derived peptides are good candidates for cyclization. Fast computation times and clear visualization allow users to quickly assess the interaction of interest. The Peptiderive server is available for free use at http://rosie.rosettacommons.org/peptiderive. PMID:27141963

  20. Protein-protein interactions in reversibly assembled nanopatterns.

    PubMed

    Rakickas, Tomas; Gavutis, Martynas; Reichel, Annett; Piehler, Jacob; Liedberg, Bo; Valiokas, Ramūnas

    2008-10-01

    We describe herein a platform to study protein-protein interactions and to form functional protein complexes in nanoscopic surface domains. For this purpose, we employed multivalent chelator (MCh) templates, which were fabricated in a stepwise procedure combining dip-pen nanolithography (DPN) and molecular recognition-directed assembly. First, we demonstrated that an atomic force microscope (AFM) tip inked with an oligo(ethylene glycol) (OEG) disulfide compound bearing terminal biotin groups can be used to generate biotin patterns on gold achieving line widths below 100 nm, a generic platform for fabrication of functional nanostructures via the highly specific biotin-streptavidin recognition. Subsequently, we converted such biotin/streptavidin patterns into functional MCh patterns for reversible assembly of histidine-tagged (His-tagged) proteins via the attachment of a tris-nitriloacetic acid (trisNTA) biotin derivative. Fluorescence microscopy confirmed reversible immobilization of the receptor subunit ifnar2-His10 and its interaction with interferon-alpha2 labeled with fluorescent quantum dots in a 7 x 7 dot array consisting of trisNTA spots with a diameter of approximately 230 nm. Moreover, we carried out characterization of the specificity, stability, and reversibility as well as quantitative real-time analysis of protein-protein interactions at the fabricated nanopatterns by imaging surface plasmon resonance. Our work offers a route for construction and analysis of functional protein-based nanoarchitectures. PMID:18788824

  1. Use of protein-protein interactions in affinity chromatography.

    PubMed

    Muronetz, V I; Sholukh, M; Korpela, T

    2001-10-30

    Biospecific recognition between proteins is a phenomenon that can be exploited for designing affinity-chromatographic purification systems for proteins. In principle, the approach is straightforward, and there are usually many alternative ways, since a protein can be always found which binds specifically enough to the desired protein. Routine immunoaffinity chromatography utilizes the recognition of antigenic epitopes by antibodies. However, forces involved in protein-protein interactions as well the forces keeping the three-dimensional structures of proteins intact are complicated, and proteins are easily unfolded by various factors with unpredictable results. Because of this and because of the generally high association strength between proteins, the correct adjustment of binding forces between an immobilized protein and the protein to be purified as well as the release of bound proteins in biologically active form from affinity complexes are the main problem. Affinity systems involving interactions like enzyme-enzyme, subunit-oligomer, protein-antibody, protein-chaperone and the specific features involved in each case are presented as examples. This article also aims to sketch prospects for further development of the use of protein-protein interactions for the purification of proteins. PMID:11694271

  2. Novel computational methods to design protein-protein interactions

    NASA Astrophysics Data System (ADS)

    Zhou, Alice Qinhua; O'Hern, Corey; Regan, Lynne

    2014-03-01

    Despite the abundance of structural data, we still cannot accurately predict the structural and energetic changes resulting from mutations at protein interfaces. The inadequacy of current computational approaches to the analysis and design of protein-protein interactions has hampered the development of novel therapeutic and diagnostic agents. In this work, we apply a simple physical model that includes only a minimal set of geometrical constraints, excluded volume, and attractive van der Waals interactions to 1) rank the binding affinity of mutants of tetratricopeptide repeat proteins with their cognate peptides, 2) rank the energetics of binding of small designed proteins to the hydrophobic stem region of the influenza hemagglutinin protein, and 3) predict the stability of T4 lysozyme and staphylococcal nuclease mutants. This work will not only lead to a fundamental understanding of protein-protein interactions, but also to the development of efficient computational methods to rationally design protein interfaces with tunable specificity and affinity, and numerous applications in biomedicine. NSF DMR-1006537, PHY-1019147, Raymond and Beverly Sackler Institute for Biological, Physical and Engineering Sciences, and Howard Hughes Medical Institute.

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

    PubMed Central

    Sun, Zheng; Xiang, Jianhai

    2014-01-01

    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. PMID:24982879

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

    PubMed Central

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

    2016-01-01

    Aim: Evaluation of biological characteristics of 13 identified proteins of patients with cirrhotic liver disease is the main aim of this research. Background: In clinical usage, liver biopsy remains the gold standard for diagnosis of hepatic fibrosis. Evaluation and confirmation of liver fibrosis stages and severity of chronic diseases require a precise and noninvasive biomarkers. Since the early detection of cirrhosis is a clinical problem, achieving a sensitive, specific and predictive novel method based on biomarkers is an important task. Methods: Essential analysis, such as gene ontology (GO) enrichment and protein-protein interactions (PPI) was undergone EXPASy, STRING Database and DAVID Bioinformatics Resources query. Results: Based on GO analysis, most of proteins are located in the endoplasmic reticulum lumen, intracellular organelle lumen, membrane-enclosed lumen, and extracellular region. The relevant molecular functions are actin binding, metal ion binding, cation binding and ion binding. Cell adhesion, biological adhesion, cellular amino acid derivative, metabolic process and homeostatic process are the related processes. Protein-protein interaction network analysis introduced five proteins (fibroblast growth factor receptor 4, tropomyosin 4, tropomyosin 2 (beta), lectin, Lectin galactoside-binding soluble 3 binding protein and apolipoprotein A-I) as hub and bottleneck proteins. Conclusion: Our result indicates that regulation of lipid metabolism and cell survival are important biological processes involved in cirrhosis disease. More investigation of above mentioned proteins will provide a better understanding of cirrhosis disease. PMID:27099671

  5. Prediction and redesign of protein-protein interactions.

    PubMed

    Lua, Rhonald C; Marciano, David C; Katsonis, Panagiotis; Adikesavan, Anbu K; Wilkins, Angela D; Lichtarge, Olivier

    2014-01-01

    Understanding the molecular basis of protein function remains a central goal of biology, with the hope to elucidate the role of human genes in health and in disease, and to rationally design therapies through targeted molecular perturbations. We review here some of the computational techniques and resources available for characterizing a critical aspect of protein function - those mediated by protein-protein interactions (PPI). We describe several applications and recent successes of the Evolutionary Trace (ET) in identifying molecular events and shapes that underlie protein function and specificity in both eukaryotes and prokaryotes. ET is a part of analytical approaches based on the successes and failures of evolution that enable the rational control of PPI. PMID:24878423

  6. A Method for Predicting Protein-Protein Interaction Types

    PubMed Central

    Silberberg, Yael

    2014-01-01

    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. PMID:24625764

  7. Studying protein-protein interactions: progress, pitfalls and solutions.

    PubMed

    Hayes, Sheri; Malacrida, Beatrice; Kiely, Maeve; Kiely, Patrick A

    2016-08-15

    Signalling proteins are intrinsic to all biological processes and interact with each other in tightly regulated and orchestrated signalling complexes and pathways. Characterization of protein binding can help to elucidate protein function within signalling pathways. This information is vital for researchers to gain a more comprehensive knowledge of cellular networks which can then be used to develop new therapeutic strategies for disease. However, studying protein-protein interactions (PPIs) can be challenging as the interactions can be extremely transient downstream of specific environmental cues. There are many powerful techniques currently available to identify and confirm PPIs. Choosing the most appropriate range of techniques merits serious consideration. The aim of this review is to provide a starting point for researchers embarking on a PPI study. We provide an overview and point of reference for some of the many methods available to identify interactions from in silico analysis and large scale screening tools through to the methods used to validate potential PPIs. We discuss the advantages and disadvantages of each method and we also provide a workflow chart to highlight the main experimental questions to consider when planning cell lysis to maximize experimental success. PMID:27528744

  8. Interface-Resolved Network of Protein-Protein Interactions

    PubMed Central

    Johnson, Margaret E.; Hummer, Gerhard

    2013-01-01

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

  9. Schizophrenia interactome with 504 novel protein-protein interactions.

    PubMed

    Ganapathiraju, Madhavi K; Thahir, Mohamed; Handen, Adam; Sarkar, Saumendra N; Sweet, Robert A; Nimgaonkar, Vishwajit L; Loscher, Christine E; Bauer, Eileen M; Chaparala, Srilakshmi

    2016-01-01

    Genome-wide association studies of schizophrenia (GWAS) have revealed the role of rare and common genetic variants, but the functional effects of the risk variants remain to be understood. Protein interactome-based studies can facilitate the study of molecular mechanisms by which the risk genes relate to schizophrenia (SZ) genesis, but protein-protein interactions (PPIs) are unknown for many of the liability genes. We developed a computational model to discover PPIs, which is found to be highly accurate according to computational evaluations and experimental validations of selected PPIs. We present here, 365 novel PPIs of liability genes identified by the SZ Working Group of the Psychiatric Genomics Consortium (PGC). Seventeen genes that had no previously known interactions have 57 novel interactions by our method. Among the new interactors are 19 drug targets that are targeted by 130 drugs. In addition, we computed 147 novel PPIs of 25 candidate genes investigated in the pre-GWAS era. While there is little overlap between the GWAS genes and the pre-GWAS genes, the interactomes reveal that they largely belong to the same pathways, thus reconciling the apparent disparities between the GWAS and prior gene association studies. The interactome including 504 novel PPIs overall, could motivate other systems biology studies and trials with repurposed drugs. The PPIs are made available on a webserver, called Schizo-Pi at http://severus.dbmi.pitt.edu/schizo-pi with advanced search capabilities. PMID:27336055

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

    PubMed

    Corbi-Verge, Carles; Kim, Philip M

    2016-01-01

    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. PMID:26936767

  11. Modulation of opioid receptor function by protein-protein interactions.

    PubMed

    Alfaras-Melainis, Konstantinos; Gomes, Ivone; Rozenfeld, Raphael; Zachariou, Venetia; Devi, Lakshmi

    2009-01-01

    Opioid receptors, MORP, DORP and KORP, belong to the family A of G protein coupled receptors (GPCR), and have been found to modulate a large number of physiological functions, including mood, stress, appetite, nociception and immune responses. Exogenously applied opioid alkaloids produce analgesia, hedonia and addiction. Addiction is linked to alterations in function and responsiveness of all three opioid receptors in the brain. Over the last few years, a large number of studies identified protein-protein interactions that play an essential role in opioid receptor function and responsiveness. Here, we summarize interactions shown to affect receptor biogenesis and trafficking, as well as those affecting signal transduction events following receptor activation. This article also examines protein interactions modulating the rate of receptor endocytosis and degradation, events that play a major role in opiate analgesia. Like several other GPCRs, opioid receptors may form homo or heterodimers. The last part of this review summarizes recent knowledge on proteins known to affect opioid receptor dimerization. PMID:19273296

  12. Predicting protein-protein interactions based only on sequences information.

    PubMed

    Shen, Juwen; Zhang, Jian; Luo, Xiaomin; Zhu, Weiliang; Yu, Kunqian; Chen, Kaixian; Li, Yixue; Jiang, Hualiang

    2007-03-13

    Protein-protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method. PMID:17360525

  13. Inferring high-confidence human protein-protein interactions

    PubMed Central

    2012-01-01

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

  14. Algorithmic approaches to protein-protein interaction site prediction.

    PubMed

    Aumentado-Armstrong, Tristan T; Istrate, Bogdan; Murgita, Robert A

    2015-01-01

    Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented. PMID:25713596

  15. Deciphering Supramolecular Structures with Protein-Protein Interaction Network Modeling

    PubMed Central

    Tsuji, Toshiyuki; Yoda, Takao; Shirai, Tsuyoshi

    2015-01-01

    Many biological molecules are assembled into supramolecules that are essential to perform complicated functions in the cell. However, experimental information about the structures of supramolecules is not sufficient at this point. We developed a method of predicting and modeling the structures of supramolecules in a biological network by combining structural data of the Protein Data Bank (PDB) and interaction data in IntAct databases. Templates for binary complexes in IntAct were extracted from PDB. Modeling was attempted by assembling binary complexes with superposed shared subunits. A total of 3,197 models were constructed, and 1,306 (41% of the total) contained at least one subunit absent from experimental structures. The models also suggested 970 (25% of the total) experimentally undetected subunit interfaces, and 41 human disease-related amino acid variants were mapped onto these model-suggested interfaces. The models demonstrated that protein-protein interaction network modeling is useful to fill the information gap between biological networks and structures. PMID:26549015

  16. Targeting protein-protein interactions as an anticancer strategy

    PubMed Central

    Ivanov, Andrei A.; Khuri, Fadlo R.; Fu, Haian

    2013-01-01

    The emergence and convergence of cancer genomics, targeted therapies, and network oncology have significantly expanded the landscape of protein-protein interaction (PPI) networks in cancer for therapeutic discovery. Extensive biological and clinical investigations have led to the identification of protein interaction hubs and nodes that are critical for the acquisition and maintaining characteristics of cancer essential for cell transformation. Such cancer enabling PPIs have become promising therapeutic targets. With technological advances in PPI modulator discovery and validation of PPI-targeting agents in clinical settings, targeting PPI interfaces as an anticancer strategy has become a reality. Future research directed at genomics-based PPI target discovery, PPI interface characterization, PPI-focused chemical library design, and patient-genomic subpopulation-driven clinical studies is expected to accelerate the development of the next generation of PPI-based anticancer agents for personalized precision medicine. Here we briefly review prominent PPIs that mediate cancer-acquired properties, highlight recognized challenges and promising clinical results in targeting PPIs, and outline emerging opportunities. PMID:23725674

  17. Parallel Force Assay for Protein-Protein Interactions

    PubMed Central

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

    2014-01-01

    Quantitative proteome research is greatly promoted by high-resolution parallel format assays. A characterization of protein complexes based on binding forces offers an unparalleled dynamic range and allows for the effective discrimination of non-specific interactions. Here we present a DNA-based Molecular Force Assay to quantify protein-protein interactions, namely the bond between different variants of GFP and GFP-binding nanobodies. We present different strategies to adjust the maximum sensitivity window of the assay by influencing the binding strength of the DNA reference duplexes. The binding of the nanobody Enhancer to the different GFP constructs is compared at high sensitivity of the assay. Whereas the binding strength to wild type and enhanced GFP are equal within experimental error, stronger binding to superfolder GFP is observed. This difference in binding strength is attributed to alterations in the amino acids that form contacts according to the crystal structure of the initial wild type GFP-Enhancer complex. Moreover, we outline the potential for large-scale parallelization of the assay. PMID:25546146

  18. From Topology to Phenotype in Protein-Protein Interaction Networks

    NASA Astrophysics Data System (ADS)

    Pržulj, Nataša

    We have recently witnessed an explosion in biological network data along with the development of computational approaches for their analyses. This new interdisciplinary research area is an integral part of systems biology, promising to provide new insights into organizational principles of life, as well as into evolution and disease. However, there is a danger that the area might become hindered by several emerging issues. In particular, there is typically a weak link between biological and computational scientists, resulting in the use of simple computational techniques of limited potential to explain these complex biological data. Hence, there is a danger that the community might view the topological features of network data as mere statistics, ignoring the value of the information contained in these data. This might result in the imposition of scientific doctrines, such as scale-free-centric (on the modelling side) and genome-centric (on the biological side) opinions onto this nascent research area. In this chapter, we take a network science perspective and present a brief, high-level overview of the area, commenting on possible challenges ahead. We focus on protein-protein interaction networks (PINs) in which nodes correspond to proteins in a cell and edges to physical bindings between the proteins.

  19. Targeting Protein-Protein Interactions for Parasite Control

    PubMed Central

    Taylor, Christina M.; Fischer, Kerstin; Abubucker, Sahar; Wang, Zhengyuan; Martin, John; Jiang, Daojun; Magliano, Marc; Rosso, Marie-Noëlle; Li, Ben-Wen; Fischer, Peter U.; Mitreva, Makedonka

    2011-01-01

    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 ortholgous 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 applicable. PMID

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

    ERIC Educational Resources Information Center

    Witherow, D. Scott; Carson, Sue

    2011-01-01

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

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

    PubMed Central

    Khor, Susan

    2014-01-01

    Identifying reliable domain-domain interactions will increase our ability to predict novel protein-protein interactions, to unravel interactions in protein complexes, and thus gain more information about the function and behavior of genes. One of the challenges of identifying reliable domain-domain interactions is domain promiscuity. Promiscuous domains are domains that can occur in many domain architectures and are therefore found in many proteins. This becomes a problem for a method where the score of a domain-pair is the ratio between observed and expected frequencies because the protein-protein interaction network is sparse. As such, many protein-pairs will be non-interacting and domain-pairs with promiscuous domains will be penalized. This domain promiscuity challenge to the problem of inferring reliable domain-domain interactions from protein-protein interactions has been recognized, and a number of work-arounds have been proposed. This paper reports on an application of Formal Concept Analysis to this problem. It is found that the relationship between formal concepts provides a natural way for rare domains to elevate the rank of promiscuous domain-pairs and enrich highly ranked domain-pairs with reliable domain-domain interactions. This piggybacking of promiscuous domain-pairs onto less promiscuous domain-pairs is possible only with concept lattices whose attribute-labels are not reduced and is enhanced by the presence of proteins that comprise both promiscuous and rare domains. PMID:24586450

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

    PubMed

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

    2015-01-01

    Protein-protein docking aims at predicting the three-dimensional structure of a protein complex starting from the free forms of the individual partners. As assessed in the CAPRI community-wide experiment, the most successful docking algorithms combine pure laws of physics with information derived from various experimental or bioinformatics sources. Of these so-called "information-driven" approaches, HADDOCK stands out as one of the most successful representatives. In this chapter, we briefly summarize which experimental information can be used to drive the docking prediction in HADDOCK, and then focus on the docking protocol itself. We discuss and illustrate with a tutorial example a "classical" protein-protein docking prediction, as well as more recent developments for modelling multi-body systems and large conformational changes. PMID:25330973

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

    SciTech Connect

    Zhou, C; Zemla, A

    2009-02-25

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

  4. Template-based structure modeling of protein-protein interactions

    PubMed Central

    Szilagyi, Andras; Zhang, Yang

    2014-01-01

    The structure of protein-protein complexes can be constructed by using the known structure of other protein complexes as a template. The complex structure templates are generally detected either by homology-based sequence alignments or, given the structure of monomer components, by structure-based comparisons. Critical improvements have been made in recent years by utilizing interface recognition and by recombining monomer and complex template libraries. Encouraging progress has also been witnessed in genome-wide applications of template-based modeling, with modeling accuracy comparable to high-throughput experimental data. Nevertheless, bottlenecks exist due to the incompleteness of the proteinprotein complex structure library and the lack of methods for distant homologous template identification and full-length complex structure refinement. PMID:24721449

  5. Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks

    PubMed Central

    Shen, Ru; Wang, Xiaosheng; Guda, Chittibabu

    2015-01-01

    Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein-protein interaction networks carry vital information about molecular interactions in cellular systems, and identification of functional modules (subgraphs) in these networks is one of the most important applications of biological network analysis. Results. In this study, we developed a new graph theory based method to identify distinct functional modules from nine different cancer protein-protein interaction networks. The method is composed of three major steps: (i) extracting modules from protein-protein interaction networks using network clustering algorithms; (ii) identifying distinct subgraphs from the derived modules; and (iii) identifying distinct subgraph patterns from distinct subgraphs. The subgraph patterns were evaluated using experimentally determined cancer-specific protein-protein interaction data from the Ingenuity knowledgebase, to identify distinct functional modules that are specific to each cancer type. Conclusion. We identified cancer-type specific subgraph patterns that may represent the functional modules involved in the molecular pathogenesis of different cancer types. Our method can serve as an effective tool to discover cancer-type specific functional modules from large protein-protein interaction networks. PMID:26495282

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

    SciTech Connect

    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.

  7. Protein-Protein Interaction for the De Novo Design of Cyclin-Dependent Kinase Peptide Inhibitors.

    PubMed

    Arumugasamy, Karthiga; Tripathi, Sunil Kumar; Singh, Poonam; Singh, Sanjeev Kumar

    2016-01-01

    The homology of the inhibitor binding site regions on the surface of cyclin-dependent kinases (CDKs) makes actual CDK inhibitors unable to bind specifically to their molecular targets. Most of them are ATP competitive inhibitors with low specificity that also affect the phosphorylation mechanisms of other nontarget kinases giving rise to harmful side effects. So, the search of specific and potent inhibitors able to bind to the desired CDK target is still a pending issue. Structure based drug design minimized the erroneous binding and increased the affinity of the inhibitor interaction. In the case of CDKs their activation and regulation mechanisms mainly depend on protein-protein interactions (PPIs). The design of drugs targeting these PPIs makes feasible and promising towards the discovery of new and specific CDK inhibitors. Development of peptide inhibitors for a target protein is an emerging approach in computer aided drug designing. This chapter describes in detail methodology for use of the VitAL-Viterbi algorithm for de novo peptide design of CDK2 inhibitors. PMID:26231708

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

    PubMed Central

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

    2015-01-01

    A common approach for exploring the interactome, the network of protein-protein interactions in cells, uses a commercially available ORF library to express affinity tagged bait proteins; these can be expressed in cells and endogenous cellular proteins that copurify with the bait can be identified as putative interacting proteins using mass spectrometry. Control experiments can be used to limit false-positive results, but in many cases, there are still a surprising number of prey proteins that appear to copurify specifically with the bait. Here, we have identified one source of false-positive interactions in such studies. We have found that a combination of: 1) the variable sequence of the C-terminus of the bait with 2) a C-terminal valine “cloning scar” present in a commercially available ORF library, can in some cases create a peptide motif that results in the aberrant co-purification of endogenous cellular proteins. Control experiments may not identify false positives resulting from such artificial motifs, as aberrant binding depends on sequences that vary from one bait to another. It is possible that such cryptic protein binding might occur in other systems using affinity tagged proteins; this study highlights the importance of conducting careful follow-up studies where novel protein-protein interactions are suspected. PMID:25704442

  9. Protein-protein interactions of PDE4 family members - Functions, interactions and therapeutic value.

    PubMed

    Klussmann, Enno

    2016-07-01

    The second messenger cyclic adenosine monophosphate (cAMP) is ubiquitous and directs a plethora of functions in all cells. Although theoretically freely diffusible through the cell from the site of its synthesis it is not evenly distributed. It rather is shaped into gradients and these gradients are established by phospodiesterases (PDEs), the only enzymes that hydrolyse cAMP and thereby terminate cAMP signalling upstream of cAMP's effector systems. Miles D. Houslay has devoted most of his scientific life highly successfully to a particular family of PDEs, the PDE4 family. The family is encoded by four genes and gives rise to around 20 enzymes, all with different functions. M. Houslay has discovered many of these functions and realised early on that PDE4 family enzymes are attractive drug targets in a variety of human diseases, but not their catalytic activity as that is encoded in conserved domains in all family members. He postulated that targeting the intracellular location would provide the specificity that modern innovative drugs require to improve disease conditions with fewer side effects than conventional drugs. Due to the wealth of M. Houslay's work, this article can only summarize some of his discoveries and, therefore, focuses on protein-protein interactions of PDE4. The aim is to discuss functions of selected protein-protein interactions and peptide spot technology, which M. Houslay introduced into the PDE4 field for identifying interacting domains. The therapeutic potential of PDE4 interactions will also be discussed. PMID:26498857

  10. Imaging Protein Protein Interactions inside Living Cells via Interaction-Dependent Fluorophore Ligation

    PubMed Central

    Slavoff, Sarah A.; Liu, Daniel S.; Cohen, Justin D.; Ting, Alice Y.

    2012-01-01

    We report a new method, Interaction-Dependent PRobe Incorporation Mediated by Enzymes, or ID-PRIME, for imaging protein protein interactions (PPIs) inside living cells. ID-PRIME utilizes a mutant of Escherichia coli lipoic acid ligase, LplAW37V, which can catalyze the covalent ligation of a coumarin fluorophore onto a peptide recognition sequence called LAP1. The affinity between the ligase and LAP1 is tuned such that, when each is fused to a protein partner of interest, LplAW37V labels LAP1 with coumarin only when the protein partners to which they are fused bring them together. Coumarin labeling in the absence of such interaction is low or undetectable. Characterization of ID-PRIME in living mammalian cells shows that multiple protein protein interactions can be imaged (FRB FKBP, Fos Jun, and neuroligin PSD-95), with as little as 10 min of coumarin treatment. The signal intensity and detection sensitivity are similar to those of the widely used fluorescent protein complementation technique (BiFC) for PPI detection, without the disadvantage of irreversible complex trapping. ID-PRIME provides a powerful and complementary approach to existing methods for visualization of PPIs in living cells with spatial and temporal resolution. PMID:22098454

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

    PubMed Central

    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. PMID:27516733

  12. Protein-protein interactions of mitochondrial-associated protein via bioluminescence resonance energy transfer

    PubMed Central

    Koshiba, Takumi

    2015-01-01

    Protein-protein interactions are essential biological reactions occurring at inter- and intra-cellular levels. The analysis of their mechanism is generally required in order link to understand their various cellular functions. Bioluminescence resonance energy transfer (BRET), which is based on an enzymatic activity of luciferase, is a useful tool for investigating protein-protein interactions in live cells. The combination of the BRET system and biomolecular fluorescence complementation (BiFC) would provide us a better understanding of the hetero-oligomeric structural states of protein complexes. In this review, we discuss the application of BRET to the protein-protein interactions of mitochondrial-associated proteins and discuss its physiological relevance. PMID:27493852

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

    PubMed

    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. PMID:27516733

  14. A novel method for protein-protein interaction site prediction using phylogenetic substitution models

    PubMed Central

    La, David; Kihara, Daisuke

    2011-01-01

    Protein-protein binding events mediate many critical biological functions in the cell. Typically, functionally important sites in proteins can be well identified by considering sequence conservation. However, protein-protein interaction sites exhibit higher sequence variation than other functional regions, such as catalytic sites of enzymes. Consequently, the mutational behavior leading to weak sequence conservation poses significant challenges to the protein-protein interaction site prediction. Here, we present a phylogenetic framework to capture critical sequence variations that favor the selection of residues essential for protein-protein binding. Through the comprehensive analysis of diverse protein families, we show that protein binding interfaces exhibit distinct amino acid substitution as compared with other surface residues. Based on this analysis, we have developed a novel method, BindML, which utilizes the substitution models to predict protein-protein binding sites of protein with unknown interacting partners. BindML estimates the likelihood that a phylogenetic tree of a local surface region in a query protein structure follows the substitution patterns of protein binding interface and non-binding surfaces. BindML is shown to perform well compared to alternative methods for protein binding interface prediction. The methodology developed in this study is very versatile in the sense that it can be generally applied for predicting other types of functional sites, such as DNA, RNA, and membrane binding sites in proteins. PMID:21989996

  15. Domains mediate protein-protein interactions and nucleate protein assemblies.

    PubMed

    Costa, S; Cesareni, G

    2008-01-01

    Cell physiology is governed by an intricate mesh of physical and functional links among proteins, nucleic acids and other metabolites. The recent information flood coming from large-scale genomic and proteomic approaches allows us to foresee the possibility of compiling an exhaustive list of the molecules present within a cell, enriched with quantitative information on concentration and cellular localization. Moreover, several high-throughput experimental and computational techniques have been devised to map all the protein interactions occurring in a living cell. So far, such maps have been drawn as graphs where nodes represent proteins and edges represent interactions. However, this representation does not take into account the intrinsically modular nature of proteins and thus fails in providing an effective description of the determinants of binding. Since proteins are composed of domains that often confer on proteins their binding capabilities, a more informative description of the interaction network would detail, for each pair of interacting proteins in the network, which domains mediate the binding. Understanding how protein domains combine to mediate protein interactions would allow one to add important features to the protein interaction network, making it possible to discriminate between simultaneously occurring and mutually exclusive interactions. This objective can be achieved by experimentally characterizing domain recognition specificity or by analyzing the frequency of co-occurring domains in proteins that do interact. Such approaches allow gaining insights on the topology of complexes with unknown three-dimensional structure, thus opening the prospect of adopting a more rational strategy in developing drugs designed to selectively target specific protein interactions. PMID:18491061

  16. Design of Protein-Protein Interactions with a Novel Ensemble-Based Scoring Algorithm

    NASA Astrophysics Data System (ADS)

    Roberts, Kyle E.; Cushing, Patrick R.; Boisguerin, Prisca; Madden, Dean R.; Donald, Bruce R.

    Protein-protein interactions (PPIs) are vital for cell signaling, protein trafficking and localization, gene expression, and many other biological functions. Rational modification of PPI targets provides a mechanism to understand their function and importance. However, PPI systems often have many more degrees of freedom and flexibility than the small-molecule binding sites typically targeted by protein design algorithms. To handle these challenging design systems, we have built upon the computational protein design algorithm K * [8,19] to develop a new design algorithm to study protein-protein and protein-peptide interactions. We validated our algorithm through the design and experimental testing of novel peptide inhibitors.

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

    PubMed Central

    2010-01-01

    Background 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. Results 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. Conclusion 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. PMID:20122195

  18. Choosing negative examples for the prediction of protein-protein interactions

    PubMed Central

    Ben-Hur, Asa; Noble, William Stafford

    2006-01-01

    The protein-protein interaction networks of even well-studied model organisms are sketchy at best, highlighting the continued need for computational methods to help direct experimentalists in the search for novel interactions. This need has prompted the development of a number of methods for predicting protein-protein interactions based on various sources of data and methodologies. The common method for choosing negative examples for training a predictor of protein-protein interactions is based on annotations of cellular localization, and the observation that pairs of proteins that have different localization patterns are unlikely to interact. While this method leads to high quality sets of non-interacting proteins, we find that this choice can lead to biased estimates of prediction accuracy, because the constraints placed on the distribution of the negative examples makes the task easier. The effects of this bias are demonstrated in the context of both sequence-based and non-sequence based features used for predicting protein-protein interactions. PMID:16723005

  19. Protein-protein interaction networks (PPI) and complex diseases

    PubMed Central

    Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Rezaei-Tavirani, Mostafa; Goliaei, Bahram

    2014-01-01

    The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network. PMID:25436094

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

    PubMed Central

    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 transcription of target genes. Whether the formation of functional tetramers is a widespread property of plant MADS domain proteins, or it is specific to few of these transcriptional regulators remains unclear. Results We analyzed the structure of the network of physical interactions among MADS domain proteins in Arabidopsis thaliana. We determined the abundance of subgraphs that represent the connection pattern expected for a MADS domain protein heterotetramer. These subgraphs were significantly more abundant in the MADS domain protein interaction network than in randomized analogous networks. Importantly, these subgraphs are not significantly frequent in a protein interaction network of TCP plant transcription factors, when compared to expectation by chance. In addition, we found that MADS domain proteins in tetramer-like subgraphs are more likely to be expressed jointly than proteins in other subgraphs. This effect is mainly due to proteins in the monophyletic MIKC clade, as there is no association between tetramer-like subgraphs and co-expression for proteins outside this clade. Conclusions Our results support that the tendency to form functional tetramers is widespread in the MADS domain protein-protein interaction network. Our observations also suggest that this trend is prevalent, or perhaps exclusive, for proteins in the MIKC clade. Because it is possible to retrodict several experimental results from our analyses, our work can be an important aid to make new predictions and facilitates experimental research on plant MADS domain proteins. PMID:24468197

  1. Evolving new protein-protein interaction specificity through promiscuous intermediates.

    PubMed

    Aakre, Christopher D; Herrou, Julien; Phung, Tuyen N; Perchuk, Barrett S; Crosson, Sean; Laub, Michael T

    2015-10-22

    Interacting proteins typically coevolve, and the identification of coevolving amino acids can pinpoint residues required for interaction specificity. This approach often assumes that an interface-disrupting mutation in one protein drives selection of a compensatory mutation in its partner during evolution. However, this model requires a non-functional intermediate state prior to the compensatory change. Alternatively, a mutation in one protein could first broaden its specificity, allowing changes in its partner, followed by a specificity-restricting mutation. Using bacterial toxin-antitoxin systems, we demonstrate the plausibility of this second, promiscuity-based model. By screening large libraries of interface mutants, we show that toxins and antitoxins with high specificity are frequently connected in sequence space to more promiscuous variants that can serve as intermediates during a reprogramming of interaction specificity. We propose that the abundance of promiscuous variants promotes the expansion and diversification of toxin-antitoxin systems and other paralogous protein families during evolution. PMID:26478181

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

    DOEpatents

    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.

  3. 4,6-Diphenylpyridines as Promising Novel Anti-Influenza Agents Targeting the PA-PB1 Protein-Protein Interaction: Structure-Activity Relationships Exploration with the Aid of Molecular Modeling.

    PubMed

    Trist, Iuni M L; Nannetti, Giulio; Tintori, Cristina; Fallacara, Anna Lucia; Deodato, Davide; Mercorelli, Beatrice; Palù, Giorgio; Wijtmans, Maikel; Gospodova, Tzveta; Edink, Ewald; Verheij, Mark; de Esch, Iwan; Viteva, Lilia; Loregian, Arianna; Botta, Maurizio

    2016-03-24

    Influenza is an infectious disease that represents an important public health burden, with high impact on the global morbidity, mortality, and economy. The poor protection and the need of annual updating of the anti-influenza vaccine, added to the rapid emergence of viral strains resistant to current therapy make the need for antiviral drugs with novel mechanisms of action compelling. In this regard, the viral RNA polymerase is an attractive target that allows the design of selective compounds with reduced risk of resistance. In previous studies we showed that the inhibition of the polymerase acidic protein-basic protein 1 (PA-PB1) interaction is a promising strategy for the development of anti-influenza agents. Starting from the previously identified 3-cyano-4,6-diphenyl-pyridines, we chemically modified this scaffold and explored its structure-activity relationships. Noncytotoxic compounds with both the ability of disrupting the PA-PB1 interaction and antiviral activity were identified, and their mechanism of target binding was clarified with molecular modeling simulations. PMID:26924568

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

    SciTech Connect

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

    2009-12-01

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

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

    PubMed

    Zhang, Shu-Bo; Tang, Qiang-Rong

    2016-07-21

    Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein-protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein-protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein-protein interaction. PMID:27117309

  6. The Arabidopsis ESCRT protein-protein interaction network.

    PubMed

    Shahriari, Mojgan; Richter, Klaus; Keshavaiah, Channa; Sabovljevic, Aneta; Huelskamp, Martin; Schellmann, Swen

    2011-05-01

    In yeast, endosomal sorting of monoubiquitylated transmembrane proteins is performed by a subset of the 19 "class E vacuolar protein sorting" proteins. The core machinery consists of 11 proteins that are organised in three complexes termed ESCRT I-III (endosomal sorting complex required for transport I-III) and is conserved in eukaryotic cells. While the pathway is well understood in yeast and animals, the plant ESCRT system is largely unexplored. At least one sequence homolog for each ESCRT component can be found in the Arabidopsis genome. Generally, sequence conservation between yeast/animals and the Arabidopsis proteins is low. To understand details about participating proteins and complex organization we have performed a systematic pairwise yeast two hybrid analysis of all Arabidopsis proteins showing homology to the ESCRT core machinery. Positive interactions were validated using bimolecular fluorescence complementation. In our experiments, most putative ESCRT components exhibited interactions with other ESCRT components that could be shown to occur on endosomes suggesting that despite their low homology to their yeast and animal counterparts they represent functional components of the plant ESCRT pathway. PMID:21442383

  7. Predicting Protein-Protein Interactions from the Molecular to the Proteome Level.

    PubMed

    Keskin, Ozlem; Tuncbag, Nurcan; Gursoy, Attila

    2016-04-27

    Identification of protein-protein interactions (PPIs) is at the center of molecular biology considering the unquestionable role of proteins in cells. Combinatorial interactions result in a repertoire of multiple functions; hence, knowledge of PPI and binding regions naturally serve to functional proteomics and drug discovery. Given experimental limitations to find all interactions in a proteome, computational prediction/modeling of protein interactions is a prerequisite to proceed on the way to complete interactions at the proteome level. This review aims to provide a background on PPIs and their types. Computational methods for PPI predictions can use a variety of biological data including sequence-, evolution-, expression-, and structure-based data. Physical and statistical modeling are commonly used to integrate these data and infer PPI predictions. We review and list the state-of-the-art methods, servers, databases, and tools for protein-protein interaction prediction. PMID:27074302

  8. GWIDD: a comprehensive resource for genome-wide structural modeling of protein-protein interactions

    PubMed Central

    2012-01-01

    Protein-protein interactions are a key component of life processes. The knowledge of the three-dimensional structure of these interactions is important for understanding protein function. Genome-Wide Docking Database (http://gwidd.bioinformatics.ku.edu) offers an extensive source of data for structural studies of protein-protein complexes on genome scale. The current release of the database combines the available experimental data on the structure and characteristics of protein interactions with structural modeling of protein complexes for 771 organisms spanned over the entire universe of life from viruses to humans. The interactions are stored in a relational database with user-friendly interface that includes various search options. The search results can be interactively previewed; the structures, downloaded, along with the interaction characteristics. PMID:23245398

  9. Intricate protein-protein interactions in the cyanobacterial circadian clock.

    PubMed

    Egli, Martin

    2014-08-01

    The cyanobacterial circadian clock consists of a post-translational oscillator (PTO) and a PTO-dependent transcription-translation feedback loop (TTFL). The PTO can be reconstituted in vitro with the KaiA, KaiB, and KaiC proteins, enabling detailed biochemical and biophysical investigations. Both the CI and the CII halves of the KaiC hexamer harbor ATPases, but only the C-terminal CII ring exhibits kinase and phospho-transferase activities. KaiA stimulates the kinase and KaiB associates with KaiC during the dephosphorylation phase and sequesters KaiA. Recent research has led to conflicting models of the KaiB-KaiC interaction, precluding a clear understanding of KaiB function and KaiABC clock mechanism. PMID:24936066

  10. Intricate Protein-Protein Interactions in the Cyanobacterial Circadian Clock*

    PubMed Central

    Egli, Martin

    2014-01-01

    The cyanobacterial circadian clock consists of a post-translational oscillator (PTO) and a PTO-dependent transcription-translation feedback loop (TTFL). The PTO can be reconstituted in vitro with the KaiA, KaiB, and KaiC proteins, enabling detailed biochemical and biophysical investigations. Both the CI and the CII halves of the KaiC hexamer harbor ATPases, but only the C-terminal CII ring exhibits kinase and phospho-transferase activities. KaiA stimulates the kinase and KaiB associates with KaiC during the dephosphorylation phase and sequesters KaiA. Recent research has led to conflicting models of the KaiB-KaiC interaction, precluding a clear understanding of KaiB function and KaiABC clock mechanism. PMID:24936066

  11. MEGADOCK: an all-to-all protein-protein interaction prediction system using tertiary structure data.

    PubMed

    Ohue, Masahito; Matsuzaki, Yuri; Uchikoga, Nobuyuki; Ishida, Takashi; Akiyama, Yutaka

    2014-01-01

    The elucidation of protein-protein interaction (PPI) networks is important for understanding cellular structure and function and structure-based drug design. However, the development of an effective method to conduct exhaustive PPI screening represents a computational challenge. We have been investigating a protein docking approach based on shape complementarity and physicochemical properties. We describe here the development of the protein-protein docking software package "MEGADOCK" that samples an extremely large number of protein dockings at high speed. MEGADOCK reduces the calculation time required for docking by using several techniques such as a novel scoring function called the real Pairwise Shape Complementarity (rPSC) score. We showed that MEGADOCK is capable of exhaustive PPI screening by completing docking calculations 7.5 times faster than the conventional docking software, ZDOCK, while maintaining an acceptable level of accuracy. When MEGADOCK was applied to a subset of a general benchmark dataset to predict 120 relevant interacting pairs from 120 x 120 = 14,400 combinations of proteins, an F-measure value of 0.231 was obtained. Further, we showed that MEGADOCK can be applied to a large-scale protein-protein interaction-screening problem with accuracy better than random. When our approach is combined with parallel high-performance computing systems, it is now feasible to search and analyze protein-protein interactions while taking into account three-dimensional structures at the interactome scale. MEGADOCK is freely available at http://www.bi.cs.titech.ac.jp/megadock. PMID:23855673

  12. Cluster conservation as a novel tool for studying protein-protein interactions evolution.

    PubMed

    Rahat, Ofer; Yitzhaky, Assif; Schreiber, Gideon

    2008-05-01

    Protein-protein interactions networks has come to be a buzzword associated with nets containing edges that represent a pair of interacting proteins (e.g. hormone-receptor, enzyme-inhibitor, antigen-antibody, and a subset of multichain biological machines). Yet, each such interaction composes its own unique network, in which vertices represent amino acid residues, and edges represent atomic contacts. Recent studies have shown that analyses of the data encapsulated in these detailed networks may impact predictions of structure-function correlation. Here, we study homologous families of protein-protein interfaces, which share the same fold but vary in sequence. In this context, we address what properties of the network are shared among relatives with different sequences (and hence different atomic interactions) and which are not. Herein, we develop the general mathematical framework needed to compare the modularity of homologous networks. We then apply this analysis to the structural data of a few interface families, including hemoglobin alpha-beta, growth hormone-receptor, and Serine protease-inhibitor. Our results suggest that interface modularity is an evolutionarily conserved property. Hence, protein-protein interfaces can be clustered down to a few modules, with the boundaries being evolutionarily conserved along homologous complexes. This suggests that protein engineering of protein-protein binding sites may be simplified by varying each module, but retaining the overall modularity of the interface. PMID:17972288

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

    PubMed Central

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

    2012-01-01

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

  14. Module organization and variance in protein-protein interaction networks

    PubMed Central

    Lin, Chun-Yu; Lee, Tsai-Ling; Chiu, Yi-Yuan; Lin, Yi-Wei; Lo, Yu-Shu; Lin, Chih-Ta; Yang, Jinn-Moon

    2015-01-01

    A module is a group of closely related proteins that act in concert to perform specific biological functions through protein–protein interactions (PPIs) that occur in time and space. However, the underlying module organization and variance remain unclear. In this study, we collected module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores and interface evolution scores to describe the module elements, including core and ring components. Functions of core components were highly correlated with those of essential genes. In comparison with ring components, core proteins/PPIs were conserved across multiple species. Subsequently, protein/module variance of PPI networks confirmed that core components form dynamic network hubs and play key roles in various biological functions. Based on the analyses of gene essentiality, module variance, and gene co-expression, we summarize the observations of module organization and variance as follows: 1) a module consists of core and ring components; 2) core components perform major biological functions and collaborate with ring components to execute certain functions in some cases; 3) core components are more conserved and essential during organizational changes in different biological states or conditions. PMID:25797237

  15. Protein/Protein Interactions in the Mammalian Heme Degradation Pathway

    PubMed Central

    Spencer, Andrea L. M.; Bagai, Ireena; Becker, Donald F.; Zuiderweg, Erik R. P.; Ragsdale, Stephen W.

    2014-01-01

    Heme oxygenase (HO) catalyzes the rate-limiting step in the O2-dependent degradation of heme to biliverdin, CO, and iron with electrons delivered from NADPH via cytochrome P450 reductase (CPR). Biliverdin reductase (BVR) then catalyzes conversion of biliverdin to bilirubin. We describe mutagenesis combined with kinetic, spectroscopic (fluorescence and NMR), surface plasmon resonance, cross-linking, gel filtration, and analytical ultracentrifugation studies aimed at evaluating interactions of HO-2 with CPR and BVR. Based on these results, we propose a model in which HO-2 and CPR form a dynamic ensemble of complex(es) that precede formation of the productive electron transfer complex. The 1H-15N TROSY NMR spectrum of HO-2 reveals specific residues, including Leu-201, near the heme face of HO-2 that are affected by the addition of CPR, implicating these residues at the HO/CPR interface. Alanine substitutions at HO-2 residues Leu-201 and Lys-169 cause a respective 3- and 22-fold increase in Km values for CPR, consistent with a role for these residues in CPR binding. Sedimentation velocity experiments confirm the transient nature of the HO-2·CPR complex (Kd = 15.1 μm). Our results also indicate that HO-2 and BVR form a very weak complex that is only captured by cross-linking. For example, under conditions where CPR affects the 1H-15N TROSY NMR spectrum of HO-2, BVR has no effect. Fluorescence quenching experiments also suggest that BVR binds HO-2 weakly, if at all, and that the previously reported high affinity of BVR for HO is artifactual, resulting from the effects of free heme (dissociated from HO) on BVR fluorescence. PMID:25196843

  16. PIMA: Protein-Protein interactions in Macromolecular Assembly - a web server for its Analysis and Visualization

    PubMed Central

    Kaleeckal Mathew, Oommen; Sowdhamini, Ramanathan

    2016-01-01

    Protein-protein interactions are essential for the basic biological machinery of the cell. This is important for processes like protein synthesis, enzyme kinetics, molecular assembly and signal transduction. A high number of macromolecular structural complexes are known due to recent advances in structure determination techniques. Therefore, it is of interest to develop an interactive tool to objectively analyze large protein complexes. Hence, we describe the development and utility of a web enabled application named ‘Protein-Protein Interaction in Macro-molecular Assembly’ (PIMA) for the analysis of large protein assemblies. The intricate details of physical interactions amongst protein subunits in a large complex are presented as simple user preferred interactive network diagrams PMID:27212837

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

    PubMed Central

    Teilum, Kaare; Olsen, Johan G.; Kragelund, Birthe B.

    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. PMID:26217672

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

    PubMed

    Teilum, Kaare; Olsen, Johan G; Kragelund, Birthe B

    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). PMID:26217672

  19. Using support vector machine for improving protein-protein interaction prediction utilizing domain interactions

    SciTech Connect

    Singhal, Mudita; Shah, Anuj R.; Brown, Roslyn N.; Adkins, Joshua N.

    2010-10-02

    Understanding protein interactions is essential to gain insights into the biological processes at the whole cell level. The high-throughput experimental techniques for determining protein-protein interactions (PPI) are error prone and expensive with low overlap amongst them. Although several computational methods have been proposed for predicting protein interactions there is definite room for improvement. Here we present DomainSVM, a predictive method for PPI that uses computationally inferred domain-domain interaction values in a Support Vector Machine framework to predict protein interactions. DomainSVM method utilizes evidence of multiple interacting domains to predict a protein interaction. It outperforms existing methods of PPI prediction by achieving very high explanation ratios, precision, specificity, sensitivity and F-measure values in a 10 fold cross-validation study conducted on the positive and negative PPIs in yeast. A Functional comparison study using GO annotations on the positive and the negative test sets is presented in addition to discussing novel PPI predictions in Salmonella Typhimurium.

  20. Ortholog-based protein-protein interaction prediction and its application to inter-species interactions

    PubMed Central

    Lee, Sheng-An; Chan, Cheng-hsiung; Tsai, Chi-Hung; Lai, Jin-Mei; Wang, Feng-Sheng; Kao, Cheng-Yan; Huang, Chi-Ying F

    2008-01-01

    Background The rapid growth of protein-protein interaction (PPI) data has led to the emergence of PPI network analysis. Despite advances in high-throughput techniques, the interactomes of several model organisms are still far from complete. Therefore, it is desirable to expand these interactomes with ortholog-based and other methods. Results Orthologous pairs of 18 eukaryotic species were expanded and merged with experimental PPI datasets. The contributions of interologs from each species were evaluated. The expanded orthologous pairs enable the inference of interologs for various species. For example, more than 32,000 human interactions can be predicted. The same dataset has also been applied to the prediction of host-pathogen interactions. PPIs between P. falciparum calmodulin and several H. sapiens proteins are predicted, and these interactions may contribute to the maintenance of host cell Ca2+ concentration. Using comparisons with Bayesian and structure-based approaches, interactions between putative HSP40 homologs of P. falciparum and the H. sapiens TNF receptor associated factor family are revealed, suggesting a role for these interactions in the interference of the human immune response to P. falciparum. Conclusion The PPI datasets are available from POINT and POINeT . Further development of methods to predict host-pathogen interactions should incorporate multiple approaches in order to improve sensitivity, and should facilitate the identification of targets for drug discovery and design. PMID:19091010

  1. Investigating Protein-protein Interactions in Live Cells Using Bioluminescence Resonance Energy Transfer

    PubMed Central

    Estruch, Sara B.; Fisher, Simon E.

    2014-01-01

    Assays based on Bioluminescence Resonance Energy Transfer (BRET) provide a sensitive and reliable means to monitor protein-protein interactions in live cells. BRET is the non-radiative transfer of energy from a 'donor' luciferase enzyme to an 'acceptor' fluorescent protein. In the most common configuration of this assay, the donor is Renilla reniformis luciferase and the acceptor is Yellow Fluorescent Protein (YFP). Because the efficiency of energy transfer is strongly distance-dependent, observation of the BRET phenomenon requires that the donor and acceptor be in close proximity. To test for an interaction between two proteins of interest in cultured mammalian cells, one protein is expressed as a fusion with luciferase and the second as a fusion with YFP. An interaction between the two proteins of interest may bring the donor and acceptor sufficiently close for energy transfer to occur. Compared to other techniques for investigating protein-protein interactions, the BRET assay is sensitive, requires little hands-on time and few reagents, and is able to detect interactions which are weak, transient, or dependent on the biochemical environment found within a live cell. It is therefore an ideal approach for confirming putative interactions suggested by yeast two-hybrid or mass spectrometry proteomics studies, and in addition it is well-suited for mapping interacting regions, assessing the effect of post-translational modifications on protein-protein interactions, and evaluating the impact of mutations identified in patient DNA. PMID:24893771

  2. The influence of protein-protein interactions on the organization of proteins within thylakoid membranes.

    PubMed

    Tremmel, I G; Weis, E; Farquhar, G D

    2005-04-01

    The influence of attractive protein-protein interactions on the organization of photosynthetic proteins within the thylakoid membrane was investigated. Protein-protein interactions were simulated using Monte Carlo techniques and the influence of different interaction energies was examined. It was found that weak interactions led to protein clusters whereas strong interactions led to ramified chains. An optimum curve for the relationship between interaction energy and the number of contact sites emerged. With increasing particle densities the effect decreased. In a mixture of interacting and noninteracting particles the distance between the noninteracting particles was increased and there seemed to be much more free space around them. In thylakoids, this could lead to a more homogeneous distribution of the noninteracting but rate-limiting cytochrome bf complexes. Due to the increased free space between cytochrome bf, obstruction of binding sites--occurring unavoidably in a random distribution--may be drastically reduced. Furthermore, protein-protein interactions in thylakoids may lead to a decrease in plastoquinone diffusion. PMID:15665125

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

    PubMed

    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. PMID:16099201

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

    PubMed

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

    2014-05-01

    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. PMID:24486250

  5. Protein-protein interaction between ezrin and p65 in human breast cancer cells.

    PubMed

    Tang, R; Li, F X; Shao, W F; Wen, Q S; Yu, X R; Xiong, J B

    2016-01-01

    Our study aimed to investigate the co-localization and protein-protein interactions between ezrin and p65 in human breast cancer cells. Liquid chromatography-mass spectrometry (LCMS) was used to uncover novel protein interactions with ezrin in MDA-MB-231 cells. Endogenous co-immunoprecipitation was used to validate protein-protein interactions between ezrin and p65 in MDA-MB-231. Exogenous interactions between ezrin and p65 were validated in MDA-MB-231 cells via Flag-ezrin and HA-p65 co-transfection and followed by co-immunoprecipitation. Immunofluorescence staining was used to visualize ezrin and p65 co-localization in MDA-MB-231. LCMS results showed that there were 1000 proteins interacting with ezrin in MDA-MB-231 cells. Ezrin and p65 interactions were confirmed with both endogenous and exogenous methods. We were also able to visualize ezrin and p65 co-localization in MDA-MB-231. In summary, we found protein-protein interactions between Ezrin and p65 in human breast cancer cells. PMID:27420986

  6. Studying Protein-Protein Interactions in Budding Yeast Using Co-immunoprecipitation.

    PubMed

    Foltman, Magdalena; Sanchez-Diaz, Alberto

    2016-01-01

    Understanding protein-protein interactions and the architecture of protein complexes in which they work is essential to identify their biological role. Protein co-immunoprecipitation (co-IP) is an invaluable technique used in biochemistry allowing the identification of protein interactors. Here, we describe in detail an immunoaffinity purification protocol as a one-step or two-step immunoprecipitation from budding yeast Saccharomyces cerevisiae cells to subsequently detect interactions between proteins involved in the same biological process. PMID:26519317

  7. Homology Inference of Protein-Protein Interactions via Conserved Binding Sites

    PubMed Central

    Tyagi, Manoj; Thangudu, Ratna R.; Zhang, Dachuan; Bryant, Stephen H.; Madej, Thomas; Panchenko, Anna R.

    2012-01-01

    The coverage and reliability of protein-protein interactions determined by high-throughput experiments still needs to be improved, especially for higher organisms, therefore the question persists, how interactions can be verified and predicted by computational approaches using available data on protein structural complexes. Recently we developed an approach called IBIS (Inferred Biomolecular Interaction Server) to predict and annotate protein-protein binding sites and interaction partners, which is based on the assumption that the structural location and sequence patterns of protein-protein binding sites are conserved between close homologs. In this study first we confirmed high accuracy of our method and found that its accuracy depends critically on the usage of all available data on structures of homologous complexes, compared to the approaches where only a non-redundant set of complexes is employed. Second we showed that there exists a trade-off between specificity and sensitivity if we employ in the prediction only evolutionarily conserved binding site clusters or clusters supported by only one observation (singletons). Finally we addressed the question of identifying the biologically relevant interactions using the homology inference approach and demonstrated that a large majority of crystal packing interactions can be correctly identified and filtered by our algorithm. At the same time, about half of biological interfaces that are not present in the protein crystallographic asymmetric unit can be reconstructed by IBIS from homologous complexes without the prior knowledge of crystal parameters of the query protein. PMID:22303436

  8. A Gateway-Based System for Fast Evaluation of Protein-Protein Interactions in Bacteria

    PubMed Central

    Wille, Thorsten; Barlag, Britta; Jakovljevic, Vladimir; Hensel, Michael; Sourjik, Victor; Gerlach, Roman G.

    2015-01-01

    Protein-protein interactions are important layers of regulation in all kingdoms of life. Identification and characterization of these interactions is one challenging task of the post-genomic era and crucial for understanding of molecular processes within a cell. Several methods have been successfully employed during the past decades to identify protein-protein interactions in bacteria, but most of them include tedious and time-consuming manipulations of DNA. In contrast, the MultiSite Gateway system is a fast tool for transfer of multiple DNA fragments between plasmids enabling simultaneous and site directed cloning of up to four fragments into one construct. Here we developed a new set of Gateway vectors including custom made entry vectors and modular Destination vectors for studying protein-protein interactions via Fluorescence Resonance Energy Transfer (FRET), Bacterial two Hybrid (B2H) and split Gaussia luciferase (Gluc), as well as for fusions with SNAP-tag and HaloTag for dual-color super-resolution microscopy. As proof of principle, we characterized the interaction between the Salmonella effector SipA and its chaperone InvB via split Gluc and B2H approach. The suitability for FRET analysis as well as functionality of fusions with SNAP- and HaloTag could be demonstrated by studying the transient interaction between chemotaxis response regulator CheY and its phosphatase CheZ. PMID:25856398

  9. Protein-protein interaction studies based on molecular aptamers by affinity capillary electrophoresis.

    PubMed

    Huang, Chih-Ching; Cao, Zehui; Chang, Huan-Tsung; Tan, Weihong

    2004-12-01

    Protein-DNA/protein-protein interactions play critical roles in many biological processes. We report here the investigation of protein-protein interactions using molecular aptamers with affinity capillary electrophoresis (ACE). A human alpha-thrombin binding aptamer was labeled with 6-carboxyfluorescein and exploited as a selective fluorescent probe for studying thrombin-protein interactions using capillary electrophoresis with laser-induced fluorescence. A 15-mer binding DNA aptamer can be separated into two peaks in CE that correspond to the linear aptamer (L-Apt) and the thrombin-binding G-quadruplex structure in the presence of K(+) or Ba(2+). In a bare capillary, the peak area of G-quadruplex aptamer (G-Apt) was found to decrease with the addition of thrombin while that of L-Apt remained unchanged. Even though the peak of the G-Apt/thrombin binding complex is broad due to a weaker binding affinity between aptamer and thrombin, we were still able to quantify the thrombin and anti-thrombin proteins (human anti-thrombin III, AT III) based on the peak areas of free G-Apt. The detection limits of thrombin and AT III were 9.8 and 2.1 nM, respectively. The aptamer-based competitive ACE assay has also been applied to quantify thrombin-anti-thrombin III interaction and to monitor this reaction in real time. The addition of poly(ethylene glycol) to the sample matrix stabilized the complex of the G-Aptthrombin. This assay can be used to study the interactions between thrombin and proteins that do not disrupt G-Apt binding property at Exosit I site of the thrombin. Our aptamer-based ACE assay can be an effective approach for studying protein-protein interactions and for analyzing binding site and binding constant information in protein-protein and protein-DNA interaction studies. PMID:15571349

  10. Use of Flow Cytometric Methods to Quantify Protein-Protein Interactions

    PubMed Central

    Blazer, Levi L.; Roman, David L.; Muxlow, Molly R.; Neubig, Richard R.

    2010-01-01

    A method is described for the quantitative analysis of protein-protein interactions using the Flow Cytometry Protein Interaction Assay (FCPIA). This method is based upon immobilizing protein on a polystyrene bead, incubating these beads with a fluorescently labeled binding partner, and assessing the sample for bead-associated fluorescence in a flow cytometer. This method can be used to calculate protein-protein interaction affinities or to perform competition experiments with unlabeled binding partners or small molecules. Examples described in this protocol highlight the use of this assay in the quantification of the affinity of binding partners of the Regulator of G-Protein Signaling protein, RGS19, in either a saturation or competition format. An adaptation of this method that is compatible for High Throughput screening is also provided. PMID:20069525

  11. Investigation of protein-protein interactions in living cells by chemical crosslinking and mass spectrometry.

    PubMed

    Sinz, Andrea

    2010-08-01

    The identification of protein-protein interactions within their physiological environment is the key to understanding biological processes at the molecular level. However, the artificial nature of in vitro experiments, with their lack of other cellular components, may obstruct observations of specific cellular processes. In vivo analyses can provide information on the processes within a cell that might not be observed in vitro. Chemical crosslinking combined with mass spectrometric analysis of the covalently connected binding partners allows us to identify interacting proteins and to map their interface regions directly in the cell. In this paper, different in vivo crosslinking strategies for deriving information on protein-protein interactions in their physiological environment are described. PMID:20076950

  12. Some remarks on prediction of protein-protein interaction with machine learning.

    PubMed

    Zhang, Shao-Wu; Wei, Ze-Gang

    2015-01-01

    Protein-protein interactions (PPIs) play a key role in many cellular processes. Uncovering the PPIs and their function within the cell is a challenge of post-genomic biology and will improve our understanding of disease and help in the development of novel methods for disease diagnosis and forensics. The experimental methods currently used to identify PPIs are both time-consuming and expensive, and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. These obstacles could be overcome by developing computational approaches to predict PPIs and validate the obtained experimental results. In this work, we will describe the recent advances in predicting protein-protein interaction from the following aspects: i) the benchmark dataset construction, ii) the sequence representation approaches, iii) the common machine learning algorithms, and iv) the cross-validation test methods and assessment metrics. PMID:25548927

  13. Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network

    PubMed Central

    Yang, Lei; Zhao, Xudong; Tang, Xianglong

    2014-01-01

    Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases. PMID:25013377

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

    PubMed

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

    2015-01-01

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

  15. The Intrinsic Geometric Structure of Protein-Protein Interaction Networks for Protein Interaction Prediction.

    PubMed

    Fang, Yi; Sun, Mengtian; Dai, Guoxian; Ramain, Karthik

    2016-01-01

    Recent developments in high-throughput technologies for measuring protein-protein interaction (PPI) have profoundly advanced our ability to systematically infer protein function and regulation. However, inherently high false positive and false negative rates in measurement have posed great challenges in computational approaches for the prediction of PPI. A good PPI predictor should be 1) resistant to high rate of missing and spurious PPIs, and 2) robust against incompleteness of observed PPI networks. To predict PPI in a network, we developed an intrinsic geometry structure (IGS) for network, which exploits the intrinsic and hidden relationship among proteins in network through a heat diffusion process. In this process, all explicit PPIs participate simultaneously to glue local infinitesimal and noisy experimental interaction data to generate a global macroscopic descriptions about relationships among proteins. The revealed implicit relationship can be interpreted as the probability of two proteins interacting with each other. The revealed relationship is intrinsic and robust against individual, local and explicit protein interactions in the original network. We apply our approach to publicly available PPI network data for the evaluation of the performance of PPI prediction. Experimental results indicate that, under different levels of the missing and spurious PPIs, IGS is able to robustly exploit the intrinsic and hidden relationship for PPI prediction with a higher sensitivity and specificity compared to that of recently proposed methods. PMID:26886733

  16. Interaction and localization diversities of global and local hubs in human protein-protein interaction networks.

    PubMed

    Kiran, M; Nagarajaram, H A

    2016-08-16

    Hubs, the highly connected nodes in protein-protein interaction networks (PPINs), are associated with several characteristic properties and are known to perform vital roles in cells. We defined two classes of hubs, global (housekeeping) and local (tissue-specific) hubs. These two categories of hubs are distinct from each other with respect to their abundance, structure and function. However, how distinct are the spatial expression pattern and other characteristics of their interacting partners is still not known. Our investigations revealed that the partners of the local hubs compared with those of global hubs are conserved across the tissues in which they are expressed. Partners of local hubs show diverse subcellular localizations as compared with the partners of global hubs. We examined the nature of interacting domains in both categories of hubs and found that they are promiscuous in global hubs but not so in local hubs. Deletion of some of the local and global hubs has an impact on the characteristic path length of the network indicating that those hubs are inter-modular in nature. Our present study has, therefore, shed further light on the characteristic features of the local and global hubs in human PPIN. This knowledge of different topological aspects of hubs with regard to their types and subtypes is essential as it helps in better understanding of roles of hub proteins in various cellular processes under various conditions including those caused by host-pathogen interactions and therefore useful in prioritizing targets for drug design and repositioning. PMID:27400769

  17. Assessing Energetic Contributions to Binding from a Disordered Region in a Protein-Protein Interaction

    SciTech Connect

    S Cho; C Swaminathan; D Bonsor; M Kerzic; R Guan; J Yang; C Kieke; P Anderson; D Kranz; et al.

    2011-12-31

    Many functional proteins are at least partially disordered prior to binding. Although the structural transitions upon binding of disordered protein regions can influence the affinity and specificity of protein complexes, their precise energetic contributions to binding are unknown. Here, we use a model protein-protein interaction system in which a locally disordered region has been modified by directed evolution to quantitatively assess the thermodynamic and structural contributions to binding of disorder-to-order transitions. Through X-ray structure determination of the protein binding partners before and after complex formation and isothermal titration calorimetry of the interactions, we observe a correlation between protein ordering and binding affinity for complexes along this affinity maturation pathway. Additionally, we show that discrepancies between observed and calculated heat capacities based on buried surface area changes in the protein complexes can be explained largely by heat capacity changes that would result solely from folding the locally disordered region. Previously developed algorithms for predicting binding energies of protein-protein interactions, however, are unable to correctly model the energetic contributions of the structural transitions in our model system. While this highlights the shortcomings of current computational methods in modeling conformational flexibility, it suggests that the experimental methods used here could provide training sets of molecular interactions for improving these algorithms and further rationalizing molecular recognition in protein-protein interactions.

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

    PubMed

    Nguyen, Peter Q; Silberg, Jonathan J

    2010-07-01

    Many proteins can be split into fragments that exhibit enhanced function upon fusion to interacting proteins. While this strategy has been widely used to create protein-fragment complementation assays (PCAs) for discovering protein-protein interactions within mesophilic organisms, similar assays have not yet been developed for studying natural and engineered protein complexes at the temperatures where thermophilic microbes grow. We describe the development of a selection for protein-protein interactions within Thermus thermophilus that is based upon growth complementation by fragments of Thermotoga neapolitana adenylate kinase (AK(Tn)). Complementation studies with an engineered thermophile (PQN1) that is not viable above 75 degrees C because its adk gene has been replaced by a Geobacillus stearothermophilus ortholog revealed that growth could be restored at 78 degrees C by a vector that coexpresses polypeptides corresponding to residues 1-79 and 80-220 of AK(Tn). In contrast, PQN1 growth was not complemented by AK(Tn) fragments harboring a C156A mutation within the zinc-binding tetracysteine motif unless these fragments were fused to Thermotoga maritima chemotaxis proteins that heterodimerize (CheA and CheY) or homodimerize (CheX). This enhanced complementation is interpreted as arising from chemotaxis protein-protein interactions, since AK(Tn)-C156A fragments having only one polypeptide fused to a chemotaxis protein did not complement PQN1 to the same extent. This selection increases the maximum temperature where a PCA can be used to engineer thermostable protein complexes and to map protein-protein interactions. PMID:20418388

  19. Identification of essential proteins based on ranking edge-weights in protein-protein interaction networks.

    PubMed

    Wang, Yan; Sun, Huiyan; Du, Wei; Blanzieri, Enrico; Viero, Gabriella; Xu, Ying; Liang, Yanchun

    2014-01-01

    Essential proteins are those that are indispensable to cellular survival and development. Existing methods for essential protein identification generally rely on knock-out experiments and/or the relative density of their interactions (edges) with other proteins in a Protein-Protein Interaction (PPI) network. Here, we present a computational method, called EW, to first rank protein-protein interactions in terms of their Edge Weights, and then identify sub-PPI-networks consisting of only the highly-ranked edges and predict their proteins as essential proteins. We have applied this method to publicly-available PPI data on Saccharomyces cerevisiae (Yeast) and Escherichia coli (E. coli) for essential protein identification, and demonstrated that EW achieves better performance than the state-of-the-art methods in terms of the precision-recall and Jackknife measures. The highly-ranked protein-protein interactions by our prediction tend to be biologically significant in both the Yeast and E. coli PPI networks. Further analyses on systematically perturbed Yeast and E. coli PPI networks through randomly deleting edges demonstrate that the proposed method is robust and the top-ranked edges tend to be more associated with known essential proteins than the lowly-ranked edges. PMID:25268881

  20. Structural Analysis of Protein-Protein Interactions in Type I Polyketide Synthases

    PubMed Central

    Xu, Wei; Qiao, Kangjian; Tang, Yi

    2013-01-01

    Polyketide synthases (PKSs) are responsible for synthesizing a myriad of natural products with agricultural, medicinal relevance. The PKSs consist of multiple functional domains of which each can catalyze a specified chemical reaction leading to the synthesis of polyketides. Biochemical studies showed that protein-substrate and protein-protein interactions play crucial roles in these complex regio-/stereo- selective biochemical processes. Recent developments on X-ray crystallography and protein NMR techniques have allowed us to understand the biosynthetic mechanism of these enzymes from their structures. These structural studies have facilitated the elucidation of sequence-function relationship of PKSs and will ultimately contribute to the prediction of product structure. This review will focus on the current knowledge of type I PKS structures and the protein-protein interactions in this system. PMID:23249187

  1. A residue level protein-protein interaction model in electrolyte solutions

    NASA Astrophysics Data System (ADS)

    Song, Xueyu

    2014-03-01

    The osmotic second virial coefficients B2 are directly related to the solubility of protein molecules in electrolyte solutions and can be useful to narrow down the search parameter space of protein crystallization conditions. Using a residue level model of protein-protein interaction in electrolyte solutions B2 of bovine pancreatic trypsin inhibitor and lysozyme in various solution conditions such as salt concentration, pH and temperature are calculated using an extended Fast Multipole Methods in combination with the boundary element formulation. Overall, the calculated B2 are well correlated with the experimental observations for various solution conditions. In combination with our previous work on the binding affinity calculations of protein complexes it is demonstrated that our residue level model can be used as a reliable model to describe protein-protein interaction in solutions.

  2. Fabrication of Molecular Strain Probes for Illuminating Protein-Protein Interactions.

    PubMed

    Kim, Sung-Bae; Fujii, Rika

    2016-01-01

    A unique bioluminescent imaging probe is introduced for illuminating molecular tension appended by protein-protein interactions (PPIs) of interest. A full-length luciferase is sandwiched between two proteins of interest via minimal flexible linkers. The ligand-activated PPIs append intramolecular tension to the sandwiched luciferase, boosting or dropping the enzymatic activity in a quantitative manner. This method guides construction of a new lineage of bioassays for ligand-activated PPIs. PMID:27424904

  3. Predicting disease-related genes by topological similarity in human protein-protein interaction network

    NASA Astrophysics Data System (ADS)

    Zhang, Lei; Hu, Ke; Tang, Yi

    2010-08-01

    Predicting genes likely to be involved in human diseases is an important task in bioinformatics field. Nowadays, the accumulation of human protein-protein interactions (PPIs) data provides us an unprecedented opportunity to gain insight into human diseases. In this paper, we adopt the topological similarity in human protein-protein interaction network to predict disease-related genes. As a computational algorithm to speed up the identification of disease-related genes, the topological similarity has substantial advantages over previous topology-based algorithms. First of all, it provides a global measurement of similarity between two vertices. Secondly, quantity which can measure new topological feature has been integrated into the notion of topological similarity. Our method is specially designed for predicting disease-related genes of single disease-gene family. The proposed method is applied to human protein-protein interaction and hepatocellular carcinoma (HCC) data. The results show a significant enrichment of disease-related genes that are characterized by higher topological similarity than other genes.

  4. Kinetic Partitioning Between Alternative Protein:Protein Interactions Controls a Transcriptional Switch

    PubMed Central

    Zhao, Huaying; Beckett, Dorothy

    2008-01-01

    SUMMARY Proteins can perform completely distinct functions in response to the particular partners to which they bind. Consequently, determination of the mechanism of functional regulation in such systems requires elucidation of the mechanism switching between binding partners. The central protein of the Escherichia coli Biotin Regulatory System, BirA, switches between it’s function as a metabolic enzyme or a transcriptional repressor in response to binding either the Biotin Carboxyl Carrier Protein subunit of acetyl-CoA carboxylase or a second BirA monomer. These two protein:protein interactions are structurally mutually exclusive. Results of previous studies suggest that the system is regulated by kinetic partitioning between the two protein:protein interactions. In this work sedimentation velocity was employed to directly monitor the partitioning. Results of the measurements indicate similar equilibrium parameters governing formation of the two protein:protein interactions. Kinetic analysis of the sedimentation velocity data indicates that holoBirA dimerization is governed by very slow forward and reverse rate constants. The slow kinetics of holoBirA dimerization combined with fluctuations in the intracellular apoBCCP pool are critical determinants in partitioning of BirA between its distinct biological functions. PMID:18508076

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

    PubMed Central

    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. PMID:26469276

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

    PubMed Central

    van Dijk, Aalt D. J.; Morabito, Giuseppa; Fiers, Martijn; van Ham, Roeland C. H. J.; Angenent, Gerco C.; Immink, Richard G. H.

    2010-01-01

    Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein 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 network evolution. PMID

  7. Global approaches to study protein-protein interactions among viruses and hosts.

    PubMed

    Mendez-Rios, Jorge; Uetz, Peter

    2010-02-01

    While high-throughput protein-protein interaction screens were first published approximately 10 years ago, systematic attempts to map interactions among viruses and hosts started only a few years ago. HIV-human interactions dominate host-pathogen interaction databases (with approximately 2000 interactions) despite the fact that probably none of these interactions have been identified in systematic interaction screens. Recently, combinations of protein interaction data with RNAi and other functional genomics data allowed researchers to model more complex interaction networks. The rapid progress in this area promises a flood of new data in the near future, with clinical applications as soon as structural and functional genomics catches up with next-generation sequencing of human variation and structure-based drug design. PMID:20143950

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

    PubMed

    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. PMID:27080133

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

    PubMed

    Peri, Claudio; Morra, Giulia; Colombo, Giorgio

    2016-01-01

    Understanding protein-protein interactions (PPI) at the molecular level is a fundamental task in the design of new drugs, the prediction of protein function and the clarification of the mechanisms of (dis)regulation of biochemical pathways. In this study, we use a novel computational approach to investigate the energetics of aminoacid networks located on the surface of proteins, isolated and in complex with their respective partners. Interestingly, the analysis of individual proteins identifies patches of surface residues that, when mapped on the structure of their respective complexes, reveal regions of residue-pair couplings that extend across the binding interfaces, forming continuous motifs. An enhanced effect is visible across the proteins of the dataset forming larger quaternary assemblies. The method indicates the presence of energetic signatures in the isolated proteins that are retained in the bound form, which we hypothesize to determine binding orientation upon complex formation. We propose our method, BLUEPRINT, as a complement to different approaches ranging from the ab-initio characterization of PPIs, to protein-protein docking algorithms, for the physico-chemical and functional investigation of protein-protein interactions. PMID:27050828

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

    PubMed Central

    Peri, Claudio; Morra, Giulia; Colombo, Giorgio

    2016-01-01

    Understanding protein-protein interactions (PPI) at the molecular level is a fundamental task in the design of new drugs, the prediction of protein function and the clarification of the mechanisms of (dis)regulation of biochemical pathways. In this study, we use a novel computational approach to investigate the energetics of aminoacid networks located on the surface of proteins, isolated and in complex with their respective partners. Interestingly, the analysis of individual proteins identifies patches of surface residues that, when mapped on the structure of their respective complexes, reveal regions of residue-pair couplings that extend across the binding interfaces, forming continuous motifs. An enhanced effect is visible across the proteins of the dataset forming larger quaternary assemblies. The method indicates the presence of energetic signatures in the isolated proteins that are retained in the bound form, which we hypothesize to determine binding orientation upon complex formation. We propose our method, BLUEPRINT, as a complement to different approaches ranging from the ab-initio characterization of PPIs, to protein-protein docking algorithms, for the physico-chemical and functional investigation of protein-protein interactions. PMID:27050828

  11. A Cascade Random Forests Algorithm for Predicting Protein-Protein Interaction Sites.

    PubMed

    Wei, Zhi-Sen; Yang, Jing-Yu; Shen, Hong-Bin; Yu, Dong-Jun

    2015-10-01

    Protein-protein interactions exist ubiquitously and play important roles in the life cycles of living cells. The interaction sites (residues) are essential to understanding the underlying mechanisms of protein-protein interactions. Previous research has demonstrated that the accurate identification of protein-protein interaction sites (PPIs) is helpful for developing new therapeutic drugs because many drugs will interact directly with those residues. Because of its significant potential in biological research and drug development, the prediction of PPIs has become an important topic in computational biology. However, a severe data imbalance exists in the PPIs prediction problem, where the number of the majority class samples (non-interacting residues) is far larger than that of the minority class samples (interacting residues). Thus, we developed a novel cascade random forests algorithm (CRF) to address the serious data imbalance that exists in the PPIs prediction problem. The proposed CRF resolves the negative effect of data imbalance by connecting multiple random forests in a cascade-like manner, each of which is trained with a balanced training subset that includes all minority samples and a subset of majority samples using an effective ensemble protocol. Based on the proposed CRF, we implemented a new sequence-based PPIs predictor, called CRF-PPI, which takes the combined features of position-specific scoring matrices, averaged cumulative hydropathy, and predicted relative solvent accessibility as model inputs. Benchmark experiments on both the cross validation and independent validation datasets demonstrated that the proposed CRF-PPI outperformed the state-of-the-art sequence-based PPIs predictors. The source code for CRF-PPI and the benchmark datasets are available online at http://csbio.njust.edu.cn/bioinf/CRF-PPI for free academic use. PMID:26441427

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

    PubMed

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

    2015-01-01

    Accurate identification of protein-protein interactions (PPI) is the key step in understanding proteins' biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein-protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB) database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein-protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC). Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent). PMID:26157620

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

    PubMed Central

    2010-01-01

    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. PMID:20550717

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

    PubMed Central

    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. PMID:26697220

  15. Effective protein-protein interaction from structure factor data of a lysozyme solution

    SciTech Connect

    Abramo, M. C.; Caccamo, C.; Costa, D.; Ruberto, R.; Wanderlingh, U.; Cavero, M.; Pellicane, G.

    2013-08-07

    We report the determination of an effective protein-protein central potential for a lysozyme solution, obtained from the direct inversion of the total structure factor of the system, as extracted from small angle neutron scattering. The inversion scheme rests on a hypernetted-chain relationship between the effective potential and the structural functions, and is preliminarily tested for the case of a Lennard-Jones interaction. The characteristics of our potential are discussed in comparison with current models of effective interactions in complex fluids. The phase behavior predictions are also investigated.

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

    PubMed

    Buijsman, W; Sheinman, M

    2014-02-01

    Various biological sensory systems exhibit a response to a relative change of the stimulus, often referred to as fold-change detection. In the past few years, fold-change detecting mechanisms, based on transcriptional networks, have been proposed. Here we present a fold-change detecting mechanism, based on protein-protein interactions, consisting of two interacting proteins. This mechanism does not consume chemical energy and is not subject to transcriptional and translational noise, in contrast to previously proposed mechanisms. We show by analytical and numerical calculations that the mechanism is robust and can have a fast, precise, and efficient response for parameters that are relevant to eukaryotic cells. PMID:25353514

  17. Screening for in planta protein-protein interactions combining bimolecular fluorescence complementation with flow cytometry

    PubMed Central

    2012-01-01

    Understanding protein and gene function requires identifying interaction partners using biochemical, molecular or genetic tools. In plants, searching for novel protein-protein interactions is limited to protein purification assays, heterologous in vivo systems such as the yeast-two-hybrid or mutant screens. Ideally one would be able to search for novel protein partners in living plant cells. We demonstrate that it is possible to screen for novel protein-protein interactions from a random library in protoplasted Arabidopsis plant cells and recover some of the interacting partners. Our screen is based on capturing the bi-molecular complementation of mYFP between an YN-bait fusion partner and a completely random prey YC-cDNA library with FACS. The candidate interactions were confirmed using in planta BiFC assays and in planta FRET-FLIM assays. From this work, we show that the well characterized protein Calcium Dependent Protein Kinase 3 (CPK3) interacts with APX3, HMGB5, ORP2A and a ricin B-related lectin domain containing protein At2g39050. This is one of the first randomin planta screens to be successfully employed. PMID:22789293

  18. Split-Cre recombinase effectively monitors protein-protein interactions in living bacteria.

    PubMed

    O'Brien, Sean P; DeLisa, Matthew P

    2014-03-01

    The ability of Cre recombinase to excise genetic material has been used extensively for genome engineering in prokaryotic and eukaryotic cells. Recently, split-Cre fragments have been described that advance control of recombinase activity in mammalian cells. However, whether these fragments can be utilized for monitoring protein-protein interactions has not been reported. In this work, we developed a protein-fragment complementation assay (PCA) based on split-Cre for monitoring and engineering pairwise protein interactions in living Escherichia coli cells. This required creation of a dual-fluorescent reporter plasmid that permits visualization of reconstituted Cre recombinase activity by switching from red to green in the presence of an interacting protein pair. The resulting split-Cre PCA faithfully links cell fluorescence with differences in binding affinity, thereby allowing the facile isolation of high-affinity binders based on phenotype. Given the resolution of its activity and sensitivity to interactions, our system may prove a viable option for poorly expressed or weakly interacting protein pairs that evade detection in other PCA formats. Based on these findings, we anticipate that our split-Cre PCA will become a highly complementary and useful new addition to the protein-protein interaction toolbox. PMID:24390935

  19. Protein-protein interactions between histidine kinases and response regulators of Mycobacterium tuberculosis H37Rv.

    PubMed

    Lee, Ha-Na; Jung, Kwang-Eun; Ko, In-Jeong; Baik, Hyung Suk; Oh, Jeong-Il

    2012-04-01

    Using yeast two-hybrid assay, we investigated protein-protein interactions between all orthologous histidine kinase (HK)/response regulator (RR) pairs of M. tuberculosis H37Rv and identified potential protein-protein interactions between a noncognate HK/RR pair, DosT/NarL. The protein interaction between DosT and NarL was verified by phosphotransfer reaction from DosT to NarL. Furthermore, we found that the DosT and DosS HKs, which share considerable sequence similarities to each other and form a two-component system with the DosR RR, have different cross-interaction capabilities with NarL: DosT interacted with NarL, while DosS did not. The dimerization domains of DosT and DosS were shown to be sufficient to confer specificity for DosR, and the different cross-interaction abilities of DosS and DosT with NarL were demonstrated to be attributable to variations in the amino acid sequences of the α2-helices of their dimerization domains. PMID:22538656

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

    PubMed Central

    2014-01-01

    Background Molecular interactions need to be taken into account to adequately model the complex behavior of biological systems. These interactions are captured by various types of biological networks, such as metabolic, gene-regulatory, signal transduction and protein-protein interaction networks. We recently developed Natalie, which computes high-quality network alignments via advanced methods from combinatorial optimization. Results Here, we present NatalieQ, a web server for topology-based alignment of a specified query protein-protein interaction network to a selected target network using the Natalie algorithm. By incorporating similarity at both the sequence and the network level, we compute alignments that allow for the transfer of functional annotation as well as for the prediction of missing interactions. We illustrate the capabilities of NatalieQ with a biological case study involving the Wnt signaling pathway. Conclusions We show that topology-based network alignment can produce results complementary to those obtained by using sequence similarity alone. We also demonstrate that NatalieQ is able to predict putative interactions. The server is available at: http://www.ibi.vu.nl/programs/natalieq/. PMID:24690407

  1. Energy Landscape of All-Atom Protein-Protein Interactions Revealed by Multiscale Enhanced Sampling

    PubMed Central

    Moritsugu, Kei; Terada, Tohru; Kidera, Akinori

    2014-01-01

    Protein-protein interactions are regulated by a subtle balance of complicated atomic interactions and solvation at the interface. To understand such an elusive phenomenon, it is necessary to thoroughly survey the large configurational space from the stable complex structure to the dissociated states using the all-atom model in explicit solvent and to delineate the energy landscape of protein-protein interactions. In this study, we carried out a multiscale enhanced sampling (MSES) simulation of the formation of a barnase-barstar complex, which is a protein complex characterized by an extraordinary tight and fast binding, to determine the energy landscape of atomistic protein-protein interactions. The MSES adopts a multicopy and multiscale scheme to enable for the enhanced sampling of the all-atom model of large proteins including explicit solvent. During the 100-ns MSES simulation of the barnase-barstar system, we observed the association-dissociation processes of the atomistic protein complex in solution several times, which contained not only the native complex structure but also fully non-native configurations. The sampled distributions suggest that a large variety of non-native states went downhill to the stable complex structure, like a fast folding on a funnel-like potential. This funnel landscape is attributed to dominant configurations in the early stage of the association process characterized by near-native orientations, which will accelerate the native inter-molecular interactions. These configurations are guided mostly by the shape complementarity between barnase and barstar, and lead to the fast formation of the final complex structure along the downhill energy landscape. PMID:25340714

  2. Exploiting Expert Knowledge of Protein-Protein Interactions in a Computational Evolution System for Detecting Epistasis

    NASA Astrophysics Data System (ADS)

    Pattin, Kristine A.; Payne, Joshua L.; Hill, Douglas P.; Caldwell, Thomas; Fisher, Jonathan M.; Moore, Jason H.

    The etiology of common human disease often involves a complex genetic architecture, where numerous points of genetic variation interact to influence disease susceptibility. Automating the detection of such epistatic genetic risk factors poses a major computational challenge, as the number of possible gene-gene interactions increases combinatorially with the number of sequence variations. Previously, we addressed this challenge with the development of a computational evolution system (CES) that incorporates greater biological realism than traditional artificial evolution methods. Our results demonstrated that CES is capable of efficiently navigating these large and rugged epistatic landscapes toward the discovery of biologically meaningful genetic models of disease predisposition. Further, we have shown that the efficacy of CES is improved dramatically when the system is provided with statistical expert knowledge. We anticipate that biological expert knowledge, such as genetic regulatory or protein-protein interaction maps, will provide complementary information, and further improve the ability of CES to model the genetic architectures of common human disease. The goal of this study is to test this hypothesis, utilizing publicly available protein-protein interaction information. We show that by incorporating this source of expert knowledge, the system is able to identify functional interactions that represent more concise models of disease susceptibility with improved accuracy. Our ability to incorporate biological knowledge into learning algorithms is an essential step toward the routine use of methods such as CES for identifying genetic risk factors for common human diseases.

  3. STRING v10: protein-protein interaction networks, integrated over the tree of life.

    PubMed

    Szklarczyk, Damian; Franceschini, Andrea; Wyder, Stefan; Forslund, Kristoffer; Heller, Davide; Huerta-Cepas, Jaime; Simonovic, Milan; Roth, Alexander; Santos, Alberto; Tsafou, Kalliopi P; Kuhn, Michael; Bork, Peer; Jensen, Lars J; von Mering, Christian

    2015-01-01

    The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks. PMID:25352553

  4. Pharmacological manipulation of transcription factor protein-protein interactions: opportunities and obstacles.

    PubMed

    Fontaine, Frank; Overman, Jeroen; François, Mathias

    2015-01-01

    Much research on transcription factor biology and their genetic pathways has been undertaken over the last 30 years, especially in the field of developmental biology and cancer. Yet, very little is known about the molecular modalities of highly dynamic interactions between transcription factors, genomic DNA, and protein partners. Methodological breakthroughs such as RNA-seq (RNA-sequencing), ChIP-seq (chromatin immunoprecipitation sequencing), RIME (rapid immunoprecipitation mass spectrometry of endogenous proteins), and single-molecule imaging will dramatically accelerate the discovery rate of their molecular mode of action in the next few years. From a pharmacological viewpoint, conventional methods used to target transcription factor activity with molecules mimicking endogenous ligands fail to achieve high specificity and are limited by a lack of identification of new molecular targets. Protein-protein interactions are likely to represent one of the next major classes of therapeutic targets. Transcription factors, known to act mostly via protein-protein interaction, may well be at the forefront of this type of drug development. One hurdle in this field remains the difficulty to collate structural data into meaningful information for rational drug design. Another hurdle is the lack of chemical libraries meeting the structural requirements of protein-protein interaction disruption. As more attempts at modulating transcription factor activity are undertaken, valuable knowledge will be accumulated on the modality of action required to modulate transcription and how these findings can be applied to developing transcription factor drugs. Key discoveries will spawn into new therapeutic approaches not only as anticancer targets but also for other indications, such as those with an inflammatory component including neurodegenerative disorders, diabetes, and chronic liver and kidney diseases. PMID:25848531

  5. Ribo-Proteomics Approach to Profile RNA-Protein and Protein-Protein Interaction Networks.

    PubMed

    Yeh, Hsin-Sung; Chang, Jae-Woong; Yong, Jeongsik

    2016-01-01

    Characterizing protein-protein and protein-RNA interaction networks is a fundamental step to understanding the function of an RNA-binding protein. In many cases, these interactions are transient and highly dynamic. Therefore, capturing stable as well as transient interactions in living cells for the identification of protein-binding partners and the mapping of RNA-binding sequences is key to a successful establishment of the molecular interaction network. In this chapter, we will describe a method for capturing the molecular interactions in living cells using formaldehyde as a crosslinker and enriching a specific RNA-protein complex from cell extracts followed by mass spectrometry and Next-Gen sequencing analyses. PMID:26965265

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

    PubMed

    Traister, Alexandra; Lu, Mingliang; Coles, John G; Maynes, Jason T

    2016-06-01

    Using hearts from mice overexpressing integrin linked kinase (ILK) behind the cardiac specific promoter αMHC, we have performed immunoprecipitation and mass spectrometry to identify novel ILK protein:protein interactions that regulate cardiomyocyte activity and calcium flux. Integrin linked kinase complexes were captured from mouse heart lysates using a commercial antibody, with subsequent liquid chromatography tandem mass spectral analysis. Interacting partners were identified using the MASCOT server, and important interactions verified using reverse immunoprecipitation and mass spectrometry. All ILK interacting proteins were identified in a non-biased manner, and are stored in the ProteomeXchange Consortium via the PRIDE partner repository (reference ID PRIDE: PXD001053). The functional role of identified ILK interactions in cardiomyocyte function and arrhythmia were subsequently confirmed in human iPSC-cardiomyocytes. PMID:27408918

  7. Identification and validation of protein-protein interactions by combining co-immunoprecipitation, antigen competition, and stable isotope labeling.

    PubMed

    Sommer, Frederik; Mühlhaus, Timo; Hemme, Dorothea; Veyel, Daniel; Schroda, Michael

    2014-01-01

    Co-immunoprecipitation (coIP) in combination with mass spectrometry (MS) is a powerful tool to identify potential protein-protein interactions. However, unspecifically precipitated proteins usually result in large numbers of false-positive identifications. Here we describe a detailed protocol particularly useful in plant sciences that is based on (15)N stable isotope labeling of cells, (14)N antigen titration, and coIP/MS to distinguish true from false protein-protein interactions. PMID:25059616

  8. Novel protein-protein interaction between spermidine synthase and S-adenosylmethionine decarboxylase from Leishmania donovani.

    PubMed

    Mishra, Arjun K; Agnihotri, Pragati; Srivastava, Vijay Kumar; Pratap, J Venkatesh

    2015-01-01

    Polyamine biosynthesis pathway has long been considered an essential drug target for trypanosomatids including Leishmania. S-adenosylmethionine decarboxylase (AdoMetDc) and spermidine synthase (SpdSyn) are enzymes of this pathway that catalyze successive steps, with the product of the former, decarboxylated S-adenosylmethionine (dcSAM), acting as an aminopropyl donor for the latter enzyme. Here we have explored the possibility of and identified the protein-protein interaction between SpdSyn and AdoMetDc. The protein-protein interaction has been identified using GST pull down assay. Isothermal titration calorimetry reveals that the interaction is thermodynamically favorable. Fluorescence spectroscopy studies also confirms the interaction, with SpdSyn exhibiting a change in tertiary structure with increasing concentrations of AdoMetDc. Size exclusion chromatography suggests the presence of the complex as a hetero-oligomer. Taken together, these results suggest that the enzymes indeed form a heteromer. Computational analyses suggest that this complex differs significantly from the corresponding human complex, implying that this complex could be a better therapeutic target than the individual enzymes. PMID:25511700

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

    PubMed Central

    Loureiro, Maria Eugenia; D’Antuono, Alejandra; Levingston Macleod, Jesica M.; López, Nora

    2012-01-01

    The Arenaviridae family includes widely distributed pathogens that cause severe hemorrhagic fever in humans. Replication and packaging of their single-stranded RNA genome involve RNA recognition by viral proteins and a number of key protein-protein interactions. Viral RNA synthesis is directed by the virus-encoded RNA dependent-RNA polymerase (L protein) and requires viral RNA encapsidation by the Nucleoprotein. In addition to the role that the interaction between L and the Nucleoprotein may have in the replication process, polymerase activity appears to be modulated by the association between L and the small multifunctional Z protein. Z is also a structural component of the virions that plays an essential role in viral morphogenesis. Indeed, interaction of the Z protein with the Nucleoprotein is critical for genome packaging. Furthermore, current evidence suggests that binding between Z and the viral envelope glycoprotein complex is required for virion infectivity, and that Z homo-oligomerization is an essential step for particle assembly and budding. Efforts to understand the molecular basis of arenavirus life cycle have revealed important details on these viral protein-protein interactions that will be reviewed in this article. PMID:23170177

  10. Uncovering viral protein-protein interactions and their role in arenavirus life cycle.

    PubMed

    Loureiro, Maria Eugenia; D'Antuono, Alejandra; Levingston Macleod, Jesica M; López, Nora

    2012-09-01

    The Arenaviridae family includes widely distributed pathogens that cause severe hemorrhagic fever in humans. Replication and packaging of their single-stranded RNA genome involve RNA recognition by viral proteins and a number of key protein-protein interactions. Viral RNA synthesis is directed by the virus-encoded RNA dependent-RNA polymerase (L protein) and requires viral RNA encapsidation by the Nucleoprotein. In addition to the role that the interaction between L and the Nucleoprotein may have in the replication process, polymerase activity appears to be modulated by the association between L and the small multifunctional Z protein. Z is also a structural component of the virions that plays an essential role in viral morphogenesis. Indeed, interaction of the Z protein with the Nucleoprotein is critical for genome packaging. Furthermore, current evidence suggests that binding between Z and the viral envelope glycoprotein complex is required for virion infectivity, and that Z homo-oligomerization is an essential step for particle assembly and budding. Efforts to understand the molecular basis of arenavirus life cycle have revealed important details on these viral protein-protein interactions that will be reviewed in this article. PMID:23170177

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

    PubMed

    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 . PMID:24469380

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

    SciTech Connect

    Chowdhury, Saiful M.; Shi, Liang; Yoon, Hyunjin; Ansong, Charles; Rommereim, Leah M.; Norbeck, Angela D.; Auberry, Kenneth J.; Moore, R. J.; Adkins, Joshua N.; Heffron, Fred; Smith, Richard D.

    2009-02-10

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

  13. Phthalic acid chemical probes synthesized for protein-protein interaction analysis.

    PubMed

    Liang, Shih-Shin; Liao, Wei-Ting; Kuo, Chao-Jen; Chou, Chi-Hsien; Wu, Chin-Jen; Wang, Hui-Min

    2013-01-01

    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. PMID:23797655

  14. Essential protein identification based on essential protein-protein interaction prediction by Integrated Edge Weights.

    PubMed

    Jiang, Yuexu; Wang, Yan; Pang, Wei; Chen, Liang; Sun, Huiyan; Liang, Yanchun; Blanzieri, Enrico

    2015-07-15

    Essential proteins play a crucial role in cellular survival and development process. Experimentally, essential proteins are identified by gene knockouts or RNA interference, which are expensive and often fatal to the target organisms. Regarding this, an alternative yet important approach to essential protein identification is through computational prediction. Existing computational methods predict essential proteins based on their relative densities in a protein-protein interaction (PPI) network. Degree, betweenness, and other appropriate criteria are often used to measure the relative density. However, no matter what criterion is used, a protein is actually ordered by the attributes of this protein per se. In this research, we presented a novel computational method, Integrated Edge Weights (IEW), to first rank protein-protein interactions by integrating their edge weights, and then identified sub PPI networks consisting of those highly-ranked edges, and finally regarded the nodes in these sub networks as essential proteins. We evaluated IEW on three model organisms: Saccharomyces cerevisiae (S. cerevisiae), Escherichia coli (E. coli), and Caenorhabditis elegans (C. elegans). The experimental results showed that IEW achieved better performance than the state-of-the-art methods in terms of precision-recall and Jackknife measures. We had also demonstrated that IEW is a robust and effective method, which can retrieve biologically significant modules by its highly-ranked protein-protein interactions for S. cerevisiae, E. coli, and C. elegans. We believe that, with sufficient data provided, IEW can be used to any other organisms' essential protein identification. A website about IEW can be accessed from http://digbio.missouri.edu/IEW/index.html. PMID:25892709

  15. Investigation of Protein-Protein Interactions and Conformational Changes in Hedgehog Signaling Pathway by FRET.

    PubMed

    Fu, Lin; Lv, Xiangdong; Xiong, Yue; Zhao, Yun

    2015-01-01

    Protein-protein interactions and signal-induced protein conformational changes are fundamental molecular events that are considered as essential in modern life sciences. Among various techniques developed to study such phenomena, fluorescence resonance energy transfer (FRET) is a widely used method with many advantages in detecting these molecular events. Here, we describe the application of FRET in the mechanistic investigation of cell signal transduction, taking the example of the Hh signaling pathway, which plays a critical role in embryonic development and tissue homeostasis. A number of general guidelines as well as some key notes have been summarized as a protocol for reader's reference. PMID:26179039

  16. Small molecular weight protein-protein interaction antagonists: an insurmountable challenge?

    PubMed

    Dömling, Alexander

    2008-06-01

    Several years ago small molecular weight protein-protein interaction (PPI) antagonists were considered as the Mount Everest in drug discovery and generally regarded as too difficult to be targeted. However, recent industrial and academic research has produced a great number of new antagonists of diverse PPIs. This review structurally analyses small molecular weight PPI antagonists and their particular targets as well as tools to discover such compounds. Besides general discussions there will be a focus on the PPI p53/mdm2. PMID:18501203

  17. [Recent advances in the techniques of protein-protein interaction study].

    PubMed

    Wang, Ming-Qiang; Wu, Jin-Xia; Zhang, Yu-Hong; Han, Ning; Bian, Hong-Wu; Zhu, Mu-Yuan

    2013-11-01

    Protein-protein interactions play key roles in the development of organisms and the response to biotic and abiotic stresses. Several wet-lab methods have been developed to study this challenging area,including yeast two-hybrid system, tandem affinity purification, Co-immunoprecipitation, GST Pull-down, bimolecular fluorescence complementation, fluorescence resonance energy transfer and surface plasmon resonance analysis. In this review, we discuss theoretical principles and relative advantages and disvantages of these techniques,with an emphasis on recent advances to compensate for limitations. PMID:24579310

  18. Protein-protein interactions in plant mitogen-activated protein kinase cascades.

    PubMed

    Zhang, Tong; Chen, Sixue; Harmon, Alice C

    2016-02-01

    Mitogen-activated protein kinases (MAPKs) form tightly controlled signaling cascades that play essential roles in plant growth, development, and defense. However, the molecular mechanisms underlying MAPK cascades are still elusive, due largely to our poor understanding of how they relay the signals. Extensive effort has been devoted to characterization of MAPK-substrate interactions to illustrate phosphorylation-based signaling. The diverse MAPK substrates identified also shed light on how spatiotemporal-specific protein-protein interactions function in distinct MAPK cascade-mediated biological processes. This review surveys various technologies used for characterizing MAPK-substrate interactions and presents case studies of MPK4 and MPK6, highlighting the multiple functions of MAPKs. Mass spectrometry-based approaches in identifying MAPK-interacting proteins are emphasized due to their increasing utility and effectiveness. The potential for using MAPKs and their substrates in enhancing plant stress tolerance is also discussed. PMID:26646897

  19. PIPE: a protein-protein interaction passage extraction module for BioCreative challenge.

    PubMed

    Chang, Yung-Chun; Chu, Chun-Han; Su, Yu-Chen; Chen, Chien Chin; Hsu, Wen-Lian

    2016-01-01

    Identifying the interactions between proteins mentioned in biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this article, we propose PIPE, an interaction pattern generation module used in the Collaborative Biocurator Assistant Task at BioCreative V (http://www.biocreative.org/) to capture frequent protein-protein interaction (PPI) patterns within text. We also present an interaction pattern tree (IPT) kernel method that integrates the PPI patterns with convolution tree kernel (CTK) to extract PPIs. Methods were evaluated on LLL, IEPA, HPRD50, AIMed and BioInfer corpora using cross-validation, cross-learning and cross-corpus evaluation. Empirical evaluations demonstrate that our method is effective and outperforms several well-known PPI extraction methods. DATABASE URL. PMID:27524807

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

    PubMed Central

    Hwang, Woochang; Cho, Young-Rae; Zhang, Aidong; Ramanathan, Murali

    2006-01-01

    Background The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM. Results STM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be effective at detecting clusters with a diverse range of interaction structures that are significant on measures of biological relevance. The STM approach is compared to six competing approaches including the maximum clique, quasi-clique, minimum cut, betweeness cut and Markov Clustering (MCL) algorithms. The clusters obtained by each technique are compared for enrichment of biological function. STM generates larger clusters and the clusters identified have p-values that are approximately 125-fold better than the other methods on biological function. An important strength of STM is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches. Conclusion STM outperforms competing approaches and is capable of effectively detecting both densely and sparsely connected, biologically relevant functional modules with fewer discards. PMID:17147822

  1. Predicting Abdominal Aortic Aneurysm Target Genes by Level-2 Protein-Protein Interaction

    PubMed Central

    Fu, Yi; Cui, Qinghua; Kong, Wei

    2015-01-01

    Abdominal aortic aneurysm (AAA) is frequently lethal and has no effective pharmaceutical treatment, posing a great threat to human health. Previous bioinformatics studies of the mechanisms underlying AAA relied largely on the detection of direct protein-protein interactions (level-1 PPI) between the products of reported AAA-related genes. Thus, some proteins not suspected to be directly linked to previously reported genes of pivotal importance to AAA might have been missed. In this study, we constructed an indirect protein-protein interaction (level-2 PPI) network based on common interacting proteins encoded by known AAA-related genes and successfully predicted previously unreported AAA-related genes using this network. We used four methods to test and verify the performance of this level-2 PPI network: cross validation, human AAA mRNA chip array comparison, literature mining, and verification in a mouse CaPO4 AAA model. We confirmed that the new level-2 PPI network is superior to the original level-1 PPI network and proved that the top 100 candidate genes predicted by the level-2 PPI network shared similar GO functions and KEGG pathways compared with positive genes. PMID:26496478

  2. Ex vivo identification of protein-protein interactions involving the dopamine transporter.

    PubMed

    Hadlock, Gregory C; Nelson, Chad C; Baucum, Anthony J; Hanson, Glen R; Fleckenstein, Annette E

    2011-03-30

    The dopamine (DA) transporter (DAT) is a key regulator of dopaminergic signaling as it mediates the reuptake of extrasynaptic DA and thereby terminates dopaminergic signaling. Emerging evidence indicates that DAT function is influenced through interactions with other proteins. The current report describes a method to identify such interactions following DAT immunoprecipitation from a rat striatal synaptosomal preparation. This subcellular fraction was selected since DAT function is often determined ex vivo by measuring DA uptake in this preparation and few reports investigating DAT-protein interactions have utilized this preparation. Following SDS-PAGE and colloidal Coomassie staining, selected protein bands from a DAT-immunoprecipitate were excised, digested with trypsin, extracted, and analyzed by liquid chromatography tandem mass spectrometry (LC/MS/MS). From the analysis of the tryptic peptides, several proteins were identified including DAT, Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) β, CaMKII δ, protein kinase C (PKC) β, and PKC γ. Co-immunoprecipitation of PKC, CaMKII, and protein interacting with C kinase-1 with DAT was confirmed by Western blotting. Thus, the present study highlights a method to immunoprecipitate DAT and to identify co-immunoprecipitating proteins using LC/MS/MS and Western blotting. This method can be utilized to evaluate DAT protein-protein interactions but also to assess interactions involving other synaptic proteins. Ex vivo identification of protein-protein interactions will provide new insight into the function and regulation of a variety of synaptic, membrane-associated proteins, including DAT. PMID:21291912

  3. Confirmation of a Protein-Protein Interaction in the Pantothenate Biosynthetic Pathway by Using Sortase-Mediated Labelling.

    PubMed

    Morrison, Philip M; Balmforth, Matthew R; Ness, Samuel W; Williamson, Daniel J; Rugen, Michael D; Turnbull, W Bruce; Webb, Michael E

    2016-04-15

    High-throughput studies have been widely used to identify protein-protein interactions; however, few of these candidate interactions have been confirmed in vitro. We have used a combination of isothermal titration calorimetry and fluorescence anisotropy to screen candidate interactions within the pantothenate biosynthetic pathway. In particular, we observed no interaction between the next enzyme in the pathway, pantothenate synthetase (PS), and aspartate decarboxylase, but did observe an interaction between PS and the putative Nudix hydrolase, YfcD. Confirmation of the interaction by fluorescence anisotropy was dependent upon labelling an adventitiously formed glycine on the protein N-terminal affinity purification tag by using Sortase. Subsequent formation of the protein-protein complex led to apparent restriction of the dynamics of this tag, thus suggesting that this approach could be generally applied to a subset of other protein-protein interaction complexes. PMID:26818742

  4. [A novel biological pathway expansion method based on the knowledge of protein-protein interactions].

    PubMed

    Zhao, Xiaolei; Zuo, Xiaoyu; Qin, Jiheng; Liang, Yan; Zhang, Naizun; Luan, Yizhao; Rao, Shaoqi

    2014-04-01

    Biological pathways have been widely used in gene function studies; however, the current knowledge for biological pathways is per se incomplete and has to be further expanded. Bioinformatics prediction provides us a cheap but effective way for pathway expansion. Here, we proposed a novel method for biological pathway prediction, by intergrating prior knowledge of protein?protein interactions and Gene Ontology (GO) database. First, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to which the interacting neighbors of a targe gene (at the level of protein?protein interaction) belong were chosen as the candidate pathways. Then, the pathways to which the target gene belong were determined by testing whether the genes in the candidate pathways were enriched in the GO terms to which the target gene were annotated. The protein?protein interaction data obtained from the Human Protein Reference Database (HPRD) and Biological General Repository for Interaction Datasets (BioGRID) were respectively used to predict the pathway attribution(s) of the target gene. The results demanstrated that both the average accuracy (the ratio of the correctly predicted pathways to the totally pathways to which all the target genes were annotated) and the relative accuracy (of the genes with at least one annotated pathway being successful predicted, the percentage of the genes with all the annotated pathways being correctly predicted) for pathway predictions were increased with the number of the interacting neighbours. When the number of interacting neighbours reached 22, the average accuracy was 96.2% (HPRD) and 96.3% (BioGRID), respectively, and the relative accuracy was 93.3% (HPRD) and 84.1% (BioGRID), respectively. Further validation analysis of 89 genes whose pathway knowledge was updated in a new database release indicated that 50 genes were correctly predicted for at least one updated pathway, and 43 genes were accurately predicted for all the updated pathways, giving an

  5. Prediction of protein-protein interaction network using a multi-objective optimization approach.

    PubMed

    Chowdhury, Archana; Rakshit, Pratyusha; Konar, Amit

    2016-06-01

    Protein-Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score. PMID:26846814

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

    NASA Astrophysics Data System (ADS)

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

    2012-10-01

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

  7. Characterization of Protein Complexes and Subcomplexes in Protein-Protein Interaction Databases

    PubMed Central

    Zaki, Nazar; Mohamed, Elfadil A.; Mora, Antonio

    2015-01-01

    The identification and characterization of protein complexes implicated in protein-protein interaction data are crucial to the understanding of the molecular events under normal and abnormal physiological conditions. This paper provides a novel characterization of subcomplexes in protein interaction databases, stressing definition and representation issues, quantification, biological validation, network metrics, motifs, modularity, and gene ontology (GO) terms. The paper introduces the concept of “nested group” as a way to represent subcomplexes and estimates that around 15% of those nested group with the higher Jaccard index may be a result of data artifacts in protein interaction databases, while a number of them can be found in biologically important modular structures or dynamic structures. We also found that network centralities, enrichment in essential proteins, GO terms related to regulation, imperfect 5-clique motifs, and higher GO homogeneity can be used to identify proteins in nested complexes. PMID:25722891

  8. Fluorescence lifetime imaging microscopy (FLIM) to quantify protein-protein interactions inside cells.

    PubMed

    Duncan, R R

    2006-11-01

    Recent developments in cellular imaging spectroscopy now permit the minimally invasive study of protein dynamics inside living cells. These advances are of interest to cell biologists, as proteins rarely act in isolation, but rather in concert with others in forming cellular machinery. Until recently, all protein interactions had to be determined in vitro using biochemical approaches: this biochemical legacy has provided cell biologists with the basis to test defined protein-protein interactions not only inside cells, but now also with high spatial resolution. These techniques can detect and quantify protein behaviours down to the single-molecule level, all inside living cells. More recent developments in TCSPC (time-correlated single-photon counting) imaging are now also driving towards being able to determine protein interaction rates with similar spatial resolution, and together, these experimental advances allow investigators to perform biochemical experiments inside living cells. PMID:17052173

  9. Overview of the protein-protein interaction annotation extraction task of BioCreative II

    PubMed Central

    Krallinger, Martin; Leitner, Florian; Rodriguez-Penagos, Carlos; Valencia, Alfonso

    2008-01-01

    Background: The biomedical literature is the primary information source for manual protein-protein interaction annotations. Text-mining systems have been implemented to extract binary protein interactions from articles, but a comprehensive comparison between the different techniques as well as with manual curation was missing. Results: We designed a community challenge, the BioCreative II protein-protein interaction (PPI) task, based on the main steps of a manual protein interaction annotation workflow. It was structured into four distinct subtasks related to: (a) detection of protein interaction-relevant articles; (b) extraction and normalization of protein interaction pairs; (c) retrieval of the interaction detection methods used; and (d) retrieval of actual text passages that provide evidence for protein interactions. A total of 26 teams submitted runs for at least one of the proposed subtasks. In the interaction article detection subtask, the top scoring team reached an F-score of 0.78. In the interaction pair extraction and mapping to SwissProt, a precision of 0.37 (with recall of 0.33) was obtained. For associating articles with an experimental interaction detection method, an F-score of 0.65 was achieved. As for the retrieval of the PPI passages best summarizing a given protein interaction in full-text articles, 19% of the submissions returned by one of the runs corresponded to curator-selected sentences. Curators extracted only the passages that best summarized a given interaction, implying that many of the automatically extracted ones could contain interaction information but did not correspond to the most informative sentences. Conclusion: The BioCreative II PPI task is the first attempt to compare the performance of text-mining tools specific for each of the basic steps of the PPI extraction pipeline. The challenges identified range from problems in full-text format conversion of articles to difficulties in detecting interactor protein pairs and then

  10. Protein-Protein Interactions, Not Substrate Recognition, Dominate the Turnover of Chimeric Assembly Line Polyketide Synthases.

    PubMed

    Klaus, Maja; Ostrowski, Matthew P; Austerjost, Jonas; Robbins, Thomas; Lowry, Brian; Cane, David E; Khosla, Chaitan

    2016-07-29

    The potential for recombining intact polyketide synthase (PKS) modules has been extensively explored. Both enzyme-substrate and protein-protein interactions influence chimeric PKS activity, but their relative contributions are unclear. We now address this issue by studying a library of 11 bimodular and 8 trimodular chimeric PKSs harboring modules from the erythromycin, rifamycin, and rapamycin synthases. Although many chimeras yielded detectable products, nearly all had specific activities below 10% of the reference natural PKSs. Analysis of selected bimodular chimeras, each with the same upstream module, revealed that turnover correlated with the efficiency of intermodular chain translocation. Mutation of the acyl carrier protein (ACP) domain of the upstream module in one chimera at a residue predicted to influence ketosynthase-ACP recognition led to improved turnover. In contrast, replacement of the ketoreductase domain of the upstream module by a paralog that produced the enantiomeric ACP-bound diketide caused no changes in processing rates for each of six heterologous downstream modules compared with those of the native diketide. Taken together, these results demonstrate that protein-protein interactions play a larger role than enzyme-substrate recognition in the evolution or design of catalytically efficient chimeric PKSs. PMID:27246853

  11. Automated High-Throughput Fluorescence Lifetime Imaging Microscopy to Detect Protein-Protein Interactions.

    PubMed

    Guzmán, Camilo; Oetken-Lindholm, Christina; Abankwa, Daniel

    2016-04-01

    Fluorescence resonance energy transfer (FRET) is widely used to study conformational changes of macromolecules and protein-protein, protein-nucleic acid, and protein-small molecule interactions. FRET biosensors can serve as valuable secondary assays in drug discovery and for target validation in mammalian cells. Fluorescence lifetime imaging microscopy (FLIM) allows precise quantification of the FRET efficiency in intact cells, as FLIM is independent of fluorophore concentration, detection efficiency, and fluorescence intensity. We have developed an automated FLIM system using a commercial frequency domain FLIM attachment (Lambert Instruments) for wide-field imaging. Our automated FLIM system is capable of imaging and analyzing up to 50 different positions of a slide in less than 4 min, or the inner 60 wells of a 96-well plate in less than 20 min. Automation is achieved using a motorized stage and controller (Prior Scientific) coupled with a Zeiss Axio Observer body and full integration into the Lambert Instruments FLIM acquisition software. As an application example, we analyze the interaction of the oncoprotein Ras and its effector Raf after drug treatment. In conclusion, our automated FLIM imaging system requires only commercial components and may therefore allow for a broader use of this technique in chemogenomics projects. PMID:26384400

  12. Deep-tissue multiphoton fluorescence lifetime microscopy for intravital imaging of protein-protein interactions

    NASA Astrophysics Data System (ADS)

    Fruhwirth, G. O.; Matthews, D. R.; Brock, A.; Keppler, M.; Vojnovic, B.; Ng, T.; Ameer-Beg, S.

    2009-02-01

    Fluorescent lifetime imaging microscopy (FLIM) has proven to be a valuable tool in beating the Rayleigh criterion for light microscopy by measuring Förster resonance energy transfer (FRET) between two fluorophores. Applying multiphoton FLIM, we previously showed in a human breast cancer cell line that recycling of a membrane receptorgreen fluorescent protein fusion is enhanced concomitantly with the formation of a receptor:protein kinase C α complex in the endosomal compartment. We have extended this established technique to probe direct protein-protein interactions also in vivo. Therefore, we used various expressible fluorescent tags fused to membrane receptor molecules in order to generate stable two-colour breast carcinoma cell lines via controlled retroviral infection. We used these cell lines for establishing a xenograft tumour model in immune-compromised Nude mice. Using this animal model in conjunction with scanning Ti:Sapphire laser-based two-photon excitation, we established deep-tissue multiphoton FLIM in vivo. For the first time, this novel technique enables us to directly assess donor fluorescence lifetime changes in vivo and we show the application of this method for intravital imaging of direct protein-protein interactions.

  13. Extracting gene function from protein-protein interactions using Quantitative BAC InteraCtomics (QUBIC).

    PubMed

    Hubner, Nina C; Mann, Matthias

    2011-04-01

    Large-scale proteomic screens are increasingly employed for placing genes into specific pathways. Therefore generic methods providing a physiological context for protein-protein interaction studies are of great interest. In recent years many protein-protein interactions have been determined by affinity purification followed by mass spectrometry (AP-MS). Among many different AP-MS approaches, the recently developed Quantitative BAC InteraCtomics (QUBIC) approach is particularly attractive as it uses tagged, full-length baits that are expressed under endogenous control. For QUBIC large cell line collections expressing tagged proteins from BAC transgenes or gene trap loci have been developed and are freely available. Here we describe detailed workflows on how to obtain specific protein binding partners with high confidence under physiological conditions. The methods are based on fast, streamlined and generic purification procedures followed by single run liquid chromatography-mass spectrometric analysis. Quantification is achieved either by the stable isotope labeling of amino acids in cell culture (SILAC) method or by a 'label-free' procedure. In either case data analysis is performed by using the freely available MaxQuant environment. The QUBIC approach enables biologists with access to high resolution mass spectrometry to perform small and large-scale protein interactome mappings. PMID:21184827

  14. Protein-Protein Interaction Antagonists as Novel Inhibitors of Non-Canonical Polyubiquitylation

    PubMed Central

    Sanclimens, Glòria; Moure, Alejandra; Masip, Isabel; González-Ruiz, Domingo; Rubio, Nuria; Crosas, Bernat; Meca-Cortés, Óscar; Loukili, Noureddine; Plans, Vanessa; Morreale, Antonio; Blanco, Jerónimo; Ortiz, Angel R.; Messeguer, Àngel; Thomson, Timothy M.

    2010-01-01

    Background Several pathways that control cell survival under stress, namely RNF8-dependent DNA damage recognition and repair, PCNA-dependent DNA damage tolerance and activation of NF-κB by extrinsic signals, are regulated by the tagging of key proteins with lysine 63-based polyubiquitylated chains, catalyzed by the conserved ubiquitin conjugating heterodimeric enzyme Ubc13-Uev. Methodology/Principal Findings By applying a selection based on in vivo protein-protein interaction assays of compounds from a combinatorial chemical library followed by virtual screening, we have developed small molecules that efficiently antagonize the Ubc13-Uev1 protein-protein interaction, inhibiting the enzymatic activity of the heterodimer. In mammalian cells, they inhibit lysine 63-type polyubiquitylation of PCNA, inhibit activation of NF-κB by TNF-α and sensitize tumor cells to chemotherapeutic agents. One of these compounds significantly inhibited invasiveness, clonogenicity and tumor growth of prostate cancer cells. Conclusions/Significance This is the first development of pharmacological inhibitors of non-canonical polyubiquitylation that show that these compounds produce selective biological effects with potential therapeutic applications. PMID:20613989

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

    PubMed Central

    Chakraborty, Sandip

    2016-01-01

    Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins) are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons) tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes' adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another. PMID:27119079

  16. PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction

    DOE PAGESBeta

    Yao, Jianzhuang; Guo, Hong; Yang, Xiaohan

    2015-01-01

    Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using anmore » assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed that PPCM significantly improved the PPI prediction accuracy over the corresponding individual classifiers. We found that additional classifiers incorporated into PPCM could lead to further improvement in the PPI prediction accuracy. Furthermore, cross species PPCM could achieve competitive and even better prediction accuracy compared to the single species PPCM. This study established a robust pipeline for PPI prediction by integrating multiple classifiers using Random Forests algorithm. This pipeline will be useful for predicting PPI in nonmodel species.« less

  17. DUF581 Is Plant Specific FCS-Like Zinc Finger Involved in Protein-Protein Interaction

    PubMed Central

    K, Muhammed Jamsheer; Laxmi, Ashverya

    2014-01-01

    Zinc fingers are a ubiquitous class of protein domain with considerable variation in structure and function. Zf-FCS is a highly diverged group of C2-C2 zinc finger which is present in animals, prokaryotes and viruses, but not in plants. In this study we identified that a plant specific domain of unknown function, DUF581 is a zf-FCS type zinc finger. Based on HMM-HMM comparison and signature motif similarity we named this domain as FCS-Like Zinc finger (FLZ) domain. A genome wide survey identified that FLZ domain containing genes are bryophytic in origin and this gene family is expanded in spermatophytes. Expression analysis of selected FLZ gene family members of A. thaliana identified an overlapping expression pattern suggesting a possible redundancy in their function. Unlike the zf-FCS domain, the FLZ domain found to be highly conserved in sequence and structure. Using a combination of bioinformatic and protein-protein interaction tools, we identified that FLZ domain is involved in protein-protein interaction. PMID:24901469

  18. Neutral evolution of Protein-protein interactions: a computational study using simple models

    PubMed Central

    Noirel, Josselin; Simonson, Thomas

    2007-01-01

    Background Protein-protein interactions are central to cellular organization, and must have appeared at an early stage of evolution. To understand better their role, we consider a simple model of protein evolution and determine the effect of an explicit selection for Protein-protein interactions. Results In the model, viable sequences all have the same fitness, following the neutral evolution theory. A very simple, two-dimensional lattice representation of the protein structures is used, and the model only considers two kinds of amino acids: hydrophobic and polar. With these approximations, exact calculations are performed. The results do not depend too strongly on these assumptions, since a model using a 3D, off-lattice representation of the proteins gives results in qualitative agreement with the 2D one. With both models, the evolutionary dynamics lead to a steady state population that is enriched in sequences that dimerize with a high affinity, well beyond the minimal level needed to survive. Correspondingly, sequences close to the viability threshold are less abundant in the steady state, being subject to a larger proportion of lethal mutations. The set of viable sequences has a "funnel" shape, consistent with earlier studies: sequences that are highly populated in the steady state are "close" to each other (with proximity being measured by the number of amino acids that differ). Conclusion This bias in the the steady state sequences should lead to an increased resistance of the population to environmental change and an increased ability to evolve. PMID:18021454

  19. Protein-protein interactions in intracellular Ca2+-release channel function.

    PubMed Central

    MacKrill, J J

    1999-01-01

    Release of Ca2+ ions from intracellular stores can occur via two classes of Ca2+-release channel (CRC) protein, the inositol 1,4, 5-trisphosphate receptors (InsP3Rs) and the ryanodine receptors (RyRs). Multiple isoforms and subtypes of each CRC class display distinct but overlapping distributions within mammalian tissues. InsP3Rs and RyRs interact with a plethora of accessory proteins which modulate the activity of their intrinsic channels. Although many aspects of CRC structure and function have been reviewed in recent years, the properties of proteins with which they interact has not been comprehensively surveyed, despite extensive current research on the roles of these modulators. The aim of this article is to review the regulation of CRC activity by accessory proteins and, wherever possible, to outline the structural details of such interactions. The CRCs are large transmembrane proteins, with the bulk of their structure located cytoplasmically. Intra- and inter-complex protein-protein interactions between these cytoplasmic domains also regulate CRC function. Some accessory proteins modulate channel activity of all CRC subtypes characterized, whereas other have class- or even isoform-specific effects. Certain accessory proteins exert both direct and indirect forms of regulation on CRCs, occasionally with opposing effects. Others are themselves modulated by changes in Ca2+ concentration, thereby participating in feedback mechanisms acting on InsP3R and RyR activity. CRCs are therefore capable of integrating numerous signalling events within a cell by virtue of such protein-protein interactions. Consequently, the functional properties of InsP3Rs and RyRs within particular cells and subcellular domains are 'customized' by the accessory proteins present. PMID:9895277

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

    PubMed Central

    2010-01-01

    Background Protein-protein interactions are crucially important for cellular processes. Knowledge of these interactions improves the understanding of cell cycle, metabolism, signaling, transport, and secretion. Information about interactions can hint at molecular causes of diseases, and can provide clues for new therapeutic approaches. Several (usually expensive and time consuming) experimental methods can probe protein - protein interactions. Data sets, derived from such experiments make the development of prediction methods feasible, and make the creation of protein-protein interaction network predicting tools possible. Methods Here we report the development of a simple open source program module (OpenPPI_predictor) that can generate a putative protein-protein interaction network for target genomes. This tool uses the orthologous interactome network data from a related, experimentally studied organism. Results Results from our predictions can be visualized using the Cytoscape visualization software, and can be piped to downstream processing algorithms. We have employed our program to predict protein-protein interaction network for the human parasite roundworm Brugia malayi, using interactome data from the free living nematode Caenorhabditis elegans. Availability The OpenPPI_predictor source code is available from http://tools.neb.com/~posfai/. PMID:20684769

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

    PubMed Central

    Chu, Liang-Hui; Chen, Bor-Sen

    2008-01-01

    Background Cancer is caused by genetic abnormalities, such as mutations of oncogenes or tumor suppressor genes, which alter downstream signal transduction pathways and protein-protein interactions. Comparisons of the interactions of proteins in cancerous and normal cells can shed light on the mechanisms of carcinogenesis. Results We constructed initial networks of protein-protein interactions involved in the apoptosis of cancerous and normal cells by use of two human yeast two-hybrid data sets and four online databases. Next, we applied a nonlinear stochastic model, maximum likelihood parameter estimation, and Akaike Information Criteria (AIC) to eliminate false-positive protein-protein interactions in our initial protein interaction networks by use of microarray data. Comparisons of the networks of apoptosis in HeLa (human cervical carcinoma) cells and in normal primary lung fibroblasts provided insight into the mechanism of apoptosis and allowed identification of potential drug targets. The potential targets include BCL2, caspase-3 and TP53. Our comparison of cancerous and normal cells also allowed derivation of several party hubs and date hubs in the human protein-protein interaction networks involved in caspase activation. Conclusion Our method allows identification of cancer-perturbed protein-protein interactions involved in apoptosis and identification of potential molecular targets for development of anti-cancer drugs. PMID:18590547

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

    PubMed

    Sultana, Azmiri; Lee, Jeffrey E

    2015-01-01

    Biolayer interferometry (BLI) is a simple, optical dip-and-read system useful for measuring interactions between proteins, peptides, nucleic acids, small molecules, and/or lipids in real time. In BLI, a biomolecular bait is immobilized on a matrix at the tip of a fiber-optic sensor. The binding between the immobilized ligand and another molecule in an analyte solution produces a change in optical thickness at the tip and results in a wavelength shift proportional to binding. BLI provides direct binding affinities and rates of association and dissociation. This unit describes an efficient approach using streptavidin-based BLI to analyze DNA-protein and protein-protein interactions. A quantitative set of equilibrium binding affinities (K(d)) and rates of association and dissociation (k(a)/k(d)) can be measured in minutes using nanomole quantities of sample. PMID:25640894

  3. Inhibition of α-helix-mediated protein-protein interactions using designed molecules

    NASA Astrophysics Data System (ADS)

    Azzarito, Valeria; Long, Kérya; Murphy, Natasha S.; Wilson, Andrew J.

    2013-03-01

    Inhibition of protein-protein interactions (PPIs) represents a significant challenge because it is unclear how they can be effectively and selectively targeted using small molecules. Achieving this goal is critical given the defining role of these interactions in biological processes. A rational approach to inhibitor design based on the secondary structure at the interface is the focus of much research, and different classes of designed ligands have emerged, some of which effectively and selectively disrupt targeted PPIs. This Review discusses the relevance of PPIs and, in particular, the importance of α-helix-mediated PPIs to chemical biology and drug discovery with a focus on designing inhibitors, including constrained peptides, foldamers and proteomimetic-derived ligands. In doing so, key challenges and major advances in developing generic approaches for the elaboration of PPI inhibitors are highlighted. The challenges faced in developing such ligands as drug leads -- and how criteria applied to these may differ from conventional small-molecule drugs -- are summarized.

  4. Prediction of human protein-protein interaction by a domain-based approach.

    PubMed

    Zhang, Xiaopan; Jiao, Xiong; Song, Jie; Chang, Shan

    2016-05-01

    Protein-protein interactions (PPIs) are vital to a number of biological processes. With computational methods, plenty of domain information can help us to predict and assess PPIs. In this study, we proposed a domain-based approach for the prediction of human PPIs based on the interactions between the proteins and the domains. In this method, an optimizing model was built with the information from InterDom, 3did, DOMINE and Pfam databases. With this model, for 147 proteins in the integrin adhesome PPI network, 736 probable PPIs have been predicted, and the corresponding confidence probabilities of these PPIs were also calculated. It provides an opportunity to visualize the PPIs by using network graphs, which were constructed with Cytoscape, so that we can indicate underlying pathways possible. PMID:26925814

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

    PubMed

    Li, Lishuang; Zhang, Panpan; Zheng, Tianfu; Zhang, Hongying; Jiang, Zhenchao; Huang, Degen

    2014-01-01

    Protein-Protein Interaction (PPI) extraction is an important task in the biomedical information extraction. Presently, many machine learning methods for PPI extraction have achieved promising results. However, the performance is still not satisfactory. One reason is that the semantic resources were basically ignored. In this paper, we propose a multiple-kernel learning-based approach to extract PPIs, combining the feature-based kernel, tree kernel and semantic kernel. Particularly, we extend the shortest path-enclosed tree kernel (SPT) by a dynamic extended strategy to retrieve the richer syntactic information. Our semantic kernel calculates the protein-protein pair similarity and the context similarity based on two semantic resources: WordNet and Medical Subject Heading (MeSH). We evaluate our method with Support Vector Machine (SVM) and achieve an F-score of 69.40% and an AUC of 92.00%, which show that our method outperforms most of the state-of-the-art systems by integrating semantic information. PMID:24622773

  6. Integrating Semantic Information into Multiple Kernels for Protein-Protein Interaction Extraction from Biomedical Literatures

    PubMed Central

    Li, Lishuang; Zhang, Panpan; Zheng, Tianfu; Zhang, Hongying; Jiang, Zhenchao; Huang, Degen

    2014-01-01

    Protein-Protein Interaction (PPI) extraction is an important task in the biomedical information extraction. Presently, many machine learning methods for PPI extraction have achieved promising results. However, the performance is still not satisfactory. One reason is that the semantic resources were basically ignored. In this paper, we propose a multiple-kernel learning-based approach to extract PPIs, combining the feature-based kernel, tree kernel and semantic kernel. Particularly, we extend the shortest path-enclosed tree kernel (SPT) by a dynamic extended strategy to retrieve the richer syntactic information. Our semantic kernel calculates the protein-protein pair similarity and the context similarity based on two semantic resources: WordNet and Medical Subject Heading (MeSH). We evaluate our method with Support Vector Machine (SVM) and achieve an F-score of 69.40% and an AUC of 92.00%, which show that our method outperforms most of the state-of-the-art systems by integrating semantic information. PMID:24622773

  7. Protein-Protein Interaction Inhibition (2P2I)-Oriented Chemical Library Accelerates Hit Discovery.

    PubMed

    Milhas, Sabine; Raux, Brigitt; Betzi, Stéphane; Derviaux, Carine; Roche, Philippe; Restouin, Audrey; Basse, Marie-Jeanne; Rebuffet, Etienne; Lugari, Adrien; Badol, Marion; Kashyap, Rudra; Lissitzky, Jean-Claude; Eydoux, Cécilia; Hamon, Véronique; Gourdel, Marie-Edith; Combes, Sébastien; Zimmermann, Pascale; Aurrand-Lions, Michel; Roux, Thomas; Rogers, Catherine; Müller, Susanne; Knapp, Stefan; Trinquet, Eric; Collette, Yves; Guillemot, Jean-Claude; Morelli, Xavier

    2016-08-19

    Protein-protein interactions (PPIs) represent an enormous source of opportunity for therapeutic intervention. We and others have recently pinpointed key rules that will help in identifying the next generation of innovative drugs to tackle this challenging class of targets within the next decade. We used these rules to design an oriented chemical library corresponding to a set of diverse "PPI-like" modulators with cores identified as privileged structures in therapeutics. In this work, we purchased the resulting 1664 structurally diverse compounds and evaluated them on a series of representative protein-protein interfaces with distinct "druggability" potential using homogeneous time-resolved fluorescence (HTRF) technology. For certain PPI classes, analysis of the hit rates revealed up to 100 enrichment factors compared with nonoriented chemical libraries. This observation correlates with the predicted "druggability" of the targets. A specific focus on selectivity profiles, the three-dimensional (3D) molecular modes of action resolved by X-ray crystallography, and the biological activities of identified hits targeting the well-defined "druggable" bromodomains of the bromo and extraterminal (BET) family are presented as a proof-of-concept. Overall, our present study illustrates the potency of machine learning-based oriented chemical libraries to accelerate the identification of hits targeting PPIs. A generalization of this method to a larger set of compounds will accelerate the discovery of original and potent probes for this challenging class of targets. PMID:27219844

  8. Protein-protein interaction analysis highlights additional loci of interest for multiple sclerosis.

    PubMed

    Ragnedda, Giammario; Disanto, Giulio; Giovannoni, Gavin; Ebers, George C; Sotgiu, Stefano; Ramagopalan, Sreeram V

    2012-01-01

    Genetic factors play an important role in determining the risk of multiple sclerosis (MS). The strongest genetic association in MS is located within the major histocompatibility complex class II region (MHC), but more than 50 MS loci of modest effect located outside the MHC have now been identified. However, the relative candidate genes that underlie these associations and their functions are largely unknown. We conducted a protein-protein interaction (PPI) analysis of gene products coded in loci recently reported to be MS associated at the genome-wide significance level and in loci suggestive of MS association. Our aim was to identify which suggestive regions are more likely to be truly associated, which genes are mostly implicated in the PPI network and their expression profile. From three recent independent association studies, SNPs were considered and divided into significant and suggestive depending on the strength of the statistical association. Using the Disease Association Protein-Protein Link Evaluator tool we found that direct interactions among genetic products were significantly higher than expected by chance when considering both significant regions alone (p<0.0002) and significant plus suggestive (p<0.007). The number of genes involved in the network was 43. Of these, 23 were located within suggestive regions and many of them directly interacted with proteins coded within significant regions. These included genes such as SYK, IL-6, CSF2RB, FCLR3, EIF4EBP2 and CHST12. Using the gene portal BioGPS, we tested the expression of these genes in 24 different tissues and found the highest values among immune-related cells as compared to non-immune tissues (p<0.001). A gene ontology analysis confirmed the immune-related functions of these genes. In conclusion, loci currently suggestive of MS association interact with and have similar expression profiles and function as those significantly associated, highlighting the fact that more common variants remain to be

  9. NetworkAnalyst--integrative approaches for protein-protein interaction network analysis and visual exploration.

    PubMed

    Xia, Jianguo; Benner, Maia J; Hancock, Robert E W

    2014-07-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required--identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. PMID:24861621

  10. Detecting Protein-Protein Interactions in Vesicular Stomatitis Virus Using a Cytoplasmic Yeast Two Hybrid System

    PubMed Central

    Moerdyk-Schauwecker, Megan; DeStephanis, Darla; Hastie, Eric; Grdzelishvili, Valery Z.

    2011-01-01

    Summary Protein-protein interactions play an important role in many virus-encoded functions and in virus-host interactions. While a “classical” yeast two-hybrid system (Y2H) is one of the most common techniques to detect such interactions, it has a number of limitations, including a requirement for the proteins of interest to be relocated to the nucleus. Modified Y2H, such as the Sos recruitment system (SRS), which detect interactions occurring in the cytoplasm rather than the nucleus, allow proteins from viruses replicating in the cytoplasm to be tested in a more natural context. In this study, a SRS was used to detect interactions involving proteins from vesicular stomatitis virus (VSV), a prototypic non-segmented negative strand RNA (NNS) virus. All five full-length VSV proteins, as well as several truncated proteins, were screened against each other. Using the SRS, most interactions demonstrated previously involving VSV phosphoprotein, nucleocapsid (N) and large polymerase proteins were confirmed independently, while difficulties were encountered using the membrane associated matrix and glycoproteins. A human cDNA library was also screened against VSV N protein and one cellular protein, SFRS18, was identified which interacted with N in this context. The system presented can be redesigned easily for studies in other less tractable NNS viruses. PMID:21320532

  11. PathPPI: an integrated dataset of human pathways and protein-protein interactions.

    PubMed

    Tang, HaiLin; Zhong, Fan; Liu, Wei; He, FuChu; Xie, HongWei

    2015-06-01

    Integration of pathway and protein-protein interaction (PPI) data can provide more information that could lead to new biological insights. PPIs are usually represented by a simple binary model, whereas pathways are represented by more complicated models. We developed a series of rules for transforming protein interactions from pathway to binary model, and the protein interactions from seven pathway databases, including PID, BioCarta, Reactome, NetPath, INOH, SPIKE and KEGG, were transformed based on these rules. These pathway-derived binary protein interactions were integrated with PPIs from other five PPI databases including HPRD, IntAct, BioGRID, MINT and DIP, to develop integrated dataset (named PathPPI). More detailed interaction type and modification information on protein interactions can be preserved in PathPPI than other existing datasets. Comparison analysis results indicate that most of the interaction overlaps values (O AB) among these pathway databases were less than 5%, and these databases must be used conjunctively. The PathPPI data was provided at http://proteomeview.hupo.org.cn/PathPPI/PathPPI.html. PMID:25591449

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

    PubMed

    Yang, Lei; Tang, Xianglong

    2014-01-01

    Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning. PMID:24578640

  13. Protein-protein and protein-salt interactions in aqueous protein solutions containing concentrated electrolytes

    SciTech Connect

    Curtis, R.A.; Blanch, H.W.; Prausnitz, J.M.

    1998-01-05

    Protein-protein and protein-salt interactions have been obtained for ovalbumin in solutions of ammonium sulfate and for lysozyme in solutions of ammonium sulfate, sodium chloride, potassium isothiocyanate, and potassium chloride. The two-body interactions between ovalbumin molecules in concentrated ammonium-sulfate solutions can be described by the DLVO potentials plus a potential that accounts for the decrease in free volume available to the protein due to the presence of the salt ions. The interaction between ovalbumin and ammonium sulfate is unfavorable, reflecting the kosmotropic nature of sulfate anions. Lysozyme-lysozyme interactions cannot be described by the above potentials because anion binding to lysozyme alters these interactions. Lysozyme-isothiocyanate complexes are strongly attractive due to electrostatic interactions resulting from bridging by the isothiocyanate ion. Lysozyme-lysozyme interactions in sulfate solutions are more repulsive than expected, possibly resulting from a larger excluded volume of a lysozyme-sulfate bound complex or perhaps, hydration forces between the lysozyme-sulfate complexes.

  14. A computational tool for identifying minimotifs in protein-protein interactions and improving the accuracy of minimotif predictions.

    PubMed

    Rajasekaran, Sanguthevar; Merlin, Jerlin Camilus; Kundeti, Vamsi; Mi, Tian; Oommen, Aaron; Vyas, Jay; Alaniz, Izua; Chung, Keith; Chowdhury, Farah; Deverasatty, Sandeep; Irvey, Tenisha M; Lacambacal, David; Lara, Darlene; Panchangam, Subhasree; Rathnayake, Viraj; Watts, Paula; Schiller, Martin R

    2011-01-01

    Protein-protein interactions are important to understanding cell functions; however, our theoretical understanding is limited. There is a general discontinuity between the well-accepted physical and chemical forces that drive protein-protein interactions and the large collections of identified protein-protein interactions in various databases. Minimotifs are short functional peptide sequences that provide a basis to bridge this gap in knowledge. However, there is no systematic way to study minimotifs in the context of protein-protein interactions or vice versa. Here we have engineered a set of algorithms that can be used to identify minimotifs in known protein-protein interactions and implemented this for use by scientists in Minimotif Miner. By globally testing these algorithms on verified data and on 100 individual proteins as test cases, we demonstrate the utility of these new computation tools. This tool also can be used to reduce false-positive predictions in the discovery of novel minimotifs. The statistical significance of these algorithms is demonstrated by an ROC analysis (P = 0.001). PMID:20938975

  15. Prediction of Protein-Protein Interaction Sites Using Electrostatic Desolvation Profiles

    PubMed Central

    Fiorucci, Sébastien; Zacharias, Martin

    2010-01-01

    Abstract Protein-protein complex formation involves removal of water from the interface region. Surface regions with a small free energy penalty for water removal or desolvation may correspond to preferred interaction sites. A method to calculate the electrostatic free energy of placing a neutral low-dielectric probe at various protein surface positions has been designed and applied to characterize putative interaction sites. Based on solutions of the finite-difference Poisson equation, this method also includes long-range electrostatic contributions and the protein solvent boundary shape in contrast to accessible-surface-area-based solvation energies. Calculations on a large set of proteins indicate that in many cases (>90%), the known binding site overlaps with one of the six regions of lowest electrostatic desolvation penalty (overlap with the lowest desolvation region for 48% of proteins). Since the onset of electrostatic desolvation occurs even before direct protein-protein contact formation, it may help guide proteins toward the binding region in the final stage of complex formation. It is interesting that the probe desolvation properties associated with residue types were found to depend to some degree on whether the residue was outside of or part of a binding site. The probe desolvation penalty was on average smaller if the residue was part of a binding site compared to other surface locations. Applications to several antigen-antibody complexes demonstrated that the approach might be useful not only to predict protein interaction sites in general but to map potential antigenic epitopes on protein surfaces. PMID:20441756

  16. Establishing an osteosarcoma associated protein-protein interaction network to explore the pathogenesis of osteosarcoma

    PubMed Central

    2013-01-01

    Background The aim of this study was to establish an osteosarcoma (OS) associated protein-protein interaction network and explore the pathogenesis of osteosarcoma. Methods The gene expression profile GSE9508 was downloaded from the Gene Expression Omnibus database, including five samples of non-malignant bone (the control), seven samples for non-metastatic patients (six of which were analyzed in duplicate), and 11 samples for metastatic patients (10 of which were analyzed in duplicate). Differentially expressed genes (DEGs) between osteosarcoma and control samples were identified by packages in R with the threshold of |logFC (fold change)| > 1 and false discovery rate < 0.05. Osprey software was used to construct the interaction network of DEGs, and genes at protein-protein interaction (PPI) nodes with high degrees were identified. The Database for Annotation, Visualization and Integrated Discovery and WebGestalt software were then used to perform functional annotation and pathway enrichment analyses for PPI networks, in which P < 0.05 was considered statistically significant. Results Compared to the control samples, the expressions of 42 and 341 genes were altered in non-metastatic OS and metastatic OS samples, respectively. A total of 15 significantly enriched functions were obtained with Gene Ontology analysis (P < 0.05). The DEGs were classified and significantly enriched in three pathways, including the tricarboxylic acid cycle, lysosome and axon guidance. Genes such as HRAS, IDH3A, ATP6ap1, ATP6V0D2, SEMA3F and SEMA3A were involved in the enriched pathways. Conclusions The hub genes from metastatic OS samples are not only bio-markers of OS, but also help to improve therapies for OS. PMID:24330838

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

    PubMed

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

    2011-06-01

    Mass spectrometry-based proteomics techniques have been very successful for the identification and study of protein-protein interactions. Typically, immunopurification of protein complexes is conducted, followed by protein separation by gel electrophoresis and in-gel protein digestion, and finally, mass spectrometry is performed to identify the interacting partners. However, the manual processing of the samples is time-consuming and error-prone. Here, we developed a polymer-based microfluidic proteomic reactor aimed at the parallel analysis of minute amounts of protein samples obtained from immunoprecipitation. The design of the proteomic reactor allows for the simultaneous processing of multiple samples on the same devices. Each proteomic reactor on the device consists of SCX beads packed and restricted into a 1 cm microchannel by two integrated pillar frits. The device is fabricated using a combination of low-cost hard cyclic olefin copolymer thermoplastic and elastomeric thermoplastic materials (styrene/(ethylene/butylenes)/styrene) using rapid hot-embossing replication techniques with a polymer-based stamp. Three immunopurified protein samples are simultaneously captured, reduced, alkylated, and digested on the device within 2-3 h instead of the days required for the conventional protein-protein interaction studies. The limit of detection of the microfluidic proteomic reactor was shown to be lower than 2 ng of protein. Furthermore, the application of the microfluidic proteomic reactor was demonstrated for the simultaneous processing of the interactome of the histone variant Htz1 in wild-type yeast and in a swr1Δ yeast strain compared to an untagged control using a novel three-channel microfluidic proteomic reactor. PMID:21520965

  18. Collective prediction of protein functions from protein-protein interaction networks

    PubMed Central

    2014-01-01

    Background Automated assignment of functions to unknown proteins is one of the most important task in computational biology. The development of experimental methods for genome scale analysis of molecular interaction networks offers new ways to infer protein function from protein-protein interaction (PPI) network data. Existing techniques for collective classification (CC) usually increase accuracy for network data, wherein instances are interlinked with each other, using a large amount of labeled data for training. However, the labeled data are time-consuming and expensive to obtain. On the other hand, one can easily obtain large amount of unlabeled data. Thus, more sophisticated methods are needed to exploit the unlabeled data to increase prediction accuracy for protein function prediction. Results In this paper, we propose an effective Markov chain based CC algorithm (ICAM) to tackle the label deficiency problem in CC for interrelated proteins from PPI networks. Our idea is to model the problem using two distinct Markov chain classifiers to make separate predictions with regard to attribute features from protein data and relational features from relational information. The ICAM learning algorithm combines the results of the two classifiers to compute the ranks of labels to indicate the importance of a set of labels to an instance, and uses an ICA framework to iteratively refine the learning models for improving performance of protein function prediction from PPI networks in the paucity of labeled data. Conclusion Experimental results on the real-world Yeast protein-protein interaction datasets show that our proposed ICAM method is better than the other ICA-type methods given limited labeled training data. This approach can serve as a valuable tool for the study of protein function prediction from PPI networks. PMID:24564855

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

    PubMed

    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. PMID:25385082

  20. Screening Bicyclic Peptide Libraries for Protein-Protein Interaction Inhibitors: Discovery of a Tumor Necrosis Factor-alpha Antagonist

    PubMed Central

    Rhodes, Curran A.; Liu, Yusen; Pei, Dehua

    2013-01-01

    Protein-protein interactions represent a new class of exciting but challenging drug targets, because their large, flat binding sites lack well defined pockets for small molecules to bind. We report here a methodology for chemical synthesis and screening of large combinatorial libraries of bicyclic peptides displayed on rigid small-molecule scaffolds. With planar trimesic acid as the scaffold, the resulting bicyclic peptides are effective for binding to protein surfaces such as the interfaces of protein-protein interactions. Screening of a bicyclic peptide library against tumor necrosis factor-alpha (TNFα) identified a potent antagonist that inhibits the TNFα-TNFα receptor interaction and protects cells from TNFα-induced cell death. Bicyclic peptides of this type may provide a general solution for inhibition of protein-protein interactions. PMID:23865589

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

    PubMed Central

    Andersen, Tonni Grube; Nintemann, Sebastian J.; Marek, Magdalena; Halkier, Barbara A.; Schulz, Alexander; Burow, Meike

    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 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 into the widely used split ubiquitin-, bimolecular fluorescence complementation (BiFC)- and Förster resonance energy transfer (FRET)- based methods and investigated different protein-protein interactions in yeast and plants. We demonstrate the functionality of this concept by the analysis of weakly interacting proteins from specialized metabolism in the model plant Arabidopsis thaliana. Our results illustrate that chemically induced dimerization can function as a built-in control for split-based systems that is easily implemented and allows for direct evaluation of functionality. PMID:27282591

  2. Light-Scattering Studies of Protein Solutions: Role of Hydration in Weak Protein-Protein Interactions

    PubMed Central

    Paliwal, A.; Asthagiri, D.; Abras, D.; Lenhoff, A. M.; Paulaitis, M. E.

    2005-01-01

    We model the hydration contribution to short-range electrostatic/dispersion protein interactions embodied in the osmotic second virial coefficient, B2, by adopting a quasi-chemical description in which water molecules associated with the protein are identified through explicit molecular dynamics simulations. These water molecules reduce the surface complementarity of highly favorable short-range interactions, and therefore can play an important role in mediating protein-protein interactions. Here we examine this quasi-chemical view of hydration by predicting the interaction part of B2 and comparing our results with those derived from light-scattering measurements of B2 for staphylococcal nuclease, lysozyme, and chymotrypsinogen at 25°C as a function of solution pH and ionic strength. We find that short-range protein interactions are influenced by water molecules strongly associated with a relatively small fraction of the protein surface. However, the effect of these strongly associated water molecules on the surface complementarity of short-range protein interactions is significant, and must be taken into account for an accurate description of B2. We also observe remarkably similar hydration behavior for these proteins despite substantial differences in their three-dimensional structures and spatial charge distributions, suggesting a general characterization of protein hydration. PMID:15980182

  3. Activities of the Sex-lethal protein in RNA binding and protein:protein interactions.

    PubMed Central

    Samuels, M; Deshpande, G; Schedl, P

    1998-01-01

    The Drosophila sex determination gene Sex-lethal (Sxl) controls its own expression, and the expression of downstream target genes such as transformer , by regulating pre-mRNA splicing and mRNA translation. Sxl codes an RNA-binding protein that consists of an N-terminus of approximately 100 amino acids, two 90 amino acid RRM domains, R1 and R2, and an 80 amino acid C-terminus. In the studies reported here we have examined the functional properties of the different Sxl protein domains in RNA binding and in protein:protein interactions. The two RRM domains are responsible for RNA binding. Specificity in the recognition of target RNAs requires both RRM domains, and proteins which consist of the single domains or duplicated domains have anomalous RNA recognition properties. Moreover, the length of the linker between domains can affect RNA recognition properties. Our results indicate that the two RRM domains mediate Sxl:Sxl protein interactions, and that these interactions probably occur both in cis and trans. We speculate that cis interactions between R1 and R2 play a role in RNA recognition by the Sxl protein, while trans interactions stabilize complex formation on target RNAs that contain two or more closely spaced binding sites. Finally, we show that the interaction of Sxl with the snRNP protein Snf is mediated by the R1 RRM domain. PMID:9592147

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

    PubMed

    Andersen, Tonni Grube; Nintemann, Sebastian J; Marek, Magdalena; Halkier, Barbara A; Schulz, Alexander; Burow, Meike

    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 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 into the widely used split ubiquitin-, bimolecular fluorescence complementation (BiFC)- and Förster resonance energy transfer (FRET)- based methods and investigated different protein-protein interactions in yeast and plants. We demonstrate the functionality of this concept by the analysis of weakly interacting proteins from specialized metabolism in the model plant Arabidopsis thaliana. Our results illustrate that chemically induced dimerization can function as a built-in control for split-based systems that is easily implemented and allows for direct evaluation of functionality. PMID:27282591

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

    PubMed

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

    2013-01-01

    This paper presents a new search engine called PPISearchEngine which finds protein-protein interactions (PPIs) using the gene ontology (GO) and the biological relations of proteins. For efficient retrieval of PPIs, each GO term is assigned a prime number and the relation between the terms is represented by the product of prime numbers. This representation is hidden from users but facilitates the search for the interactions of a query protein by unique prime factorisation of the number that represents the query protein. For a query protein, PPISearchEngine considers not only the GO term associated with the query protein but also the GO terms at the lower level than the GO term in the GO hierarchy, and finds all the interactions of the query protein which satisfy the search condition. In contrast, the standard keyword-matching or ID-matching search method cannot find the interactions of a protein unless the interactions involve a protein with explicit annotations. To the best of our knowledge, this search engine is the first method that can process queries like 'for protein p with GO [Formula: see text], find p's interaction partners with GO [Formula: see text]'. PPISearchEngine is freely available to academics at http://search.hpid.org/. PMID:22316075

  6. Computational prediction of virus-human protein-protein interactions using embedding kernelized heterogeneous data.

    PubMed

    Nourani, Esmaeil; Khunjush, Farshad; Durmuş, Saliha

    2016-05-24

    Pathogenic microorganisms exploit host cellular mechanisms and evade host defense mechanisms through molecular pathogen-host interactions (PHIs). Therefore, comprehensive analysis of these PHI networks should be an initial step for developing effective therapeutics against infectious diseases. Computational prediction of PHI data is gaining increasing demand because of scarcity of experimental data. Prediction of protein-protein interactions (PPIs) within PHI systems can be formulated as a classification problem, which requires the knowledge of non-interacting protein pairs. This is a restricting requirement since we lack datasets that report non-interacting protein pairs. In this study, we formulated the "computational prediction of PHI data" problem using kernel embedding of heterogeneous data. This eliminates the abovementioned requirement and enables us to predict new interactions without randomly labeling protein pairs as non-interacting. Domain-domain associations are used to filter the predicted results leading to 175 novel PHIs between 170 human proteins and 105 viral proteins. To compare our results with the state-of-the-art studies that use a binary classification formulation, we modified our settings to consider the same formulation. Detailed evaluations are conducted and our results provide more than 10 percent improvements for accuracy and AUC (area under the receiving operating curve) results in comparison with state-of-the-art methods. PMID:27072625

  7. Walk-weighted subsequence kernels for protein-protein interaction extraction

    PubMed Central

    2010-01-01

    Background The construction of interaction networks between proteins is central to understanding the underlying biological processes. However, since many useful relations are excluded in databases and remain hidden in raw text, a study on automatic interaction extraction from text is important in bioinformatics field. Results Here, we suggest two kinds of kernel methods for genic interaction extraction, considering the structural aspects of sentences. First, we improve our prior dependency kernel by modifying the kernel function so that it can involve various substructures in terms of (1) e-walks, (2) partial match, (3) non-contiguous paths, and (4) different significance of substructures. Second, we propose the walk-weighted subsequence kernel to parameterize non-contiguous syntactic structures as well as semantic roles and lexical features, which makes learning structural aspects from a small amount of training data effective. Furthermore, we distinguish the significances of parameters such as syntactic locality, semantic roles, and lexical features by varying their weights. Conclusions We addressed the genic interaction problem with various dependency kernels and suggested various structural kernel scenarios based on the directed shortest dependency path connecting two entities. Consequently, we obtained promising results over genic interaction data sets with the walk-weighted subsequence kernel. The results are compared using automatically parsed third party protein-protein interaction (PPI) data as well as perfectly syntactic labeled PPI data. PMID:20184736

  8. A profile of protein-protein interaction: Crystal structure of a lectin-lectin complex.

    PubMed

    Surya, Sukumaran; Abhilash, Joseph; Geethanandan, Krishnan; Sadasivan, Chittalakkottu; Haridas, Madhathilkovilakathu

    2016-06-01

    Proteins may utilize complex networks of interactions to create/proceed signaling pathways of highly adaptive responses such as programmed cell death. Direct binary interactions study of proteins may help propose models for protein-protein interaction. Towards this goal we applied a combination of thermodynamic kinetics and crystal structure analyses to elucidate the complexity and diversity in such interactions. By determining the heat change on the association of two galactose-specific legume lectins from Butea monosperma (BML) and Spatholobus parviflorus (SPL) belonging to Fabaceae family helped to compute the binding equilibrium. It was extended further by X-ray structural analysis of BML-SPL binary complex. In order to chart the proteins interacting mainly through their interfaces, identification of the nature of forces which stabilized the association of the lectin-lectin complex was examined. Comprehensive analysis of the BMLSPL complex by isothermal titration calorimetry and X-ray crystal structure threw new light on the lectin-lectin interactions suggesting of their use in diverse areas of glycobiology. PMID:26945504

  9. Investigating protein-protein interactions in living cells using fluorescence lifetime imaging microscopy

    PubMed Central

    Sun, Yuansheng; Day, Richard N; Periasamy, Ammasi

    2011-01-01

    Fluorescence lifetime imaging microscopy (FLIM) is now routinely used for dynamic measurements of signaling events inside living cells, including detection of protein-protein interactions. An understanding of the basic physics of fluorescence lifetime measurements is required to use this technique. In this protocol, we describe both the time-correlated single photon counting and the frequency-domain methods for FLIM data acquisition and analysis. We describe calibration of both FLIM systems, and demonstrate how they are used to measure the quenched donor fluorescence lifetime that results from Förster resonance energy transfer (FRET ). We then show how the FLIM-FRET methods are used to detect the dimerization of the transcription factor CCAAT/enhancer binding protein-α in live mouse pituitary cell nuclei. Notably, the factors required for accurate determination and reproducibility of lifetime measurements are described. With either method, the entire protocol including specimen preparation, imaging and data analysis takes ~2 d. PMID:21886099

  10. Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions.

    PubMed

    Laraia, Luca; McKenzie, Grahame; Spring, David R; Venkitaraman, Ashok R; Huggins, David J

    2015-06-18

    Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs. PMID:26091166

  11. Investigation of stable and transient protein-protein interactions: past, present and future

    PubMed Central

    Ngounou Wetie, Armand G.; Sokolowska, Izabela; Woods, Alisa G.; Roy, Urmi; Loo, Joseph A.; Darie, Costel C.

    2013-01-01

    This article presents an overview of the literature and a review of recent advances in the analysis of stable and transient protein-protein interactions (PPIs) with a focus on their function within cells, organs and organisms. The significance of post-translational modifications within the PPIs is also discussed. We focus on methods to study PPIs and methods of detecting PPIs, with particular emphasis on electrophoresis-based and mass spectrometry (MS)-based investigation of PPIs, including specific examples. The validation of PPIs is emphasized and the limitations of the current methods for studying stable and transient PPIs are discussed. Perspectives regarding PPIs, with focus on bioinformatics and transient PPIs are also provided. PMID:23193082

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

    PubMed Central

    Wang, Baoman; Yuan, Fei; Kong, Xiangyin; Hu, Lan-Dian; Cai, Yu-Dong

    2015-01-01

    Apoptosis is the process of programmed cell death (PCD) that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature. PMID:26543496

  13. Synthesis of Hydrogen-Bond Surrogate α-helices as Inhibitors of Protein-Protein Interactions

    PubMed Central

    Miller, Stephen E.; Thomson, Paul F.; Arora, Paramjit S.

    2014-01-01

    The α-helix is a prevalent secondary structure in proteins and critical in mediating protein-protein interactions (PPIs). Peptide mimetics that adopt stable helices have become powerful tools for the modulation of PPIs in vitro and in vivo. Hydrogen-bond surrogate (HBS) α-helices utilize a covalent bond in place of an N-terminal i to i+4 hydrogen bond and have been used to target and disrupt PPIs that become dysregulated in disease states. These compounds have improved conformational stability and cellular uptake as compared to their linear peptide counterparts. The protocol presented here describes current methodology for the synthesis of HBS α-helical mimetics. The solid phase synthesis of HBS helices involves solid phase peptide synthesis with three key steps involving incorporation of N-allyl functionality within the backbone of the peptide, coupling of a secondary amine, and a ring-closing metathesis step. PMID:24903885

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

    PubMed

    Jia, Jianhua; Xiao, Xuan; Liu, Bingxiang

    2016-06-01

    Protein-protein interactions (PPIs) provide valuable insight into the inner workings of cells, and it is significant to study the network of PPIs. It is vitally important to develop an automated method as a high-throughput tool to timely predict PPIs. Based on the physicochemical descriptors, a protein was converted into several digital signals, and then wavelet transform was used to analyze them. With such a formulation frame to represent the samples of protein sequences, the random forests algorithm was adopted to conduct prediction. The results on a large-scale independent-test data set show that the proposed model can achieve a good performance with an accuracy value of about 0.86 and a geometric mean value of about 0.85. Therefore, it can be a usefully supplementary tool for PPI prediction. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI_RF. PMID:25882187

  15. The centrality of cancer proteins in human protein-protein interaction network: a revisit.

    PubMed

    Xiong, Wei; Xie, Luyu; Zhou, Shuigeng; Liu, Hui; Guan, Jihong

    2014-01-01

    Topological analysis of protein-protein interaction (PPI) networks has been widely applied to the investigation on cancer mechanisms. However, there is still a debate on whether cancer proteins exhibit more topological centrality compared to the other proteins in the human PPI network. To resolve this debate, we first identified four sets of human proteins, and then mapped these proteins into the yeast PPI network by homologous genes. Finally, we compared these proteins' properties in human and yeast PPI networks. Experiments over two real datasets demonstrated that cancer proteins tend to have higher degree and smaller clustering coefficient than non-cancer proteins. Experimental results also validated that cancer proteins have larger betweenness centrality compared to the other proteins on the STRING dataset. However, on the BioGRID dataset, the average betweenness centrality of cancer proteins is larger than that of disease and control proteins, but smaller than that of essential proteins. PMID:24878726

  16. Genetically Encoded Molecular Tension Probe for Tracing Protein-Protein Interactions in Mammalian Cells.

    PubMed

    Kim, Sung Bae; Nishihara, Ryo; Citterio, Daniel; Suzuki, Koji

    2016-02-17

    Optical imaging of protein-protein interactions (PPIs) facilitates comprehensive elucidation of intracellular molecular events. We demonstrate an optical measure for visualizing molecular tension triggered by any PPI in mammalian cells. Twenty-three kinds of candidate designs were fabricated, in which a full-length artificial luciferase (ALuc) was sandwiched between two model proteins of interest, e.g., FKBP and FRB. One of the designs greatly enhanced the bioluminescence in response to varying concentrations of rapamycin. It is confirmed with negative controls that the elevated bioluminescence is solely motivated from the molecular tension. The probe design was further modified toward eliminating the C-terminal end of ALuc and was found to improve signal-to-background ratios, named "a combinational probe". The utilities were elucidated with detailed substrate selectivity, bioluminescence imaging of live cells, and different PPI models. This study expands capabilities of luciferases as a tool for analyses of molecular dynamics and cell signaling in living subjects. PMID:26322739

  17. An ALuc-Based Molecular Tension Probe for Sensing Intramolecular Protein-Protein Interactions.

    PubMed

    Kim, Sung-Bae; Nishihara, Ryo; Suzuki, Koji

    2016-01-01

    Optical imaging of protein-protein interactions (PPIs) facilitates comprehensive elucidation of intracellular molecular events. The present protocol demonstrates an optical measure for visualizing molecular tension triggered by any PPI in mammalian cells. A unique design of single-chain probes was fabricated, in which a full-length artificial luciferase (ALuc(®)) was sandwiched between two model proteins of interest, e.g., FKBP and FRB. A molecular tension probe comprising ALuc23 greatly enhances the bioluminescence in response to varying concentrations of rapamycin, and named "tension probe (TP)." The basic probe design can be further modified towards eliminating the C-terminal end of ALuc and was found to improve signal-to-background ratios, named "combinational probe." TPs may become an important addition to the tool box of bioassays in the determination of protein dynamics of interest in mammalian cells. PMID:27424905

  18. CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

    2014-12-01

    Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

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

    PubMed

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

    2016-03-01

    By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed. PMID:26691180

  20. Protein-Protein Interactions from Linear-Scaling First Principles Quantum Mechanical Calculations

    NASA Astrophysics Data System (ADS)

    Cole, Daniel; Skylaris, Chris-Kriton; Rajendra, Eeson; Venkitaraman, Ashok; Payne, Mike

    2010-03-01

    A modification of the MM-PBSA technique for calculating binding affinities of biomolecular complexes is presented. Classical molecular dynamics is used to explore the motion of the extended interface between two peptides derived from the BRC4 repeat of BRCA2 and the eukaryotic recombinase RAD51. The resulting trajectory is sampled using the linear-scaling density functional theory code, onetep, to determine from first principles, and with high computational efficiency, the relative free energies of binding of the ˜2800 atom receptor-ligand complexes. This new method provides the basis for computational interrogation of protein-protein and protein-ligand interactions, within fields ranging from chemical biological studies to small molecule binding behaviour, with both unprecedented chemical accuracy and affordable computational expense.

  1. Protein-protein interactions from linear-scaling first-principles quantum-mechanical calculations

    NASA Astrophysics Data System (ADS)

    Cole, D. J.; Skylaris, C.-K.; Rajendra, E.; Venkitaraman, A. R.; Payne, M. C.

    2010-08-01

    A modification of the MM-PBSA technique for calculating binding affinities of biomolecular complexes is presented. Classical molecular dynamics is used to explore the motion of the extended interface between two peptides derived from the BRC4 repeat of BRCA2 and the eukaryotic recombinase RAD51. The resulting trajectory is sampled using the linear-scaling density functional theory code, onetep, to determine from first principles, and with high computational efficiency, the relative free energies of binding of the ~2800 atom receptor-ligand complexes. This new method provides the basis for computational interrogation of protein-protein and protein-ligand interactions within fields ranging from chemical biological studies to small-molecule binding behaviour, with both unprecedented chemical accuracy and affordable computational expense.

  2. HKC: an algorithm to predict protein complexes in protein-protein interaction networks.

    PubMed

    Wang, Xiaomin; Wang, Zhengzhi; Ye, Jun

    2011-01-01

    With the availability of more and more genome-scale protein-protein interaction (PPI) networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods. PMID:22174556

  3. Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions

    PubMed Central

    Laraia, Luca; McKenzie, Grahame; Spring, David R.; Venkitaraman, Ashok R.; Huggins, David J.

    2015-01-01

    Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs. PMID:26091166

  4. Targeting protein-protein interactions in hematologic malignancies: still a challenge or a great opportunity for future therapies?

    PubMed Central

    Cierpicki, Tomasz; Grembecka, Jolanta

    2015-01-01

    Summary Over the past several years, there has been an increasing research effort focused on inhibition of protein-protein interactions (PPIs) to develop novel therapeutic approaches for cancer, including hematologic malignancies. These efforts have led to development of small molecule inhibitors of PPIs, some of which already advanced to the stage of clinical trials while others are at different stages of pre-clinical optimization, emphasizing PPIs as an emerging and attractive class of drug targets. Here, we review several examples of recently developed inhibitors of protein-protein interactions highly relevant to hematologic cancers. We address the existing skepticism about feasibility of targeting PPIs and emphasize potential therapeutic benefit from blocking PPIs in hematologic malignancies. We then use these examples to discuss the approaches for successful identification of PPI inhibitors and provide analysis of the protein-protein interfaces, with the goal to address ‘druggability’ of new PPIs relevant to hematology. We discuss lessons learned to improve the success of targeting new protein-protein interactions and evaluate prospects and limits of the research in this field. We conclude that not all PPIs are equally tractable for blocking by small molecules, and detailed analysis of PPI interfaces is critical for selection of those with the highest chance of success. Together, our analysis uncovers patterns that should help to advance drug discovery in hematologic malignancies by successful targeting of new protein-protein interactions. PMID:25510283

  5. Learning Sequence Determinants of Protein:Protein Interaction Specificity with Sparse Graphical Models

    PubMed Central

    Kamisetty, Hetunandan; Ghosh, Bornika; Langmead, Christopher James; Bailey-Kellogg, Chris

    2015-01-01

    Abstract In studying the strength and specificity of interaction between members of two protein families, key questions center on which pairs of possible partners actually interact, how well they interact, and why they interact while others do not. The advent of large-scale experimental studies of interactions between members of a target family and a diverse set of possible interaction partners offers the opportunity to address these questions. We develop here a method, DgSpi (data-driven graphical models of specificity in protein:protein interactions), for learning and using graphical models that explicitly represent the amino acid basis for interaction specificity (why) and extend earlier classification-oriented approaches (which) to predict the ΔG of binding (how well). We demonstrate the effectiveness of our approach in analyzing and predicting interactions between a set of 82 PDZ recognition modules against a panel of 217 possible peptide partners, based on data from MacBeath and colleagues. Our predicted ΔG values are highly predictive of the experimentally measured ones, reaching correlation coefficients of 0.69 in 10-fold cross-validation and 0.63 in leave-one-PDZ-out cross-validation. Furthermore, the model serves as a compact representation of amino acid constraints underlying the interactions, enabling protein-level ΔG predictions to be naturally understood in terms of residue-level constraints. Finally, the model DgSpi readily enables the design of new interacting partners, and we demonstrate that designed ligands are novel and diverse. PMID:25973864

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

    PubMed Central

    2012-01-01

    Background The correlation of genetic distances between pairs of protein sequence alignments has been used to infer protein-protein interactions. It has been suggested that these correlations are based on the signal of co-evolution between interacting proteins. However, although mutations in different proteins associated with maintaining an interaction clearly occur (particularly in binding interfaces and neighbourhoods), many other factors contribute to correlated rates of sequence evolution. Proteins in the same genome are usually linked by shared evolutionary history and so it would be expected that there would be topological similarities in their phylogenetic trees, whether they are interacting or not. For this reason the underlying species tree is often corrected for. Moreover processes such as expression level, are known to effect evolutionary rates. However, it has been argued that the correlated rates of evolution used to predict protein interaction explicitly includes shared evolutionary history; here we test this hypothesis. Results In order to identify the evolutionary mechanisms giving rise to the correlations between interaction proteins, we use phylogenetic methods to distinguish similarities in tree topologies from similarities in genetic distances. We use a range of datasets of interacting and non-interacting proteins from Saccharomyces cerevisiae. We find that the signal of correlated evolution between interacting proteins is predominantly a result of shared evolutionary rates, rather than similarities in tree topology, independent of evolutionary divergence. Conclusions Since interacting proteins do not have tree topologies that are more similar than the control group of non-interacting proteins, it is likely that coevolution does not contribute much to, if any, of the observed correlations. PMID:23217198

  7. Prediction of protein-protein interactions with clustered amino acids and weighted sparse representation.

    PubMed

    Huang, Qiaoying; You, Zhuhong; Zhang, Xiaofeng; Zhou, Yong

    2015-01-01

    With the completion of the Human Genome Project, bioscience has entered into the era of the genome and proteome. Therefore, protein-protein interactions (PPIs) research is becoming more and more important. Life activities and the protein-protein interactions are inseparable, such as DNA synthesis, gene transcription activation, protein translation, etc. Though many methods based on biological experiments and machine learning have been proposed, they all spent a long time to learn and obtained an imprecise accuracy. How to efficiently and accurately predict PPIs is still a big challenge. To take up such a challenge, we developed a new predictor by incorporating the reduced amino acid alphabet (RAAA) information into the general form of pseudo-amino acid composition (PseAAC) and with the weighted sparse representation-based classification (WSRC). The remarkable advantages of introducing the reduced amino acid alphabet is being able to avoid the notorious dimensionality disaster or overfitting problem in statistical prediction. Additionally, experiments have proven that our method achieved good performance in both a low- and high-dimensional feature space. Among all of the experiments performed on the PPIs data of Saccharomyces cerevisiae, the best one achieved 90.91% accuracy, 94.17% sensitivity, 87.22% precision and a 83.43% Matthews correlation coefficient (MCC) value. In order to evaluate the prediction ability of our method, extensive experiments are performed to compare with the state-of-the-art technique, support vector machine (SVM). The achieved results show that the proposed approach is very promising for predicting PPIs, and it can be a helpful supplement for PPIs prediction. PMID:25984606

  8. Protein-Protein Interactions in Clathrin Vesicular Assembly: Radial Distribution of Evolutionary Constraints in Interfaces

    PubMed Central

    Gadkari, Rupali A.; Srinivasan, Narayanaswamy

    2012-01-01

    In eukaryotic organisms clathrin-coated vesicles are instrumental in the processes of endocytosis as well as intracellular protein trafficking. Hence, it is important to understand how these vesicles have evolved across eukaryotes, to carry cargo molecules of varied shapes and sizes. The intricate nature and functional diversity of the vesicles are maintained by numerous interacting protein partners of the vesicle system. However, to delineate functionally important residues participating in protein-protein interactions of the assembly is a daunting task as there are no high-resolution structures of the intact assembly available. The two cryoEM structures closely representing intact assembly were determined at very low resolution and provide positions of Cα atoms alone. In the present study, using the method developed by us earlier, we predict the protein-protein interface residues in clathrin assembly, taking guidance from the available low-resolution structures. The conservation status of these interfaces when investigated across eukaryotes, revealed a radial distribution of evolutionary constraints, i.e., if the members of the clathrin vesicular assembly can be imagined to be arranged in spherical manner, the cargo being at the center and clathrins being at the periphery, the detailed phylogenetic analysis of these members of the assembly indicated high-residue variation in the members of the assembly closer to the cargo while high conservation was noted in clathrins and in other proteins at the periphery of the vesicle. This points to the strategy adopted by the nature to package diverse proteins but transport them through a highly conserved mechanism. PMID:22384024

  9. Virtual Screening and Experimental Validation Identify Novel Inhibitors of the Plasmodium falciparum Atg8-Atg3 Protein-Protein Interaction.

    PubMed

    Hain, Adelaide U P; Miller, Alexia S; Levitskaya, Jelena; Bosch, Jürgen

    2016-04-19

    New therapies are needed against malaria, a parasitic infection caused by Plasmodium falciparum, as drug resistance emerges against the current treatment, artemisinin. We previously characterized the Atg8-Atg3 protein-protein interaction (PPI), which is essential for autophagy and parasite survival. Herein we illustrate the use of virtual library screening to selectively block the PPI in the parasite without inhibiting the homologous interaction in humans by targeting the A-loop of PfAtg8. This A-loop is important for Atg3 binding in Plasmodium, but is absent from the human Atg8 homologues. In this proof-of-concept study, we demonstrate a shift in lipidation state of PfAtg8 and inhibition of P. falciparum growth in both blood- and liver-stage cultures upon drug treatment. Our results illustrate how in silico screening and structure-aided drug design against a PPI can be used to identify new hits for drug development. Additionally, as we targeted a region of Atg8 that is conserved within apicomplexans, we predict that our small molecule will have cross-reactivity against other disease-causing apicomplexans, such as Toxoplasma, Cryptosporidium, Theileria, Neospora, Eimeria, and Babesia. PMID:26748931

  10. Constrained Cyclic Peptides as Immunomodulatory Inhibitors of the CD2:CD58 Protein-Protein Interaction.

    PubMed

    Sable, Rushikesh; Durek, Thomas; Taneja, Veena; Craik, David J; Pallerla, Sandeep; Gauthier, Ted; Jois, Seetharama

    2016-08-19

    The interaction between the cell-cell adhesion proteins CD2 and CD58 plays a crucial role in lymphocyte recruitment to inflammatory sites, and inhibitors of this interaction have potential as immunomodulatory drugs in autoimmune diseases. Peptides from the CD2 adhesion domain were designed to inhibit CD2:CD58 interactions. To improve the stability of the peptides, β-sheet epitopes from the CD2 region implicated in CD58 recognition were grafted into the cyclic peptide frameworks of sunflower trypsin inhibitor and rhesus theta defensin. The designed multicyclic peptides were evaluated for their ability to modulate cell-cell interactions in three different cell adhesion assays, with one candidate, SFTI-a, showing potent activity in the nanomolar range (IC50: 51 nM). This peptide also suppresses the immune responses in T cells obtained from mice that exhibit the autoimmune disease rheumatoid arthritis. SFTI-a was resistant to thermal denaturation, as judged by circular dichroism spectroscopy and mass spectrometry, and had a half-life of ∼24 h in human serum. Binding of this peptide to CD58 was predicted by molecular docking studies and experimentally confirmed by surface plasmon resonance experiments. Our results suggest that cyclic peptides from natural sources are promising scaffolds for modulating protein-protein interactions that are typically difficult to target with small-molecule compounds. PMID:27337048

  11. RefSOFI for Mapping Nanoscale Organization of Protein-protein Interactions in Living cells

    PubMed Central

    Hertel, Fabian; Mo, Gary C. H.; Duwé, Sam; Dedecker, Peter; Zhang, Jin

    2015-01-01

    Summary It has become increasingly clear that protein-protein interactions (PPIs) are compartmentalized in nanoscale domains that define the biochemical architecture of the cell. Despite tremendous advances in super-resolution imaging, strategies to observe PPIs at sufficient resolution to discern their organization are just emerging. Here we describe a strategy in which PPIs induce reconstitution of fluorescent proteins (FPs) that are capable of exhibiting single-molecule fluctuations suitable for Stochastic Optical Fluctuation Imaging (SOFI). Subsequently, spatial maps of these interactions can be resolved in super-resolution in living cells. Using this strategy, termed reconstituted fluorescence-based SOFI (refSOFI), we investigated the interaction between the endoplasmic reticulum Ca2+ sensor STIM1 and the pore-forming channel subunit ORAI1, a crucial process in store-operated Ca2+ entry (SOCE). Stimulating SOCE does not appear to change the size of existing STIM1/ORAI1 interaction puncta at the ER-plasma membrane junctions, but results in an apparent increase in the number of interaction puncta. PMID:26748717

  12. Deriving Heterospecific Self-Assembling Protein-Protein Interactions Using a Computational Interactome Screen.

    PubMed

    Crooks, Richard O; Baxter, Daniel; Panek, Anna S; Lubben, Anneke T; Mason, Jody M

    2016-01-29

    Interactions between naturally occurring proteins are highly specific, with protein-network imbalances associated with numerous diseases. For designed protein-protein interactions (PPIs), required specificity can be notoriously difficult to engineer. To accelerate this process, we have derived peptides that form heterospecific PPIs when combined. This is achieved using software that generates large virtual libraries of peptide sequences and searches within the resulting interactome for preferentially interacting peptides. To demonstrate feasibility, we have (i) generated 1536 peptide sequences based on the parallel dimeric coiled-coil motif and varied residues known to be important for stability and specificity, (ii) screened the 1,180,416 member interactome for predicted Tm values and (iii) used predicted Tm cutoff points to isolate eight peptides that form four heterospecific PPIs when combined. This required that all 32 hypothetical off-target interactions within the eight-peptide interactome be disfavoured and that the four desired interactions pair correctly. Lastly, we have verified the approach by characterising all 36 pairs within the interactome. In analysing the output, we hypothesised that several sequences are capable of adopting antiparallel orientations. We subsequently improved the software by removing sequences where doing so led to fully complementary electrostatic pairings. Our approach can be used to derive increasingly large and therefore complex sets of heterospecific PPIs with a wide range of potential downstream applications from disease modulation to the design of biomaterials and peptides in synthetic biology. PMID:26655848

  13. SNAPPI-DB: a database and API of Structures, iNterfaces and Alignments for Protein-Protein Interactions.

    PubMed

    Jefferson, Emily R; Walsh, Thomas P; Roberts, Timothy J; Barton, Geoffrey J

    2007-01-01

    SNAPPI-DB, a high performance database of Structures, iNterfaces and Alignments of Protein-Protein Interactions, and its associated Java Application Programming Interface (API) is described. SNAPPI-DB contains structural data, down to the level of atom co-ordinates, for each structure in the Protein Data Bank (PDB) together with associated data including SCOP, CATH, Pfam, SWISSPROT, InterPro, GO terms, Protein Quaternary Structures (PQS) and secondary structure information. Domain-domain interactions are stored for multiple domain definitions and are classified by their Superfamily/Family pair and interaction interface. Each set of classified domain-domain interactions has an associated multiple structure alignment for each partner. The API facilitates data access via PDB entries, domains and domain-domain interactions. Rapid development, fast database access and the ability to perform advanced queries without the requirement for complex SQL statements are provided via an object oriented database and the Java Data Objects (JDO) API. SNAPPI-DB contains many features which are not available in other databases of structural protein-protein interactions. It has been applied in three studies on the properties of protein-protein interactions and is currently being employed to train a protein-protein interaction predictor and a functional residue predictor. The database, API and manual are available for download at: http://www.compbio.dundee.ac.uk/SNAPPI/downloads.jsp. PMID:17202171

  14. Techniques for the Analysis of Protein-Protein Interactions in Vivo1[OPEN

    PubMed Central

    Xing, Shuping; Wallmeroth, Niklas; Berendzen, Kenneth W.

    2016-01-01

    Identifying key players and their interactions is fundamental for understanding biochemical mechanisms at the molecular level. The ever-increasing number of alternative ways to detect protein-protein interactions (PPIs) speaks volumes about the creativity of scientists in hunting for the optimal technique. PPIs derived from single experiments or high-throughput screens enable the decoding of binary interactions, the building of large-scale interaction maps of single organisms, and the establishment of cross-species networks. This review provides a historical view of the development of PPI technology over the past three decades, particularly focusing on in vivo PPI techniques that are inexpensive to perform and/or easy to implement in a state-of-the-art molecular biology laboratory. Special emphasis is given to their feasibility and application for plant biology as well as recent improvements or additions to these established techniques. The biology behind each method and its advantages and disadvantages are discussed in detail, as are the design, execution, and evaluation of PPI analysis. We also aim to raise awareness about the technological considerations and the inherent flaws of these methods, which may have an impact on the biological interpretation of PPIs. Ultimately, we hope this review serves as a useful reference when choosing the most suitable PPI technique. PMID:27208310

  15. Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.

    PubMed

    You, Zhu-Hong; Li, Shuai; Gao, Xin; Luo, Xin; Ji, Zhen

    2014-01-01

    Protein-protein interactions are the basis of biological functions, and studying these interactions on a molecular level is of crucial importance for understanding the functionality of a living cell. During the past decade, biosensors have emerged as an important tool for the high-throughput identification of proteins and their interactions. However, the high-throughput experimental methods for identifying PPIs are both time-consuming and expensive. On the other hand, high-throughput PPI data are often associated with high false-positive and high false-negative rates. Targeting at these problems, we propose a method for PPI detection by integrating biosensor-based PPI data with a novel computational model. This method was developed based on the algorithm of extreme learning machine combined with a novel representation of protein sequence descriptor. When performed on the large-scale human protein interaction dataset, the proposed method achieved 84.8% prediction accuracy with 84.08% sensitivity at the specificity of 85.53%. We conducted more extensive experiments to compare the proposed method with the state-of-the-art techniques, support vector machine. The achieved results demonstrate that our approach is very promising for detecting new PPIs, and it can be a helpful supplement for biosensor-based PPI data detection. PMID:25215285

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

    PubMed

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

    2013-03-01

    Supramolecular chemistry has recently emerged as a promising way to modulate protein functions, but devising molecules that will interact with a protein in the desired manner is difficult as many competing interactions exist in a biological environment (with solvents, salts or different sites for the target biomolecule). We now show that lysine-specific molecular tweezers bind to a 14-3-3 adapter protein and modulate its interaction with partner proteins. The tweezers inhibit binding between the 14-3-3 protein and two partner proteins--a phosphorylated (C-Raf) protein and an unphosphorylated one (ExoS)--in a concentration-dependent manner. Protein crystallography shows that this effect arises from the binding of the tweezers to a single surface-exposed lysine (Lys214) of the 14-3-3 protein in the proximity of its central channel, which normally binds the partner proteins. A combination of structural analysis and computer simulations provides rules for the tweezers' binding preferences, thus allowing us to predict their influence on this type of protein-protein interactions. PMID:23422566

  17. Techniques for the Analysis of Protein-Protein Interactions in Vivo.

    PubMed

    Xing, Shuping; Wallmeroth, Niklas; Berendzen, Kenneth W; Grefen, Christopher

    2016-06-01

    Identifying key players and their interactions is fundamental for understanding biochemical mechanisms at the molecular level. The ever-increasing number of alternative ways to detect protein-protein interactions (PPIs) speaks volumes about the creativity of scientists in hunting for the optimal technique. PPIs derived from single experiments or high-throughput screens enable the decoding of binary interactions, the building of large-scale interaction maps of single organisms, and the establishment of cross-species networks. This review provides a historical view of the development of PPI technology over the past three decades, particularly focusing on in vivo PPI techniques that are inexpensive to perform and/or easy to implement in a state-of-the-art molecular biology laboratory. Special emphasis is given to their feasibility and application for plant biology as well as recent improvements or additions to these established techniques. The biology behind each method and its advantages and disadvantages are discussed in detail, as are the design, execution, and evaluation of PPI analysis. We also aim to raise awareness about the technological considerations and the inherent flaws of these methods, which may have an impact on the biological interpretation of PPIs. Ultimately, we hope this review serves as a useful reference when choosing the most suitable PPI technique. PMID:27208310

  18. Analyzing protein-protein interactions from affinity purification-mass spectrometry data with SAINT

    PubMed Central

    Choi, Hyungwon; Liu, Guomin; Mellacheruvu, Dattatreya; Tyers, Mike; Gingras, Anne-Claude; Nesvizhskii, Alexey I.

    2012-01-01

    Significance Analysis of INTeractome (SAINT) is a software package for scoring protein-protein interactions based on label-free quantitative proteomics data (e.g. spectral count or intensity) in affinity purification – mass spectrometry (AP-MS) experiments. SAINT allows bench scientists to select bona fide interactions and remove non-specific interactions in an unbiased manner. However, there is no `one-size-fits-all' statistical model for every dataset, since the experimental design varies across studies. Key variables include the number of baits, the number of biological replicates per bait, and control purifications. Here we give a detailed account of input data format, control data, selection of high confidence interactions, and visualization of filtered data. We explain additional options for customizing the statistical model for optimal filtering in specific datasets. We also discuss a graphical user interface of SAINT in connection to the LIMS system ProHits which can be installed as a virtual machine on Mac OSX or PC Windows computers. PMID:22948729

  19. Dynamic imaging of protein-protein interactions by MP-FLIM

    NASA Astrophysics Data System (ADS)

    Ameer-Beg, Simon M.; Peter, Marion; Keppler, Melanie D.; Prag, Soren; Barber, Paul R.; Ng, Tony C.; Vojnovic, Borivoj

    2005-03-01

    The spatio-temporal localization of molecular interactions within cells in situ is of great importance in elucidating the key mechanisms in regulation of fundamental process within the cell. Measurements of such near-field localization of protein complexes may be achieved by the detection of fluorescence (or Forster) resonance energy transfer (FRET) between protein-conjugated fluorophores. We demonstrate the applicability of time-correlated single photon counting multiphoton microscopy to the spatio-temporal localization of protein-protein interactions in live and fixed cell populations. Intramolecular interactions between protein hetero-dimers are investigated using green fluorescent protein variants. We present an improved monomeric form of the red fluorescent protein, mRFP1, as the acceptor in biological fluorescence resonance energy transfer (FRET) experiments using the enhanced green fluorescent protein as donor. We find particular advantage in using this fluorophore pair for quantitative measurements of FRET. The technique was exploited to demonstrate a novel receptor-kinase interaction between the chemokine receptor (CXCR4) and protein kinase C (PKC) α in carcinoma cells for both live and fixed cell experiments.

  20. Novel protein-protein interactions between Entamoeba histolyticad-phosphoglycerate dehydrogenase and phosphoserine aminotransferase.

    PubMed

    Mishra, Vibhor; Kumar, Ashutosh; Ali, Vahab; Nozaki, Tomoyoshi; Zhang, Kam Y J; Bhakuni, Vinod

    2012-08-01

    Physical interactions between d-phosphoglycerate dehydrogenase (EhPGDH) and phosphoserine aminotransferase (EhPSAT) from an enteric human parasite Entamoeba histolytica was observed by pull-down assay, gel filtration chromatography, chemical cross-linking, emission anisotropy, molecular docking and molecular dynamic simulations. The protein-protein complex had a 1:1 stochiometry with a dissociation constant of 3.453 × 10(-7) M. Ionic interactions play a significant role in complex formation and stability. Analysis of the energy minimized average simulated model of the protein complex show that the nucleotide binding domain of EhPGDH specifically interacts with EhPSAT. Denaturation studies suggest that the nucleotide binding domain (Nbd) and substrate binding domain (Sbd) of EhPGDH are independent folding/unfolding units. Thus the Nbd-EhPGDH was separately cloned over-expressed and purified to homogeneity. Fluorescence anisotropy study show that the purified Nbd interacts with EhPSAT. Forward enzyme catalyzed reaction for the EhPGDH-PSAT complex showed efficient Km values for 3-phosphoglyceric acid as compared to only EhPGDH suggesting a possibility of substrate channelling in the protein complex. PMID:22386871

  1. Structure and protein-protein interactions of methanol dehydrogenase from Methylococcus capsulatus (Bath).

    PubMed

    Culpepper, Megen A; Rosenzweig, Amy C

    2014-10-01

    In the initial steps of their metabolic pathway, methanotrophic bacteria oxidize methane to methanol with methane monooxygenases (MMOs) and methanol to formaldehyde with methanol dehydrogenases (MDHs). Several lines of evidence suggest that the membrane-bound or particulate MMO (pMMO) and MDH interact to form a metabolic supercomplex. To further investigate the possible existence of such a supercomplex, native MDH from Methylococcus capsulatus (Bath) has been purified and characterized by size exclusion chromatography with multi-angle light scattering and X-ray crystallography. M. capsulatus (Bath) MDH is primarily a dimer in solution, although an oligomeric species with a molecular mass of ∼450-560 kDa forms at higher protein concentrations. The 2.57 Å resolution crystal structure reveals an overall fold and α2β2 dimeric architecture similar to those of other MDH structures. In addition, biolayer interferometry studies demonstrate specific protein-protein interactions between MDH and M. capsulatus (Bath) pMMO as well as between MDH and the truncated recombinant periplasmic domains of M. capsulatus (Bath) pMMO (spmoB). These interactions exhibit KD values of 833 ± 409 nM and 9.0 ± 7.7 μM, respectively. The biochemical data combined with analysis of the crystal lattice interactions observed in the MDH structure suggest a model in which MDH and pMMO associate not as a discrete, stoichiometric complex but as a larger assembly scaffolded by the intracytoplasmic membranes. PMID:25185034

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

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

  3. New protein-protein interactions of mitochondrial connexin 43 in mouse heart.

    PubMed

    Denuc, Amanda; Núñez, Estefanía; Calvo, Enrique; Loureiro, Marta; Miro-Casas, Elisabet; Guarás, Adela; Vázquez, Jesús; Garcia-Dorado, David

    2016-05-01

    Connexin 43 (Cx43), the gap junction protein involved in cell-to-cell coupling in the heart, is also present in the subsarcolemmal fraction of cardiomyocyte mitochondria. It has been described to regulate mitochondrial potassium influx and respiration and to be important for ischaemic preconditioning protection, although the molecular effectors involved are not fully characterized. In this study, we looked for potential partners of mitochondrial Cx43 in an attempt to identify new molecular pathways for cardioprotection. Mass spectrometry analysis of native immunoprecipitated mitochondrial extracts showed that Cx43 interacts with several proteins related with mitochondrial function and metabolism. Among them, we selected for further analysis only those present in the subsarcolemmal mitochondrial fraction and known to be related with the respiratory chain. Apoptosis-inducing factor (AIF) and the beta-subunit of the electron-transfer protein (ETFB), two proteins unrelated to date with Cx43, fulfilled these conditions, and their interaction with Cx43 was proven by direct and reverse co-immunoprecipitation. Furthermore, a previously unknown molecular interaction between AIF and ETFB was established, and protein content and sub-cellular localization appeared to be independent from the presence of Cx43. Our results identify new protein-protein interactions between AIF-Cx43, ETFB-Cx43 and AIF-ETFB as possible players in the regulation of the mitochondrial redox state. PMID:26915330

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

    PubMed Central

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

    2012-01-01

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

  5. A Protein-Protein Interaction Assay FlimPIA Based on the Functional Complementation of Mutant Firefly Luciferases.

    PubMed

    Ohmuro-Matsuyama, Yuki; Ueda, Hiroshi

    2016-01-01

    There is a significant focus on detecting and assaying protein-protein interactions (PPIs) in biology and biotechnology. Protein-fragment complementation assay (PCA) is one of the most widely used methods to detect PPI by splitting the enzyme-coding or fluorescent protein-coding polypeptide, as well as Förster resonance energy transfer (FRET). Here, we describe a novel PPI assay FlimPIA (firefly luminescent intermediate-based protein-protein interaction assay) by a unique approach of splitting the two major catalytic steps (half reactions) of firefly luciferase (FLuc). PMID:27424900

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

    PubMed

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

    2016-06-01

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

  7. Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.

    PubMed

    Lin, Peng-Lin; Yu, Ya-Wen; Chung, Ren-Hua

    2016-01-01

    Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn's disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn's disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net. PMID:27622767

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

    PubMed

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

    2014-02-17

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

  9. Partner-Aware Prediction of Interacting Residues in Protein-Protein Complexes from Sequence Data

    PubMed Central

    Ahmad, Shandar; Mizuguchi, Kenji

    2011-01-01

    Computational prediction of residues that participate in protein-protein interactions is a difficult task, and state of the art methods have shown only limited success in this arena. One possible problem with these methods is that they try to predict interacting residues without incorporating information about the partner protein, although it is unclear how much partner information could enhance prediction performance. To address this issue, the two following comparisons are of crucial significance: (a) comparison between the predictability of inter-protein residue pairs, i.e., predicting exactly which residue pairs interact with each other given two protein sequences; this can be achieved by either combining conventional single-protein predictions or making predictions using a new model trained directly on the residue pairs, and the performance of these two approaches may be compared: (b) comparison between the predictability of the interacting residues in a single protein (irrespective of the partner residue or protein) from conventional methods and predictions converted from the pair-wise trained model. Using these two streams of training and validation procedures and employing similar two-stage neural networks, we showed that the models trained on pair-wise contacts outperformed the partner-unaware models in predicting both interacting pairs and interacting single-protein residues. Prediction performance decreased with the size of the conformational change upon complex formation; this trend is similar to docking, even though no structural information was used in our prediction. An example application that predicts two partner-specific interfaces of a protein was shown to be effective, highlighting the potential of the proposed approach. Finally, a preliminary attempt was made to score docking decoy poses using prediction of interacting residue pairs; this analysis produced an encouraging result. PMID:22194998

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

    PubMed

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

    2010-01-01

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

  11. A pipeline for determining protein-protein interactions and proximities in the cellular milieu.

    PubMed

    Subbotin, Roman I; Chait, Brian T

    2014-11-01

    It remains extraordinarily challenging to elucidate endogenous protein-protein interactions and proximities within the cellular milieu. The dynamic nature and the large range of affinities of these interactions augment the difficulty of this undertaking. Among the most useful tools for extracting such information are those based on affinity capture of target bait proteins in combination with mass spectrometric readout of the co-isolated species. Although highly enabling, the utility of affinity-based methods is generally limited by difficulties in distinguishing specific from nonspecific interactors, preserving and isolating all unique interactions including those that are weak, transient, or rapidly exchanging, and differentiating proximal interactions from those that are more distal. Here, we have devised and optimized a set of methods to address these challenges. The resulting pipeline involves flash-freezing cells in liquid nitrogen to preserve the cellular environment at the moment of freezing; cryomilling to fracture the frozen cells into intact micron chunks to allow for rapid access of a chemical reagent and to stabilize the intact endogenous subcellular assemblies and interactors upon thawing; and utilizing the high reactivity of glutaraldehyde to achieve sufficiently rapid stabilization at low temperatures to preserve native cellular interactions. In the course of this work, we determined that relatively low molar ratios of glutaraldehyde to reactive amines within the cellular milieu were sufficient to preserve even labile and transient interactions. This mild treatment enables efficient and rapid affinity capture of the protein assemblies of interest under nondenaturing conditions, followed by bottom-up MS to identify and quantify the protein constituents. For convenience, we have termed this approach Stabilized Affinity Capture Mass Spectrometry. Here, we demonstrate that Stabilized Affinity Capture Mass Spectrometry allows us to stabilize and elucidate

  12. Protein-protein interaction networks: unraveling the wiring of molecular machines within the cell.

    PubMed

    De Las Rivas, Javier; Fontanillo, Celia

    2012-11-01

    Mapping and understanding of the protein interaction networks with their key modules and hubs can provide deeper insights into the molecular machinery underlying complex phenotypes. In this article, we present the basic characteristics and definitions of protein networks, starting with a distinction of the different types of associations between proteins. We focus the review on protein-protein interactions (PPIs), a subset of associations defined as physical contacts between proteins that occur by selective molecular docking in a particular biological context. We present such definition as opposed to other types of protein associations derived from regulatory, genetic, structural or functional relations. To determine PPIs, a variety of binary and co-complex methods exist; however, not all the technologies provide the same information and data quality. A way of increasing confidence in a given protein interaction is to integrate orthogonal experimental evidences. The use of several complementary methods testing each single interaction assesses the accuracy of PPI data and tries to minimize the occurrence of false interactions. Following this approach there have been important efforts to unify primary databases of experimentally proven PPIs into integrated databases. These meta-databases provide a measure of the confidence of interactions based on the number of experimental proofs that report them. As a conclusion, we can state that integrated information allows the building of more reliable interaction networks. Identification of communities, cliques, modules and hubs by analysing the topological parameters and graph properties of the protein networks allows the discovery of central/critical nodes, which are candidates to regulate cellular flux and dynamics. PMID:22908212

  13. Prediction of Intra-Species Protein-Protein Interactions in Enteropathogens Facilitating Systems Biology Study

    PubMed Central

    Barman, Ranjan Kumar; Jana, Tanmoy; Das, Santasabuj; Saha, Sudipto

    2015-01-01

    Protein-protein interactions in Escherichia coli (E. coli) has been studied extensively using high throughput methods such as tandem affinity purification followed by mass spectrometry and yeast two-hybrid method. This can in turn be used to understand the mechanisms of bacterial cellular processes. However, experimental characterization of such huge amount of interactions data is not available for other important enteropathogens. Here, we propose a support vector machine (SVM)-based prediction model using the known PPIs data of E. coli that can be used to predict PPIs in other enteropathogens, such as Vibrio cholerae, Salmonella Typhi, Shigella flexneri and Yersinia entrocolitica. Different features such as domain-domain association (DDA), network topology, and sequence information were used in developing the SVM model. The proposed model using DDA, degree and amino acid composition features has achieved an accuracy of 82% and 62% on 5-fold cross validation and blind E. coli datasets, respectively. The predicted interactions were validated by Gene Ontology (GO) semantic similarity measure and String PPIs database (experimental PPIs only). Finally, we have developed a user-friendly webserver named EnPPIpred to predict intra-species PPIs in enteropathogens, which will be of great help for the experimental biologists. The webserver EnPPIpred is freely available at http://bicresources.jcbose.ac.in/ssaha4/EnPPIpred/. PMID:26717407

  14. Using data fusion for scoring reliability of protein-protein interactions.

    PubMed

    Vazifedoost, Alireza; Rahgozar, Maseud; Moshiri, Behzad; Sadeghi, Mehdi; Chua, Hon Nian; Ng, See Kiong; Wong, Limsoon

    2014-08-01

    Protein-protein interactions (PPIs) are important for understanding the cellular mechanisms of biological functions, but the reliability of PPIs extracted by high-throughput assays is known to be low. To address this, many current methods use multiple evidence from different sources of information to compute reliability scores for such PPIs. However, they often combine the evidence without taking into account the uncertainty of the evidence values, potential dependencies between the information sources used and missing values from some information sources. We propose to formulate the task of scoring PPIs using multiple information sources as a multi-criteria decision making problem that can be solved using data fusion to model potential interactions between the multiple information sources. Using data fusion, the amount of contribution from each information source can be proportioned accordingly to systematically score the reliability of PPIs. Our experimental results showed that the reliability scores assigned by our data fusion method can effectively classify highly reliable PPIs from multiple information sources, with substantial improvement in scoring over conventional approach such as the Adjust CD-Distance approach. In addition, the underlying interactions between the information sources used, as well as their relative importance, can also be determined with our data fusion approach. We also showed that such knowledge can be used to effectively handle missing values from information sources. PMID:25152039

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

    PubMed

    Cao, Huaiqing; Huang, Yongqi; Liu, Zhirong

    2016-07-01

    To clarify the interplay between the binding affinity and kinetics of protein-protein interactions, and the possible role of intrinsically disordered proteins in such interactions, molecular simulations were carried out on 20 protein complexes. With bias potential and reweighting techniques, the free energy profiles were obtained under physiological affinities, which showed that the bound-state valley is deep with a barrier height of 12 - 33 RT. From the dependence of the affinity on interface interactions, the entropic contribution to the binding affinity is approximated to be proportional to the interface area. The extracted dissociation rates based on the Arrhenius law correlate reasonably well with the experimental values (Pearson correlation coefficient R = 0.79). For each protein complex, a linear free energy relationship between binding affinity and the dissociation rate was confirmed, but the distribution of the slopes for intrinsically disordered proteins showed no essential difference with that observed for ordered proteins. A comparison with protein folding was also performed. Proteins 2016; 84:920-933. © 2016 Wiley Periodicals, Inc. PMID:27018856

  16. Inferring the Brassica rapa Interactome Using Protein-Protein Interaction Data from Arabidopsis thaliana.

    PubMed

    Yang, Jianhua; Osman, Kim; Iqbal, Mudassar; Stekel, Dov J; Luo, Zewei; Armstrong, Susan J; Franklin, F Chris H

    2012-01-01

    Following successful completion of the Brassica rapa sequencing project, the next step is to investigate functions of individual genes/proteins. For Arabidopsis thaliana, large amounts of protein-protein interaction (PPI) data are available from the major PPI databases (DBs). It is known that Brassica crop species are closely related to A. thaliana. This provides an opportunity to infer the B. rapa interactome using PPI data available from A. thaliana. In this paper, we present an inferred B. rapa interactome that is based on the A. thaliana PPI data from two resources: (i) A. thaliana PPI data from three major DBs, BioGRID, IntAct, and TAIR. (ii) ortholog-based A. thaliana PPI predictions. Linking between B. rapa and A. thaliana was accomplished in three complementary ways: (i) ortholog predictions, (ii) identification of gene duplication based on synteny and collinearity, and (iii) BLAST sequence similarity search. A complementary approach was also applied, which used known/predicted domain-domain interaction data. Specifically, since the two species are closely related, we used PPI data from A. thaliana to predict interacting domains that might be conserved between the two species. The predicted interactome was investigated for the component that contains known A. thaliana meiotic proteins to demonstrate its usability. PMID:23293649

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

    PubMed

    Hoppe, Adam D; Scott, Brandon L; Welliver, Timothy P; Straight, Samuel W; Swanson, Joel A

    2013-01-01

    Fluorescence Resonance Energy Transfer (FRET) microscopy has emerged as a powerful tool to visualize nanoscale protein-protein interactions while capturing their microscale organization and millisecond dynamics. Recently, FRET microscopy was extended to imaging of multiple donor-acceptor pairs, thereby enabling visualization of multiple biochemical events within a single living cell. These methods require numerous equations that must be defined on a case-by-case basis. Here, we present a universal multispectral microscopy method (N-Way FRET) to enable quantitative imaging for any number of interacting and non-interacting FRET pairs. This approach redefines linear unmixing to incorporate the excitation and emission couplings created by FRET, which cannot be accounted for in conventional linear unmixing. Experiments on a three-fluorophore system using blue, yellow and red fluorescent proteins validate the method in living cells. In addition, we propose a simple linear algebra scheme for error propagation from input data to estimate the uncertainty in the computed FRET images. We demonstrate the strength of this approach by monitoring the oligomerization of three FP-tagged HIV Gag proteins whose tight association in the viral capsid is readily observed. Replacement of one FP-Gag molecule with a lipid raft-targeted FP allowed direct observation of Gag oligomerization with no association between FP-Gag and raft-targeted FP. The N-Way FRET method provides a new toolbox for capturing multiple molecular processes with high spatial and temporal resolution in living cells. PMID:23762252

  18. Imaging Beads-Retained Prey Assay for Rapid and Quantitative Protein-Protein Interaction

    PubMed Central

    Zhou, Yan; Hong, Wanjin; Lu, Lei

    2013-01-01

    Conventional Western blot based pull-down methods involve lengthy and laborious work and the results are generally not quantitative. Here, we report the imaging beads-retained prey (IBRP) assay that is rapid and quantitative in studying protein-protein interactions. In this assay, the bait is immobilized onto beads and the prey is fused with a fluorescence protein. The assay takes advantage of the fluorescence of prey and directly quantifies the amount of prey binding to the immobilized bait under a microscope. We validated the assay using previously well studied interactions and found that the amount of prey retained on beads could have a relative linear relationship to both the inputs of bait and prey. IBRP assay provides a universal, fast, quantitative and economical method to study protein interactions and it could be developed to a medium- or high-throughput compatible method. With the availability of fluorescence tagged whole genome ORFs in several organisms, we predict IBRP assay should have wide applications. PMID:23555762

  19. Protein:protein interactions and the pairing of boundary elements in vivo

    PubMed Central

    Blanton, Jason; Gaszner, Miklos; Schedl, Paul

    2003-01-01

    Although it is now well-established that boundary elements/insulators function to subdivide eukaryotic chromosomes into autonomous regulatory domains, the underlying mechanisms remain elusive. One idea is that boundaries act as barriers, preventing the processive spreading of “active” or “silenced” chromatin between domains. Another is that the partitioning into autonomous functional units is a consequence of an underlying structural subdivision of the chromosome into higher order “looped” domains. In this view, boundaries are thought to delimit structural domains by interacting with each other or with some other nuclear structure. The studies reported here provide support for the looped domain model. We show that the Drosophila scs and scs‘ boundary proteins, Zw5 and BEAF, respectively, interact with each other in vitro and in vivo. Moreover, consistent with idea that this protein:protein interaction might facilitate pairing of boundary elements, we find that that scs and scs‘ are in close proximity to each other in Drosophila nuclei. PMID:12629048

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

    PubMed Central

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

    2014-01-01

    Protein complex formed by a group of physical interacting proteins plays a crucial role in cell activities. Great effort has been made to computationally identify protein complexes from protein-protein interaction (PPI) network. However, the accuracy of the prediction is still far from being satisfactory, because the topological structures of protein complexes in the PPI network are too complicated. This paper proposes a novel optimization framework to detect complexes from PPI network, named PLSMC. The method is on the basis of the fact that if two proteins are in a common complex, they are likely to be interacting. PLSMC employs this relation to determine complexes by a penalized least squares method. PLSMC is applied to several public yeast PPI networks, and compared with several state-of-the-art methods. The results indicate that PLSMC outperforms other methods. In particular, complexes predicted by PLSMC can match known complexes with a higher accuracy than other methods. Furthermore, the predicted complexes have high functional homogeneity. PMID:25405206

  1. 3DProIN: Protein-Protein Interaction Networks and Structure Visualization

    PubMed Central

    Li, Hui; Liu, Chunmei

    2015-01-01

    3DProIN is a computational tool to visualize protein–protein interaction networks in both two dimensional (2D) and three dimensional (3D) view. It models protein-protein interactions in a graph and explores the biologically relevant features of the tertiary structures of each protein in the network. Properties such as color, shape and name of each node (protein) of the network can be edited in either 2D or 3D views. 3DProIN is implemented using 3D Java and C programming languages. The internet crawl technique is also used to parse dynamically grasped protein interactions from protein data bank (PDB). It is a java applet component that is embedded in the web page and it can be used on different platforms including Linux, Mac and Window using web browsers such as Firefox, Internet Explorer, Chrome and Safari. It also was converted into a mac app and submitted to the App store as a free app. Mac users can also download the app from our website. 3DProIN is available for academic research at http://bicompute.appspot.com PMID:25664223

  2. N-Way FRET Microscopy of Multiple Protein-Protein Interactions in Live Cells

    PubMed Central

    Hoppe, Adam D.; Scott, Brandon L.; Welliver, Timothy P.; Straight, Samuel W.; Swanson, Joel A.

    2013-01-01

    Fluorescence Resonance Energy Transfer (FRET) microscopy has emerged as a powerful tool to visualize nanoscale protein-protein interactions while capturing their microscale organization and millisecond dynamics. Recently, FRET microscopy was extended to imaging of multiple donor-acceptor pairs, thereby enabling visualization of multiple biochemical events within a single living cell. These methods require numerous equations that must be defined on a case-by-case basis. Here, we present a universal multispectral microscopy method (N-Way FRET) to enable quantitative imaging for any number of interacting and non-interacting FRET pairs. This approach redefines linear unmixing to incorporate the excitation and emission couplings created by FRET, which cannot be accounted for in conventional linear unmixing. Experiments on a three-fluorophore system using blue, yellow and red fluorescent proteins validate the method in living cells. In addition, we propose a simple linear algebra scheme for error propagation from input data to estimate the uncertainty in the computed FRET images. We demonstrate the strength of this approach by monitoring the oligomerization of three FP-tagged HIV Gag proteins whose tight association in the viral capsid is readily observed. Replacement of one FP-Gag molecule with a lipid raft-targeted FP allowed direct observation of Gag oligomerization with no association between FP-Gag and raft-targeted FP. The N-Way FRET method provides a new toolbox for capturing multiple molecular processes with high spatial and temporal resolution in living cells. PMID:23762252

  3. AlphaSpace: Fragment-Centric Topographical Mapping To Target Protein-Protein Interaction Interfaces.

    PubMed

    Rooklin, David; Wang, Cheng; Katigbak, Joseph; Arora, Paramjit S; Zhang, Yingkai

    2015-08-24

    Inhibition of protein-protein interactions (PPIs) is emerging as a promising therapeutic strategy despite the difficulty in targeting such interfaces with drug-like small molecules. PPIs generally feature large and flat binding surfaces as compared to typical drug targets. These features pose a challenge for structural characterization of the surface using geometry-based pocket-detection methods. An attractive mapping strategy--that builds on the principles of fragment-based drug discovery (FBDD)--is to detect the fragment-centric modularity at the protein surface and then characterize the large PPI interface as a set of localized, fragment-targetable interaction regions. Here, we introduce AlphaSpace, a computational analysis tool designed for fragment-centric topographical mapping (FCTM) of PPI interfaces. Our approach uses the alpha sphere construct, a geometric feature of a protein's Voronoi diagram, to map out concave interaction space at the protein surface. We introduce two new features--alpha-atom and alpha-space--and the concept of the alpha-atom/alpha-space pair to rank pockets for fragment-targetability and to facilitate the evaluation of pocket/fragment complementarity. The resulting high-resolution interfacial map of targetable pocket space can be used to guide the rational design and optimization of small molecule or biomimetic PPI inhibitors. PMID:26225450

  4. 2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein-protein interactions.

    PubMed

    Basse, Marie-Jeanne; Betzi, Stéphane; Morelli, Xavier; Roche, Philippe

    2016-01-01

    2P2Idb is a hand-curated structural database dedicated to protein-protein interactions with known small molecule orthosteric modulators. It compiles the structural information related to orthosteric inhibitors and their target [i.e. related 3D structures available in the RCSB Protein Data Bank (PDB)] and provides links to other useful databases. 2P2Idb includes all interactions for which both the protein-protein and protein-inhibitor complexes have been structurally characterized. Since its first release in 2010, the database has grown constantly and the current version contains 27 protein-protein complexes and 274 protein-inhibitor complexes corresponding to 242 unique small molecule inhibitors which represent almost a 5-fold increase compared to the previous version. A number of new data have been added, including new protein-protein complexes, binding affinities, molecular descriptors, precalculated interface parameters and links to other webservers. A new query tool has been implemented to search for inhibitors within the database using standard molecular descriptors. A novel version of the 2P2I-inspector tool has been implemented to calculate a series of physical and chemical parameters of the protein interfaces. Several geometrical parameters including planarity, eccentricity and circularity have been added as well as customizable distance cutoffs. This tool has also been extended to protein-ligand interfaces. The 2P2I database thus represents a wealth of structural source of information for scientists interested in the properties of protein-protein interactions and the design of protein-protein interaction modulators. Database URL: http://2p2idb.cnrs-mrs.fr. PMID:26980515

  5. Protein Cross-Linking Capillary Electrophoresis for Protein-Protein Interaction Analysis.

    PubMed

    Ouimet, Claire M; Shao, Hao; Rauch, Jennifer N; Dawod, Mohamed; Nordhues, Bryce; Dickey, Chad A; Gestwicki, Jason E; Kennedy, Robert T

    2016-08-16

    Capillary electrophoresis (CE) has been identified as a useful platform for detecting, quantifying, and screening for modulators of protein-protein interactions (PPIs). In this method, one protein binding partner is labeled with a fluorophore, the protein binding partners are mixed, and then, the complex is separated from free protein to allow direct determination of bound to free ratios. Although it possesses many advantages for PPI studies, the method is limited by the need to have separation conditions that both prevent protein adsorption to capillary and maintain protein interactions during the separation. In this work, we use protein cross-linking capillary electrophoresis (PXCE) to overcome this limitation. In PXCE, the proteins are cross-linked under binding conditions and then separated. This approach eliminates the need to maintain noncovalent interactions during electrophoresis and facilitates method development. We report PXCE methods for an antibody-antigen interaction and heterodimer and homodimer heat shock protein complexes. Complexes are cross-linked by short treatments with formaldehyde after reaching binding equilibrium. Cross-linked complexes are separated by electrophoretic mobility using free solution CE or by size using sieving electrophoresis of SDS complexes. The method gives good quantitative results; e.g., a lysozyme-antibody interaction was found to have Kd = 24 ± 3 nM by PXCE and Kd = 17 ± 2 nM using isothermal calorimetry (ITC). Heat shock protein 70 (Hsp70) in complex with bcl2 associated athanogene 3 (Bag3) was found to have Kd = 25 ± 5 nM by PXCE which agrees with Kd values reported without cross-linking. Hsp70-Bag3 binding site mutants and small molecule inhibitors of Hsp70-Bag3 were characterized by PXCE with good agreement to inhibitory constants and IC50 values obtained by a bead-based flow cytometry protein interaction assay (FCPIA). PXCE allows rapid method development for quantitative analysis of PPIs. PMID:27434096

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

    PubMed

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

    2013-03-01

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

  7. Modulating non-native aggregation and electrostatic protein-protein interactions with computationally designed single-point mutations.

    PubMed

    O'Brien, C J; Blanco, M A; Costanzo, J A; Enterline, M; Fernandez, E J; Robinson, A S; Roberts, C J

    2016-06-01

    Non-native protein aggregation is a ubiquitous challenge in the production, storage and administration of protein-based biotherapeutics. This study focuses on altering electrostatic protein-protein interactions as a strategy to modulate aggregation propensity in terms of temperature-dependent aggregation rates, using single-charge variants of human γ-D crystallin. Molecular models were combined to predict amino acid substitutions that would modulate protein-protein interactions with minimal effects on conformational stability. Experimental protein-protein interactions were quantified by the Kirkwood-Buff integrals (G22) from laser scattering, and G22 showed semi-quantitative agreement with model predictions. Experimental initial-rates for aggregation showed that increased (decreased) repulsive interactions led to significantly increased (decreased) aggregation resistance, even based solely on single-point mutations. However, in the case of a particular amino acid (E17), the aggregation mechanism was altered by substitution with R or K, and this greatly mitigated improvements in aggregation resistance. The results illustrate that predictions based on native protein-protein interactions can provide a useful design target for engineering aggregation resistance; however, this approach needs to be balanced with consideration of how mutations can impact aggregation mechanisms. PMID:27160179

  8. Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions

    DOE PAGESBeta

    Venkatraman, S.; Doktycz, M. J.; Qi, H.; Morrell-Falvey, J. L.

    2006-01-01

    The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction.more » Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.« less

  9. Femtosecond UV-laser pulses to unveil protein-protein interactions in living cells.

    PubMed

    Itri, Francesco; Monti, Daria M; Della Ventura, Bartolomeo; Vinciguerra, Roberto; Chino, Marco; Gesuele, Felice; Lombardi, Angelina; Velotta, Raffaele; Altucci, Carlo; Birolo, Leila; Piccoli, Renata; Arciello, Angela

    2016-02-01

    A hallmark to decipher bioprocesses is to characterize protein-protein interactions in living cells. To do this, the development of innovative methodologies, which do not alter proteins and their natural environment, is particularly needed. Here, we report a method (LUCK, Laser UV Cross-linKing) to in vivo cross-link proteins by UV-laser irradiation of living cells. Upon irradiation of HeLa cells under controlled conditions, cross-linked products of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were detected, whose yield was found to be a linear function of the total irradiation energy. We demonstrated that stable dimers of GAPDH were formed through intersubunit cross-linking, as also observed when the pure protein was irradiated by UV-laser in vitro. We proposed a defined patch of aromatic residues located at the enzyme subunit interface as the cross-linking sites involved in dimer formation. Hence, by this technique, UV-laser is able to photofix protein surfaces that come in direct contact. Due to the ultra-short time scale of UV-laser-induced cross-linking, this technique could be extended to weld even transient protein interactions in their native context. PMID:26265182

  10. Structural Insights into Protein-Protein Interactions Involved in Bacterial Cell Wall Biogenesis

    PubMed Central

    Laddomada, Federica; Miyachiro, Mayara M.; Dessen, Andréa

    2016-01-01

    The bacterial cell wall is essential for survival, and proteins that participate in its biosynthesis have been the targets of antibiotic development efforts for decades. The biosynthesis of its main component, the peptidoglycan, involves the coordinated action of proteins that are involved in multi-member complexes which are essential for cell division (the “divisome”) and/or cell wall elongation (the “elongasome”), in the case of rod-shaped cells. Our knowledge regarding these interactions has greatly benefitted from the visualization of different aspects of the bacterial cell wall and its cytoskeleton by cryoelectron microscopy and tomography, as well as genetic and biochemical screens that have complemented information from high resolution crystal structures of protein complexes involved in divisome or elongasome formation. This review summarizes structural and functional aspects of protein complexes involved in the cytoplasmic and membrane-related steps of peptidoglycan biosynthesis, with a particular focus on protein-protein interactions whereby disruption could lead to the development of novel antibacterial strategies. PMID:27136593

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

    PubMed

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

    2015-01-01

    Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo. PMID:26684462

  12. Prediction and characterization of protein-protein interaction network in Bacillus licheniformis WX-02.

    PubMed

    Han, Yi-Chao; Song, Jia-Ming; Wang, Long; Shu, Cheng-Cheng; Guo, Jing; Chen, Ling-Ling

    2016-01-01

    In this study, we constructed a protein-protein interaction (PPI) network of B. licheniformis strain WX-02 with interolog method and domain-based method, which contained 15,864 edges and 2,448 nodes. Although computationally predicted networks have relatively low coverage and high false-positive rate, our prediction was confirmed from three perspectives: local structural features, functional similarities and transcriptional correlations. Further analysis of the COG heat map showed that protein interactions in B. licheniformis WX-02 mainly occurred in the same functional categories. By incorporating the transcriptome data, we found that the topological properties of the PPI network were robust under normal and high salt conditions. In addition, 267 different protein complexes were identified and 117 poorly characterized proteins were annotated with certain functions based on the PPI network. Furthermore, the sub-network showed that a hub protein CcpA jointed directly or indirectly many proteins related to γ-PGA synthesis and regulation, such as PgsB, GltA, GltB, ProB, ProJ, YcgM and two signal transduction systems ComP-ComA and DegS-DegU. Thus, CcpA might play an important role in the regulation of γ-PGA synthesis. This study therefore will facilitate the understanding of the complex cellular behaviors and mechanisms of γ-PGA synthesis in B. licheniformis WX-02. PMID:26782814

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

    PubMed

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

    2008-07-01

    Protein-protein interaction (PPI) extraction has been an important research topic in bio-text mining area, since the PPI information is critical for understanding biological processes. However, there are very few open systems available on the Web and most of the systems focus on keyword searching based on predefined PPIs. PIE (Protein Interaction information Extraction system) is a configurable Web service to extract PPIs from literature, including user-provided papers as well as PubMed articles. After providing abstracts or papers, the prediction results are displayed in an easily readable form with essential, yet compact features. The PIE interface supports more features such as PDF file extraction, PubMed search tool and network communication, which are useful for biologists and bio-system developers. The PIE system utilizes natural language processing techniques and machine learning methodologies to predict PPI sentences, which results in high precision performance for Web users. PIE is freely available at http://bi.snu.ac.kr/pie/. PMID:18508809

  14. Label-free detection of protein-protein interactions using a calmodulin-modified nanowire transistor

    PubMed Central

    Lin, Tsung-Wu; Hsieh, Po-Jen; Lin, Chih-Lung; Fang, Yi-Ya; Yang, Jia-Xun; Tsai, Chia-Chang; Chiang, Pei-Ling; Pan, Chien-Yuan; Chen, Yit-Tsong

    2010-01-01

    In this study, we describe a highly sensitive and reusable silicon nanowire field-effect transistor for the detection of protein-protein interactions. This reusable device was made possible by the reversible association of glutathione S-transferase-tagged calmodulin with a glutathione modified transistor. The calmodulin-modified transistor exhibited selective electrical responses to Ca2+ (≥1 μM) and purified cardiac troponin I (∼7 nM); the change in conductivity displayed a linear dependence on the concentration of troponin I in a range from 10 nM to 1 μM. These results are consistent with the previously reported concentration range in which the dissociation constant for the troponin I-calmodulin complex was determined. The minimum concentration of Ca2+ required to activate calmodulin was determined to be 1 μM. We have also successfully demonstrated that the N-type Ca2+ channels, expressed by cultured 293T cells, can be recognized specifically by the calmodulin-modified nanowire transistor. This sensitive nanowire transistor can serve as a high-throughput biosensor and can also substitute for immunoprecipitation methods used in the identification of interacting proteins. PMID:20080536

  15. Prediction and characterization of protein-protein interaction network in Bacillus licheniformis WX-02

    PubMed Central

    Han, Yi-Chao; Song, Jia-Ming; Wang, Long; Shu, Cheng-Cheng; Guo, Jing; Chen, Ling-Ling

    2016-01-01

    In this study, we constructed a protein-protein interaction (PPI) network of B. licheniformis strain WX-02 with interolog method and domain-based method, which contained 15,864 edges and 2,448 nodes. Although computationally predicted networks have relatively low coverage and high false-positive rate, our prediction was confirmed from three perspectives: local structural features, functional similarities and transcriptional correlations. Further analysis of the COG heat map showed that protein interactions in B. licheniformis WX-02 mainly occurred in the same functional categories. By incorporating the transcriptome data, we found that the topological properties of the PPI network were robust under normal and high salt conditions. In addition, 267 different protein complexes were identified and 117 poorly characterized proteins were annotated with certain functions based on the PPI network. Furthermore, the sub-network showed that a hub protein CcpA jointed directly or indirectly many proteins related to γ-PGA synthesis and regulation, such as PgsB, GltA, GltB, ProB, ProJ, YcgM and two signal transduction systems ComP-ComA and DegS-DegU. Thus, CcpA might play an important role in the regulation of γ-PGA synthesis. This study therefore will facilitate the understanding of the complex cellular behaviors and mechanisms of γ-PGA synthesis in B. licheniformis WX-02. PMID:26782814

  16. Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions.

    PubMed

    Fasolo, Joseph; Im, Hogune; Snyder, Michael P

    2015-01-01

    High-density functional protein microarrays containing ~4,200 recombinant yeast proteins are examined for kinase protein-protein interactions using an affinity purified yeast kinase fusion protein containing a V5-epitope tag for read-out. Purified kinase is obtained through culture of a yeast strain optimized for high copy protein production harboring a plasmid containing a Kinase-V5 fusion construct under a GAL inducible promoter. The yeast is grown in restrictive media with a neutral carbon source for 6 hr followed by induction with 2% galactose. Next, the culture is harvested and kinase is purified using standard affinity chromatographic techniques to obtain a highly purified protein kinase for use in the assay. The purified kinase is diluted with kinase buffer to an appropriate range for the assay and the protein microarrays are blocked prior to hybridization with the protein microarray. After the hybridization, the arrays are probed with monoclonal V5 antibody to identify proteins bound by the kinase-V5 protein. Finally, the arrays are scanned using a standard microarray scanner, and data is extracted for downstream informatics analysis to determine a high confidence set of protein interactions for downstream validation in vivo. PMID:26274875

  17. Probing calmodulin protein-protein interactions using high-content protein arrays.

    PubMed

    O'Connell, David J; Bauer, Mikael; Linse, Sara; Cahill, Dolores J

    2011-01-01

    The calcium ion (Ca(2+)) is a ubiquitous second messenger that is crucial for the regulation of a wide variety of cellular processes. The diverse transient signals transduced by Ca(2+) are mediated by intracellular -Ca(2+)-binding proteins. Calcium ions shuttle into and out of the cytosol, transported across membranes by channels, exchangers, and pumps that regulate flux across the ER, mitochondrial and plasma membranes. Calcium regulates both rapid events, such as cytoskeleton remodelling or release of vesicle contents, and slower ones, such as transcriptional changes. Moreover, sustained cytosolic calcium elevations can lead to unwanted cellular activation or apoptosis. Calmodulin represents the most significant of the Ca(2+)-binding proteins and is an essential regulator of intracellular processes in response to extracellular stimuli mediated by a rise in Ca(2+) ion concentration. To profile novel protein-protein interactions that calmodulin participates in, we probed a high-content recombinant human protein array with fluorophore-labelled calmodulin in the presence of Ca(2+). This protein array contains 37,200 redundant proteins, incorporating over 10,000 unique human proteins expressed from a human brain cDNA library. We describe the identification of a high affinity interaction between calmodulin and the single-pass transmembrane proteins STIM1 and STIM2 that localise to the ER. Translocation of STIM1 and STIM2 from the endoplasmic reticulum to the plasma membrane is a key step in store operated calcium entry in the cell. PMID:21901608

  18. Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

    PubMed Central

    Tan, Dan; Li, Qiang; Zhang, Mei-Jun; Liu, Chao; Ma, Chengying; Zhang, Pan; Ding, Yue-He; Fan, Sheng-Bo; Tao, Li; Yang, Bing; Li, Xiangke; Ma, Shoucai; Liu, Junjie; Feng, Boya; Liu, Xiaohui; Wang, Hong-Wei; He, Si-Min; Gao, Ning; Ye, Keqiong; Dong, Meng-Qiu; Lei, Xiaoguang

    2016-01-01

    To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction. DOI: http://dx.doi.org/10.7554/eLife.12509.001 PMID:26952210

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

    PubMed

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

    2014-08-01

    Collagen is a natural protein, which is used as a vital biomaterial in tissue engineering. The major concern about native collagen is lack of its thermal stability and weak resistance to proteolytic degradation. In this scenario, the crosslinking compounds used for stabilization of collagen are mostly of chemical nature and exhibit toxicity. The enzyme mediated crosslinking of collagen provides a novel alternative, nontoxic method for stabilization. In this study, aldehyde forming enzyme (AFE) is used in the bioconversion of hydroxylmethyl groups of collagen to formyl groups that results in the formation of peptidyl aldehyde. The resulted peptidyl aldehyde interacts with bipolar ions of basic amino acid residues of collagen. Further interaction leads to the formation of conjugated double bonds (aldol condensation involving the aldehyde group of peptidyl aldehyde) within the collagen. The enzyme modified collagen matrices have shown an increase in the denaturation temperature, when compared with native collagen. Enzyme modified collagen membranes exhibit resistance toward collagenolytic activity. Moreover, they exhibited a nontoxic nature. The catalytic activity of AFE on collagen as a substrate establishes an efficient modification, which enhances the structural stability of collagen. This finds new avenues in the context of protein-protein stabilization and discovers paramount application in tissue engineering. PMID:25098180

  20. Cell-Binding Assays for Determining the Affinity of Protein-Protein Interactions: Technologies and Considerations.

    PubMed

    Hunter, S A; Cochran, J R

    2016-01-01

    Determining the equilibrium-binding affinity (Kd) of two interacting proteins is essential not only for the biochemical study of protein signaling and function but also for the engineering of improved protein and enzyme variants. One common technique for measuring protein-binding affinities uses flow cytometry to analyze ligand binding to proteins presented on the surface of a cell. However, cell-binding assays require specific considerations to accurately quantify the binding affinity of a protein-protein interaction. Here we will cover the basic assumptions in designing a cell-based binding assay, including the relevant equations and theory behind determining binding affinities. Further, two major considerations in measuring binding affinities-time to equilibrium and ligand depletion-will be discussed. As these conditions have the potential to greatly alter the Kd, methods through which to avoid or minimize them will be provided. We then outline detailed protocols for performing direct- and competitive-binding assays against proteins displayed on the surface of yeast or mammalian cells that can be used to derive accurate Kd values. Finally, a comparison of cell-based binding assays to other types of binding assays will be presented. PMID:27586327

  1. Specific anion effects on the pressure dependence of the protein-protein interaction potential.

    PubMed

    Möller, Johannes; Grobelny, Sebastian; Schulze, Julian; Steffen, Andre; Bieder, Steffen; Paulus, Michael; Tolan, Metin; Winter, Roland

    2014-04-28

    We present a study on ion specific effects on the intermolecular interaction potential V(r) of dense protein solutions under high hydrostatic pressure conditions. Small-angle X-ray scattering in combination with a liquid-state theoretical approach was used to determine the effect of structure breaking/making salt anions (Cl(-), SO4(2-), PO4(3-)) on the intermolecular interaction of lysozyme molecules. It was found that besides the Debye-Hückel charge screening effect, reducing the repulsiveness of the interaction potential V(r) at low salt concentrations, a specific ion effect is observed at high salt concentrations for the multivalent kosmotropic anions, which modulates also the pressure dependence of the protein-protein interaction potential. Whereas sulfate and phosphate strongly influence the pressure dependence of V(r), chloride anions do not. The strong structure-making effect of the multivalent anions, dominating for the triply charged PO4(3-), renders the solution structure less bulk-water-like at high salt concentrations, which leads to an altered behavior of the pressure dependence of V(r). Hence, the particular structural properties of the salt solutions are able to influence the spatial organization and the intermolecular interactions of the proteins, in particular upon compression. These results are of interest for exploring the combined effects of ionic strength, temperature and pressure on the phase behavior of protein solutions, but may also be of relevance for understanding pressure effects on the hydration behavior of biological matter under extreme environmental conditions. PMID:24626853

  2. Lighting the Way to Protein-Protein Interactions: Recommendations on Best Practices for Bimolecular Fluorescence Complementation Analyses.

    PubMed

    Kudla, Jörg; Bock, Ralph

    2016-05-01

    Techniques to detect and verify interactions between proteins in vivo have become invaluable tools in functional genomic research. While many of the initially developed interaction assays (e.g., yeast two-hybrid system and split-ubiquitin assay) usually are conducted in heterologous systems, assays relying on bimolecular fluorescence complementation (BiFC; also referred to as split-YFP assays) are applicable to the analysis of protein-protein interactions in most native systems, including plant cells. Like all protein-protein interaction assays, BiFC can produce false positive and false negative results. The purpose of this commentary is to (1) highlight shortcomings of and potential pitfalls in BiFC assays, (2) provide guidelines for avoiding artifactual interactions, and (3) suggest suitable approaches to scrutinize potential interactions and validate them by independent methods. PMID:27099259

  3. Lighting the Way to Protein-Protein Interactions: Recommendations on Best Practices for Bimolecular Fluorescence Complementation Analyses[OPEN

    PubMed Central

    Kudla, Jörg

    2016-01-01

    Techniques to detect and verify interactions between proteins in vivo have become invaluable tools in functional genomic research. While many of the initially developed interaction assays (e.g., yeast two-hybrid system and split-ubiquitin assay) usually are conducted in heterologous systems, assays relying on bimolecular fluorescence complementation (BiFC; also referred to as split-YFP assays) are applicable to the analysis of protein-protein interactions in most native systems, including plant cells. Like all protein-protein interaction assays, BiFC can produce false positive and false negative results. The purpose of this commentary is to (1) highlight shortcomings of and potential pitfalls in BiFC assays, (2) provide guidelines for avoiding artifactual interactions, and (3) suggest suitable approaches to scrutinize potential interactions and validate them by independent methods. PMID:27099259

  4. Small molecule inhibitors of PSD95-nNOS protein-protein interactions as novel analgesics.

    PubMed

    Lee, Wan-Hung; Xu, Zhili; Ashpole, Nicole M; Hudmon, Andy; Kulkarni, Pushkar M; Thakur, Ganesh A; Lai, Yvonne Y; Hohmann, Andrea G

    2015-10-01

    Aberrant increases in NMDA receptor (NMDAR) signaling contributes to central nervous system sensitization and chronic pain by activating neuronal nitric oxide synthase (nNOS) and generating nitric oxide (NO). Because the scaffolding protein postsynaptic density 95kDA (PSD95) tethers nNOS to NMDARs, the PSD95-nNOS complex represents a therapeutic target. Small molecule inhibitors IC87201 (EC5O: 23.94 μM) and ZL006 (EC50: 12.88 μM) directly inhibited binding of purified PSD95 and nNOS proteins in AlphaScreen without altering binding of PSD95 to ErbB4. Both PSD95-nNOS inhibitors suppressed glutamate-induced cell death with efficacy comparable to MK-801. IC87201 and ZL006 preferentially suppressed phase 2A pain behavior in the formalin test and suppressed allodynia induced by intraplantar complete Freund's adjuvant administration. IC87201 and ZL006 suppressed mechanical and cold allodynia induced by the chemotherapeutic agent paclitaxel (ED50s: 2.47 and 0.93 mg/kg i.p. for IC87201 and ZL006, respectively). Efficacy of PSD95-nNOS disruptors was similar to MK-801. Motor ataxic effects were induced by MK-801 but not by ZL006 or IC87201. Finally, MK-801 produced hyperalgesia in the tail-flick test whereas IC87201 and ZL006 did not alter basal nociceptive thresholds. Our studies establish the utility of using AlphaScreen and purified protein pairs to establish and quantify disruption of protein-protein interactions. Our results demonstrate previously unrecognized antinociceptive efficacy of ZL006 and establish, using two small molecules, a broad application for PSD95-nNOS inhibitors in treating neuropathic and inflammatory pain. Collectively, our results demonstrate that disrupting PSD95-nNOS protein-protein interactions is effective in attenuating pathological pain without producing unwanted side effects (i.e. motor ataxia) associated with NMDAR antagonists. PMID:26071110

  5. Protein-Protein Interactions in high moisture-extruded meat analogs and heat-induce soy Protein Gels

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Two commercial soy protein isolates were made into fibrous meat analogs by high moisture extrusion or into gels by heating and cooling, at varying concentrations and/or temperatures. Protein-protein interactions by extrusion or gelation were investigated through protein solubility studies of raw an...

  6. Light-regulated stapled peptides to inhibit protein-protein interactions involved in clathrin-mediated endocytosis.

    PubMed

    Nevola, Laura; Martín-Quirós, Andrés; Eckelt, Kay; Camarero, Núria; Tosi, Sébastien; Llobet, Artur; Giralt, Ernest; Gorostiza, Pau

    2013-07-22

    Control of membrane traffic: Photoswitchable inhibitors of protein-protein interactions were applied to photoregulate clathrin-mediated endocytosis (CME) in living cells. Traffic light (TL) peptides acting as "stop" and "go" signals for membrane traffic can be used to dissect the role of CME in receptor internalization and in cell growth, division, and differentiation. PMID:23775788

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

    PubMed

    Li, Chuan-Xi; Chen, Peng; Wang, Ru-Jing; Wang, Xiu-Jie; Su, Ya-Ru; Li, Jinyan

    2014-01-01

    Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identification of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifies and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At first, IRO is applied in a binary classifier to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the significant performance of IRO on relation sentences classification and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and BioInfer, respectively, which are superior to most existing extraction methods. PMID:25757257

  8. Quantitation of protein-protein interactions by thermal stability shift analysis.

    PubMed

    Layton, Curtis J; Hellinga, Homme W

    2011-08-01

    Thermal stability shift analysis is a powerful method for examining binding interactions in proteins. We demonstrate that under certain circumstances, protein-protein interactions can be quantitated by monitoring shifts in thermal stability using thermodynamic models and data analysis methods presented in this work. This method relies on the determination of protein stabilities from thermal unfolding experiments using fluorescent dyes such as SYPRO Orange that report on protein denaturation. Data collection is rapid and straightforward using readily available real-time polymerase chain reaction instrumentation. We present an approach for the analysis of the unfolding transitions corresponding to each partner to extract the affinity of the interaction between the proteins. This method does not require the construction of a titration series that brackets the dissociation constant. In thermal shift experiments, protein stability data are obtained at different temperatures according to the affinity- and concentration-dependent shifts in unfolding transition midpoints. Treatment of the temperature dependence of affinity is, therefore, intrinsic to this method and is developed in this study. We used the interaction between maltose-binding protein (MBP) and a thermostable synthetic ankyrin repeat protein (Off7) as an experimental test case because their unfolding transitions overlap minimally. We found that MBP is significantly stabilized by Off7. High experimental throughput is enabled by sample parallelization, and the ability to extract quantitative binding information at a single partner concentration. In a single experiment, we were able to quantify the affinities of a series of alanine mutants, covering a wide range of affinities (∼ 100 nM to ∼ 100 μM). PMID:21674662

  9. Coarse-Grained Antibody Models for "Weak" Protein-Protein Interactions from Low to High Concentrations.

    PubMed

    Calero-Rubio, Cesar; Saluja, Atul; Roberts, Christopher J

    2016-07-14

    So-called "weak" protein-protein interactions are important for the control of solution properties and stability at elevated protein concentrations (c2) but are not practical to capture in atomistic simulations. This report focuses on a series of coarse-grained models for predicting second osmotic virial coefficients (B22) and high-concentration Rayleigh scattering (osmotic compressibility) as a function of c2 for monoclonal antibodies (MAbs) that are of interest in biotechnology. B22 and molecular volume along with c2-dependent osmotic compressibility were calculated for a series of models with increasing structural detail. Models were refined to include contributions from sterics, short-ranged van der Waals and hydrophobic attractions, screened electrostatics, and the flexibility of the mAb hinge region. The results highlight shortcomings for spherical models of MAbs and a useful balance between numerical accuracy and computational burden offered by models based on 6 or 12 spherical, partly overlapping domains. The results provide bounds for realistic values of effective charges on variable domains in order for MAbs to be stable in solution and more generally illustrate semiquantitative bounds for the space of model parameters that can reproduce experimental behavior and provide a basis for future development of computationally efficient and accurate CG mAb models to predict both low- and high-c2 behavior. PMID:27314827

  10. Expanding the Number of “Druggable” Targets: Non-Enzymes and Protein-Protein Interactions

    PubMed Central

    Makley, Leah N.; Gestwicki, Jason E.

    2012-01-01

    Following sequencing and assembly of the human genome, the preferred methods for identification of new drug targets have changed dramatically. Modern tactics such as genome-wide association studies (GWAS) and deep sequencing are fundamentally different from the pharmacology-guided approaches used previously, in which knowledge of small molecule ligands acting at their cellular targets was the primary discovery engine. A consequence of the “target-first, pharmacology-second” strategy is that many predicted drug targets are non-enzymes, such as scaffolding, regulatory or structural proteins, and their activities are often dependent on protein-protein interactions (PPIs). These types of targets create unique challenges to drug discovery efforts because enzymatic turnover cannot be used as a convenient surrogate for compound potency. Moreover, it is often challenging to predict how ligand binding to non-enzymes might affect changes in protein function and/or pathobiology. Thus, in the post-genomic era, targets might be strongly implicated by molecular biology-based methods, yet they often later earn the designation of “undruggable.” Can the scope of available targets be widened to include these promising, but challenging, non-enzymes? In this review, we discuss advances in high throughput screening technology and chemical library design that are emerging to deal with these challenges. PMID:23253128

  11. Gleditsia sinensis: Transcriptome Sequencing, Construction, and Application of Its Protein-Protein Interaction Network

    PubMed Central

    Zhu, Liucun; Zhang, Ying; Guo, Wenna; Wang, Qiang

    2014-01-01

    Gleditsia sinensis is a genus of deciduous tree in the family Caesalpinioideae, native to China, and is of great economic importance. However, despite its economic value, gene sequence information is strongly lacking. In the present study, transcriptome sequencing of G. sinensis was performed resulting in approximately 75.5 million clean reads assembled into 142155 unique transcripts generating 58583 unigenes. The average length of the unigenes was 900 bp, with an N50 of 549 bp. The obtained unigene sequences were then compared to four protein databases to include NCBI nonredundant protein (NRDB), Swiss-prot, Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Cluster of Orthologous Groups (COG). Using BLAST procedure, 31385 unigenes (53.6%) were generated to have functional annotations. Additionally, sequence homologies between identified unigenes and genes of known species in a protein-protein interaction (PPI) network facilitated G. sinensis PPI network construction. Based on this network construction, new stress resistance genes (including cold, drought, and high salinity) were predicted. The present study is the first investigation of genome-wide gene expression in G. sinensis with the results providing a basis for future functional genomic studies relating to this species. PMID:24982878

  12. Small-molecule inhibitors of protein-protein interactions: progressing towards the reality

    PubMed Central

    Arkin, Michelle R.; Tang, Yinyan; Wells, James A.

    2014-01-01

    Summary The past twenty years have seen many advances in our understanding of protein-protein interactions (PPI) and how to target them with small-molecule therapeutics. In 2004, we reviewed some early successes; since then, potent inhibitors have been developed for diverse protein complexes, and compounds are now in clinical trials for six targets. Surprisingly, many of these PPI clinical candidates have efficiency metrics typical of ‘lead-like’ or ‘drug-like’ molecules and are orally available. Successful discovery efforts have integrated multiple disciplines and make use of all the modern tools of target-based discovery - structure, computation, screening, and biomarkers. PPI become progressively more challenging as the interfaces become more complex, i.e., as binding epitopes are displayed on primary, secondary, or tertiary structures. Here, we review the last ten years of progress, focusing on the properties of PPI inhibitors that have advanced to clinical trials and prospects for the future of PPI drug discovery. PMID:25237857

  13. Imbalance in chemical space: How to facilitate the identification of protein-protein interaction inhibitors

    PubMed Central

    Kuenemann, Mélaine A.; Labbé, Céline M.; Cerdan, Adrien H.; Sperandio, Olivier

    2016-01-01

    Protein-protein interactions (PPIs) play vital roles in life and provide new opportunities for therapeutic interventions. In this large data analysis, 3,300 inhibitors of PPIs (iPPIs) were compared to 17 reference datasets of collectively ~566,000 compounds (including natural compounds, existing drugs, active compounds on conventional targets, etc.) using a chemoinformatics approach. Using this procedure, we showed that comparable classes of PPI targets can be formed using either the similarity of their ligands or the shared properties of their binding cavities, constituting a proof-of-concept that not only can binding pockets be used to group PPI targets, but that these pockets certainly condition the properties of their corresponding ligands. These results demonstrate that matching regions in both chemical space and target space can be found. Such identified classes of targets could lead to the design of PPI-class-specific chemical libraries and therefore facilitate the development of iPPIs to the stage of drug candidates. PMID:27034268

  14. Bifunctional Ligands for Inhibition of Tight-Binding Protein-Protein Interactions.

    PubMed

    Ivan, Taavi; Enkvist, Erki; Viira, Birgit; Manoharan, Ganesh Babu; Raidaru, Gerda; Pflug, Alexander; Alam, Kazi Asraful; Zaccolo, Manuela; Engh, Richard Alan; Uri, Asko

    2016-08-17

    The acknowledged potential of small-molecule therapeutics targeting disease-related protein-protein interactions (PPIs) has promoted active research in this field. The strategy of using small molecule inhibitors (SMIs) to fight strong (tight-binding) PPIs tends to fall short due to the flat and wide interfaces of PPIs. Here we propose a biligand approach for disruption of strong PPIs. The potential of this approach was realized for disruption of the tight-binding (KD = 100 pM) tetrameric holoenzyme of cAMP-dependent protein kinase (PKA). Supported by X-ray analysis of cocrystals, bifunctional inhibitors (ARC-inhibitors) were constructed that simultaneously associated with both the ATP-pocket and the PPI interface area of the catalytic subunit of PKA (PKAc). Bifunctional inhibitor ARC-1411, possessing a KD value of 3 pM toward PKAc, induced the dissociation of the PKA holoenzyme with a low-nanomolar IC50, whereas the ATP-competitive inhibitor H89 bound to the PKA holoenzyme without disruption of the protein tetramer. PMID:27389935

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

    PubMed

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

    2013-01-01

    The identification of protein complexes plays a key role in understanding major cellular processes and biological functions. Various computational algorithms have been proposed to identify protein complexes from protein-protein interaction (PPI) networks. In this paper, we first introduce a new seed-selection strategy for seed-growth style algorithms. Cliques rather than individual vertices are employed as initial seeds. After that, a result-modification approach is proposed based on this seed-selection strategy. Predictions generated by higher order clique seeds are employed to modify results that are generated by lower order ones. The performance of this seed-selection strategy and the result-modification approach are tested by using the entropy-based algorithm, which is currently the best seed-growth style algorithm to detect protein complexes from PPI networks. In addition, we investigate four pairs of strategies for this algorithm in order to improve its accuracy. The numerical experiments are conducted on a Saccharomyces cerevisiae PPI network. The group of best predictions consists of 1711 clusters, with the average f-score at 0.68 after removing all similar and redundant clusters. We conclude that higher order clique seeds can generate predictions with higher accuracy and that our improved entropy-based algorithm outputs more reasonable predictions than the original one. PMID:23112006

  16. A novel feature extraction scheme with ensemble coding for protein-protein interaction prediction.

    PubMed

    Du, Xiuquan; Cheng, Jiaxing; Zheng, Tingting; Duan, Zheng; Qian, Fulan

    2014-01-01

    Protein-protein interactions (PPIs) play key roles in most cellular processes, such as cell metabolism, immune response, endocrine function, DNA replication, and transcription regulation. PPI prediction is one of the most challenging problems in functional genomics. Although PPI data have been increasing because of the development of high-throughput technologies and computational methods, many problems are still far from being solved. In this study, a novel predictor was designed by using the Random Forest (RF) algorithm with the ensemble coding (EC) method. To reduce computational time, a feature selection method (DX) was adopted to rank the features and search the optimal feature combination. The DXEC method integrates many features and physicochemical/biochemical properties to predict PPIs. On the Gold Yeast dataset, the DXEC method achieves 67.2% overall precision, 80.74% recall, and 70.67% accuracy. On the Silver Yeast dataset, the DXEC method achieves 76.93% precision, 77.98% recall, and 77.27% accuracy. On the human dataset, the prediction accuracy reaches 80% for the DXEC-RF method. We extended the experiment to a bigger and more realistic dataset that maintains 50% recall on the Yeast All dataset and 80% recall on the Human All dataset. These results show that the DXEC method is suitable for performing PPI prediction. The prediction service of the DXEC-RF classifier is available at http://ailab.ahu.edu.cn:8087/ DXECPPI/index.jsp. PMID:25046746

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

    PubMed

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

    2010-01-01

    We participated (as Team 9) in the Article Classification Task of the Biocreative II.5 Challenge: binary classification of full-text documents relevant for protein-protein interaction. We used two distinct classifiers for the online and offline challenges: 1) the lightweight Variable Trigonometric Threshold (VTT) linear classifier we successfully introduced in BioCreative 2 for binary classification of abstracts and 2) a novel Naive Bayes classifier using features from the citation network of the relevant literature. We supplemented the supplied training data with full-text documents from the MIPS database. The lightweight VTT classifier was very competitive in this new full-text scenario: it was a top-performing submission in this task, taking into account the rank product of the Area Under the interpolated precision and recall Curve, Accuracy, Balanced F-Score, and Matthew's Correlation Coefficient performance measures. The novel citation network classifier for the biomedical text mining domain, while not a top performing classifier in the challenge, performed above the central tendency of all submissions, and therefore indicates a promising new avenue to investigate further in bibliome informatics. PMID:20671313

  18. p97 Disease Mutations Modulate Nucleotide-Induced Conformation to Alter Protein-Protein Interactions.

    PubMed

    Bulfer, Stacie L; Chou, Tsui-Fen; Arkin, Michelle R

    2016-08-19

    The AAA+ ATPase p97/VCP adopts at least three conformations that depend on the binding of ADP and ATP and alter the orientation of the N-terminal protein-protein interaction (PPI) domain into "up" and "down" conformations. Point mutations that cause multisystem proteinopathy 1 (MSP1) are found at the interface of the N domain and D1-ATPase domain and potentially alter the conformational preferences of p97. Additionally, binding of "adaptor" proteins to the N-domain regulates p97's catalytic activity. We propose that p97/adaptor PPIs are coupled to p97 conformational states. We evaluated the binding of nucleotides and the adaptor proteins p37 and p47 to wild-type p97 and MSP1 mutants. Notably, p47 and p37 bind 8-fold more weakly to the ADP-bound conformation of wild-type p97 compared to the ATP-bound conformation. However, MSP1 mutants lose this nucleotide-induced conformational coupling because they destabilize the ADP-bound, "down" conformation of the N-domain. Loss in conformation coupling to PPIs could contribute to the mechanism of MSP1. PMID:27267671

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

    PubMed

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

    2015-01-01

    Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and "interologs" in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria. PMID:26490033

  20. Dissecting the Human Protein-Protein Interaction Network via Phylogenetic Decomposition

    PubMed Central

    Chen, Cho-Yi; Ho, Andy; Huang, Hsin-Yuan; Juan, Hsueh-Fen; Huang, Hsuan-Cheng

    2014-01-01

    The protein-protein interaction (PPI) network offers a conceptual framework for better understanding the functional organization of the proteome. However, the intricacy of network complexity complicates comprehensive analysis. Here, we adopted a phylogenic grouping method combined with force-directed graph simulation to decompose the human PPI network in a multi-dimensional manner. This network model enabled us to associate the network topological properties with evolutionary and biological implications. First, we found that ancient proteins occupy the core of the network, whereas young proteins tend to reside on the periphery. Second, the presence of age homophily suggests a possible selection pressure may have acted on the duplication and divergence process during the PPI network evolution. Lastly, functional analysis revealed that each age group possesses high specificity of enriched biological processes and pathway engagements, which could correspond to their evolutionary roles in eukaryotic cells. More interestingly, the network landscape closely coincides with the subcellular localization of proteins. Together, these findings suggest the potential of using conceptual frameworks to mimic the true functional organization in a living cell. PMID:25412639

  1. Modulation of nociceptive ion channels and receptors via protein-protein interactions: implications for pain relief

    PubMed Central

    Rouwette, Tom; Avenali, Luca; Sondermann, Julia; Narayanan, Pratibha; Gomez-Varela, David; Schmidt, Manuela

    2015-01-01

    In the last 2 decades biomedical research has provided great insights into the molecular signatures underlying painful conditions. However, chronic pain still imposes substantial challenges to researchers, clinicians and patients alike. Under pathological conditions, pain therapeutics often lack efficacy and exhibit only minimal safety profiles, which can be largely attributed to the targeting of molecules with key physiological functions throughout the body. In light of these difficulties, the identification of molecules and associated protein complexes specifically involved in chronic pain states is of paramount importance for designing selective interventions. Ion channels and receptors represent primary targets, as they critically shape nociceptive signaling from the periphery to the brain. Moreover, their function requires tight control, which is usually implemented by protein-protein interactions (PPIs). Indeed, manipulation of such PPIs entails the modulation of ion channel activity with widespread implications for influencing nociceptive signaling in a more specific way. In this review, we highlight recent advances in modulating ion channels and receptors via their PPI networks in the pursuit of relieving chronic pain. Moreover, we critically discuss the potential of targeting PPIs for developing novel pain therapies exhibiting higher efficacy and improved safety profiles. PMID:26039491

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

    PubMed Central

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

    2015-01-01

    Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and “interologs” in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria. PMID:26490033

  3. Imbalance in chemical space: How to facilitate the identification of protein-protein interaction inhibitors

    NASA Astrophysics Data System (ADS)

    Kuenemann, Mélaine A.; Labbé, Céline M.; Cerdan, Adrien H.; Sperandio, Olivier

    2016-04-01

    Protein-protein interactions (PPIs) play vital roles in life and provide new opportunities for therapeutic interventions. In this large data analysis, 3,300 inhibitors of PPIs (iPPIs) were compared to 17 reference datasets of collectively ~566,000 compounds (including natural compounds, existing drugs, active compounds on conventional targets, etc.) using a chemoinformatics approach. Using this procedure, we showed that comparable classes of PPI targets can be formed using either the similarity of their ligands or the shared properties of their binding cavities, constituting a proof-of-concept that not only can binding pockets be used to group PPI targets, but that these pockets certainly condition the properties of their corresponding ligands. These results demonstrate that matching regions in both chemical space and target space can be found. Such identified classes of targets could lead to the design of PPI-class-specific chemical libraries and therefore facilitate the development of iPPIs to the stage of drug candidates.

  4. t-LSE: A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks

    PubMed Central

    Huang, De-Shuang; Wang, Bing

    2013-01-01

    Protein-protein interaction (PPI) networks provide insights into understanding of biological processes, function and the underlying complex evolutionary mechanisms of the cell. Modeling PPI network is an important and fundamental problem in system biology, where it is still of major concern to find a better fitting model that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called t-logistic semantic embedding (t-LSE) to model PPI networks. t-LSE tries to adaptively learn a metric embedding under the simple geometric assumption of PPI networks, and a non-convex cost function was adopted to deal with the noise in PPI networks. The experimental results show the superiority of the fit of t-LSE over other network models to PPI data. Furthermore, the robust loss function adopted here leads to big improvements for dealing with the noise in PPI network. The proposed model could thus facilitate further graph-based studies of PPIs and may help infer the hidden underlying biological knowledge. The Matlab code implementing the proposed method is freely available from the web site: http://home.ustc.edu.cn/~yzh33108/PPIModel.htm. PMID:23560036

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

    PubMed Central

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

    2013-01-01

    Smoking is the primary cause of lung cancer and is linked to 85% of lung cancer cases. However, how lung cancer develops in patients with smoking history remains unclear. Systems approaches that combine human protein-protein interaction (PPI) networks and gene expression data are superior to traditional methods. We performed these systems to determine the role that smoking plays in lung cancer development and used the support vector machine (SVM) model to predict PPIs. By defining expression variance (EV), we found 520 dynamic proteins (EV>0.4) using data from the Human Protein Reference Database and Gene Expression Omnibus Database, and built 7 dynamic PPI subnetworks of lung cancer in patients with smoking history. We also determined the primary functions of each subnetwork: signal transduction, apoptosis, and cell migration and adhesion for subnetwork A; cell-sustained angiogenesis for subnetwork B; apoptosis for subnetwork C; and, finally, signal transduction and cell replication and proliferation for subnetworks D–G. The probability distribution of the degree of dynamic protein and static protein differed, clearly showing that the dynamic proteins were not the core proteins which widely connected with their neighbor proteins. There were high correlations among the dynamic proteins, suggesting that the dynamic proteins tend to form specific dynamic modules. We also found that the dynamic proteins were only correlated with the expression of selected proteins but not all neighbor proteins when cancer occurred. PMID:23149315

  6. Functional and protein-protein interaction network analysis of colorectal cancer induced by ulcerative colitis

    PubMed Central

    DAI, YONG; JIANG, JIN-BO; WANG, YAN-LEI; JIN, ZU-TAO; HU, SAN-YUAN

    2015-01-01

    Colorectal cancer (CRC) is a well-recognized complication of ulcerative colitis (UC), and patients with UC have a higher incidence of CRC, compared with the general population. However, the properties of CRC induced by UC have not been clarified using an interaction network to analyze and compare gene sets. In the present study, six microarray datasets of CRC and UC were extracted from the Array Express database, and gene signatures were identified using the genome-wide relative significance (GWRS) method. Functional analysis was performed based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Prediction of the genes and microRNA were performed using a hypergeometric method. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/proteins, and clusters were obtained through the Molecular Complex Detection algorithm. Topological centrality and a novel analyzing method, based on the rank value of GWGS, were used to characterize the biological importance of the clusters. A total of 217 differentially expressed (DE) genes of CRC were identified, 341 DE genes were identified in UC, and 62 common genes existed in the two. Several KEGG pathways were the same in CRC and UC. Collagenase, progesterone, heparin, urokinase, nadh and adenosine drugs demonstrated potential for use in treatment of CRC and UC. In the PPI network of CRC, 210 nodes and 752 edges were observed, wheras 314 nodes and 882 edges were identified in UC. Cluster 3 in UC had the highest GWGS, while the topological centrality of Cluster 3 in UC had the lowest degree and betweenness. PPI network analysis provided an effective way to estimate and understand the likelihood of the potential connections between proteins/genes. The results obtained following the use of GWGS to analyze differences between clusters did not agree with the topological degree and betweenness centrality, which indicated that gene fold change based GWGS was

  7. Screening of Small-Molecule Inhibitors of Protein-Protein Interaction with Capillary Electrophoresis Frontal Analysis.

    PubMed

    Xu, Mei; Liu, Chao; Zhou, Mi; Li, Qing; Wang, Renxiao; Kang, Jingwu

    2016-08-16

    A simple and effective method for identifying inhibitors of protein-protein interactions (PPIs) was developed by using capillary electrophoresis frontal analysis (CE-FA). Antiapoptotic B-cell-2 (Bcl-2) family member Bcl-XL protein, a 5-carboxyfluorescein labeled peptide truncated from the BH3 domain of Bid (F-Bid) as the ligand, and a known Bcl-XL-Bid interaction inhibitor ABT-263 were employed as an experimental model for the proof of concept. In CE-FA, the free ligand is separated from the protein and protein-ligand complex to permit the measurement of the equilibrium concentration of the ligand, hence the dissociation constant of the protein-ligand complex. In the presence of inhibitors, formation of the protein-ligand complex is hindered, thereby the inhibition can be easily identified by the raised plateau height of the ligand and the decayed plateau of the complex. Further, we proposed an equation used to convert the IC50 value into the inhibition constant Ki value, which is more useful than the former for comparison. In addition, the sample pooling strategy was employed to improve the screening throughput more than 10 times. A small chemical library composed of synthetic compounds and natural extracts were screened with the method, two natural products, namely, demethylzeylasteral and celastrol, were identified as new inhibitors to block the Bcl-XL-Bid interaction. Cell-based assay was performed to validate the activity of the identified compounds. The result demonstrated that CE-FA represents a straightforward and robust technique for screening of PPI inhibitors. PMID:27425825

  8. Functional Mapping of Protein-Protein Interactions in an Enzyme Complex by Directed Evolution

    PubMed Central

    Roderer, Kathrin; Neuenschwander, Martin; Codoni, Giosiana; Sasso, Severin; Gamper, Marianne; Kast, Peter

    2014-01-01

    The shikimate pathway enzyme chorismate mutase converts chorismate into prephenate, a precursor of Tyr and Phe. The intracellular chorismate mutase (MtCM) of Mycobacterium tuberculosis is poorly active on its own, but becomes >100-fold more efficient upon formation of a complex with the first enzyme of the shikimate pathway, 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase (MtDS). The crystal structure of the enzyme complex revealed involvement of C-terminal MtCM residues with the MtDS interface. Here we employed evolutionary strategies to probe the tolerance to substitution of the C-terminal MtCM residues from positions 84–90. Variants with randomized positions were subjected to stringent selection in vivo requiring productive interactions with MtDS for survival. Sequence patterns identified in active library members coincide with residue conservation in natural chorismate mutases of the AroQδ subclass to which MtCM belongs. An Arg-Gly dyad at positions 85 and 86, invariant in AroQδ sequences, was intolerant to mutation, whereas Leu88 and Gly89 exhibited a preference for small and hydrophobic residues in functional MtCM-MtDS complexes. In the absence of MtDS, selection under relaxed conditions identifies positions 84–86 as MtCM integrity determinants, suggesting that the more C-terminal residues function in the activation by MtDS. Several MtCM variants, purified using a novel plasmid-based T7 RNA polymerase gene expression system, showed that a diminished ability to physically interact with MtDS correlates with reduced activatability and feedback regulatory control by Tyr and Phe. Mapping critical protein-protein interaction sites by evolutionary strategies may pinpoint promising targets for drugs that interfere with the activity of protein complexes. PMID:25551646

  9. Measuring cooperative Rev protein-protein interactions on Rev responsive RNA by fluorescence resonance energy transfer

    PubMed Central

    Vercruysse, Thomas; Pawar, Sonalika; De Borggraeve, Wim; Pardon, Els; Pavlakis, George N; Pannecouque, Christophe; Steyaert, Jan; Balzarini, Jan

    2011-01-01

    The export of viral RNA from the nucleus to the cytoplasm of the cellular host is a crucial step in the life cycle of HIV-1 that is mediated by the viral Rev protein. One aspect of the Rev function, its multimerization, is still unexplored as a target for antiviral therapy. This is partly due to the lack of a fast and solid system to measure Rev multimerization. We have developed a high throughput in vitro Rev multimerization assay based on fluorescence resonance energy transfer (FRET) in which real-time Rev-Rev interactions can be measured both in the absence and the presence of Rev specific RRE RNA. Well-characterized Rev multimerization deficient mutants showed reduced FRET as well as unlabeled Rev molecules were able to inhibit the FRET signal demonstrating the specificity of the assay. Upon multimerization along RRE RNA the FRET signal significantly increased but dropped again at equimolar Rev/RRE ratios suggesting that in this condition most Rev molecules are bound as monomers to the RRE. Furthermore, using this assay, we demonstrate that a previously selected llama heavy-chain only antibody was shown to not only prevent the development of Rev multimers but also disassemble the already formed complexes confirming the dynamic nature of the Rev-Rev interactions. The in vitro FRET based multimerization assay facilitates the further study of the basic mechanism of cooperative Rev multimerization along the RRE and is also widely applicable to study the assembly of other functional complexes involving protein homo-multimerization or cooperative protein-protein interactions on RNA or DNA. PMID:21358282

  10. Comparing human-Salmonella with plant-Salmonella protein-protein interaction predictions.

    PubMed

    Schleker, Sylvia; Kshirsagar, Meghana; Klein-Seetharaman, Judith

    2015-01-01

    Salmonellosis is the most frequent foodborne disease worldwide and can be transmitted to humans by a variety of routes, especially via animal and plant products. Salmonella bacteria are believed to use not only animal and human but also plant hosts despite their evolutionary distance. This raises the question if Salmonella employs similar mechanisms in infection of these diverse hosts. Given that most of our understanding comes from its interaction with human hosts, we investigate here to what degree knowledge of Salmonella-human interactions can be transferred to the Salmonella-plant system. Reviewed are recent publications on analysis and prediction of Salmonella-host interactomes. Putative protein-protein interactions (PPIs) between Salmonella and its human and Arabidopsis hosts were retrieved utilizing purely interolog-based approaches in which predictions were inferred based on available sequence and domain information of known PPIs, and machine learning approaches that integrate a larger set of useful information from different sources. Transfer learning is an especially suitable machine learning technique to predict plant host targets from the knowledge of human host targets. A comparison of the prediction results with transcriptomic data shows a clear overlap between the host proteins predicted to be targeted by PPIs and their gene ontology enrichment in both host species and regulation of gene expression. In particular, the cellular processes Salmonella interferes with in plants and humans are catabolic processes. The details of how these processes are targeted, however, are quite different between the two organisms, as expected based on their evolutionary and habitat differences. Possible implications of this observation on evolution of host-pathogen communication are discussed. PMID:25674082

  11. PIPINO: A Software Package to Facilitate the Identification of Protein-Protein Interactions from Affinity Purification Mass Spectrometry Data

    PubMed Central

    Schildbach, Stefan; Blumert, Conny; Horn, Friedemann; von Bergen, Martin; Labudde, Dirk

    2016-01-01

    The functionality of most proteins is regulated by protein-protein interactions. Hence, the comprehensive characterization of the interactome is the next milestone on the path to understand the biochemistry of the cell. A powerful method to detect protein-protein interactions is a combination of coimmunoprecipitation or affinity purification with quantitative mass spectrometry. Nevertheless, both methods tend to precipitate a high number of background proteins due to nonspecific interactions. To address this challenge the software Protein-Protein-Interaction-Optimizer (PIPINO) was developed to perform an automated data analysis, to facilitate the selection of bona fide binding partners, and to compare the dynamic of interaction networks. In this study we investigated the STAT1 interaction network and its activation dependent dynamics. Stable isotope labeling by amino acids in cell culture (SILAC) was applied to analyze the STAT1 interactome after streptavidin pull-down of biotagged STAT1 from human embryonic kidney 293T cells with and without activation. Starting from more than 2,000 captured proteins 30 potential STAT1 interaction partners were extracted. Interestingly, more than 50% of these were already reported or predicted to bind STAT1. Furthermore, 16 proteins were found to affect the binding behavior depending on STAT1 phosphorylation such as STAT3 or the importin subunits alpha 1 and alpha 6. PMID:26966684

  12. Telomere-telomere interactions and candidate telomere binding protein(s) in mammalian sperm cells.

    PubMed

    Zalensky, A O; Tomilin, N V; Zalenskaya, I A; Teplitz, R L; Bradbury, E M

    1997-04-10

    We have used fluorescent in situ hybridization to localize telomeres within the nuclei of sperm from six mammals (human, rat, mouse, stallion, boar, and bull). In minimally swollen sperm of mouse and rat, most of the telomeres are clustered within a limited area in the posterior part of nuclei. In sperm of other species, telomeres associate into tetrameres and dimers. On swelling of sperm cells with heparin/dithiotriethol, telomere associations disperse, and hybridization signals become smaller in size and their numbers approach or correspond to the number of chromosome ends in a haploid genome. Quantitation of telomere loci indicates that dimeric associations are prominent features of mammalian sperm nuclear architecture. Higher order telomere-telomere interactions and organization develop during meiotic stages of human spermatogenesis. At this stage, telomeres also become associated with the nuclear membrane. In an attempt to elucidate the molecular mechanisms underlying telomere interactions in sperm, we have identified a novel protein activity that binds to the double-stranded telomeric repeat (TTAGGG)n. Sperm telomere binding protein(s) (STBP) was extracted from human and bull sperm by 0.5 M NaCl. STBP does not bind single-stranded telomeric DNA and is highly specific for single base substitutions in a duplex DNA sequence. Depending on the conditions of binding, we observed the formation of several nucleoprotein complexes. We have shown that there is a transition between complexes, which indicates that the slower migrating complex is a multimer of the higher mobility one. We propose that STBP participates in association between the telomere domains which were microscopically observed in mammalian spermatozoa. PMID:9141618

  13. Evolutionary Dynamics of Floral Homeotic Transcription Factor Protein-Protein Interactions.

    PubMed

    Bartlett, Madelaine; Thompson, Beth; Brabazon, Holly; Del Gizzi, Robert; Zhang, Thompson; Whipple, Clinton

    2016-06-01

    Protein-protein interactions (PPIs) have widely acknowledged roles in the regulation of development, but few studies have addressed the timing and mechanism of shifting PPIs over evolutionary history. The B-class MADS-box transcription factors, PISTILLATA (PI) and APETALA3 (AP3) are key regulators of floral development. PI-like (PI(L)) and AP3-like (AP3(L)) proteins from a number of plants, including Arabidopsis thaliana (Arabidopsis) and the grass Zea mays (maize), bind DNA as obligate heterodimers. However, a PI(L) protein from the grass relative Joinvillea can bind DNA as a homodimer. To ascertain whether Joinvillea PI(L) homodimerization is an anomaly or indicative of broader trends, we characterized PI(L) dimerization across the Poales and uncovered unexpected evolutionary lability. Both obligate B-class heterodimerization and PI(L) homodimerization have evolved multiple times in the order, by distinct molecular mechanisms. For example, obligate B-class heterodimerization in maize evolved very recently from PI(L) homodimerization. A single amino acid change, fixed during domestication, is sufficient to toggle one maize PI(L) protein between homodimerization and obligate heterodimerization. We detected a signature of positive selection acting on residues preferentially clustered in predicted sites of contact between MADS-box monomers and dimers, and in motifs that mediate MADS PPI specificity in Arabidopsis. Changing one positively selected residue can alter PI(L) dimerization activity. Furthermore, ectopic expression of a Joinvillea PI(L) homodimer in Arabidopsis can homeotically transform sepals into petals. Our results provide a window into the evolutionary remodeling of PPIs, and show that novel interactions have the potential to alter plant form in a context-dependent manner. PMID:26908583

  14. Use of BRET to Study Protein-Protein Interactions In Vitro and In Vivo.

    PubMed

    Dimri, Shalini; Basu, Soumya; De, Abhijit

    2016-01-01

    Application of bioluminescence resonance energy transfer (BRET) assay has been of special value in measuring dynamic events such as protein-protein interactions (PPIs) in vitro or in vivo. It was only in the late 1990s the BRET assay using RLuc-YFP was introduced for biological research showing its use in determining interaction of two proteins involved in circadian rhythm. Several inherent attributes such as rapid and fairly sensitive ratiometric measurements, assessment of PPI irrespective of protein location in cellular compartment, and cost-effectiveness consenting to high-throughput assay development make BRET a popular genetic reporter-based assay for PPI studies. In BRET-based screening, within a defined proximity range of 10-100 Å, excited state energy of the luminescence molecule can excite the acceptor fluorophore in the form of resonance energy transfer, causing it to emit at its characteristic emission wavelength. Based on this principle, several such donor-acceptor pairs, using the Renilla luciferase or its mutants as donor and either GFP2, YFP, mOrange, TagRFP, or TurboFP as acceptor, have been reported for use.In recent years, BRET-related research has become significantly versatile in the assay format and its applicability by adopting the assay on multiple detection devices such as small-animal optical imaging platform or bioluminescence microscope. Beyond the scope of quantitative measurement of PPIs and protein dimerization, molecular optical imaging applications based on BRET assays have broadened its scope for screening of pharmacological compounds by unifying in vitro, live cell, and in vivo animal/plant measurement all on one platform. Taking examples from the literature, this chapter contributes to in-depth methodological details on how to perform in vitro and in vivo BRET experiments, and illustrates its advantages as a single-format assay. PMID:27246334

  15. Multiplex detection of protein-protein interactions using a next generation luciferase reporter.

    PubMed

    Verhoef, Lisette G G C; Mattioli, Michela; Ricci, Fernanda; Li, Yao-Cheng; Wade, Mark

    2016-02-01

    Cell-based assays of protein-protein interactions (PPIs) using split reporter proteins can be used to identify PPI agonists and antagonists. Generally, such assays measure one PPI at a time, and thus counterscreens for on-target activity must be run in parallel or at a subsequent stage; this increases both the cost and time during screening. Split luciferase systems offer advantages over those that use split fluorescent proteins (FPs). This is since split luciferase offers a greater signal:noise ratio and, unlike split FPs, the PPI can be reversed upon small molecule treatment. While multiplexed PPI assays using luciferase have been reported, they suffer from low signal:noise and require fairly complex spectral deconvolution during analysis. Furthermore, the luciferase enzymes used are large, which limits the range of PPIs that can be interrogated due to steric hindrance from the split luciferase fragments. Here, we report a multiplexed PPI assay based on split luciferases from Photinus pyralis (firefly luciferase, FLUC) and the deep-sea shrimp, Oplophorus gracilirostris (NanoLuc, NLUC). Specifically, we show that the binding of the p53 tumor suppressor to its two major negative regulators, MDM2 and MDM4, can be simultaneously measured within the same sample, without the requirement for complex filters or deconvolution. We provide chemical and genetic validation of this system using MDM2-targeted small molecules and mutagenesis, respectively. Combined with the superior signal:noise and smaller size of split NanoLuc, this multiplexed PPI assay format can be exploited to study the induction or disruption of pairwise interactions that are prominent in many cell signaling pathways. PMID:26646257

  16. Interaction prediction using conserved network motifs in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Albert, Reka

    2005-03-01

    High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics. This talk will describe the recent developments in analyzing and understanding protein interaction networks, then present a method that uses the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a ``leave-one-ou approach we find average success rates between 20-50% for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network. A reference implementation and a table with candidate interacting partners for each yeast protein are available at http://www.protsuggest.org

  17. SEBINI-CABIN: An Analysis Pipeline for Biological Network Inference, with a Case Study in Protein-Protein Interaction Network Reconstruction

    SciTech Connect

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

    2007-12-01

    One of the core tasks of the emerging discipline of systems biology is the reconstruction of the various biological networks in an organism. The importance of understanding such regulatory, interaction, and signaling networks has fueled the development by bioinformatics researchers of many inference algorithms for determining their structure. The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment, testing, and improvement of algorithms used to reconstruct the structures of regulatory and interaction networks from high-throughput expression data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN) tool, a software package for exploratory data analysis that allows basic integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. Thus, the combined SEBINI–CABIN platform aids in the more accurate determination of biological networks, in less time, with less effort. In this paper, we present a case study demonstrating the use of the SEBINI and CABIN tools for protein-protein interaction network reconstruction. Incorporating the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro) algorithm into the SEBINI toolkit, we have created a pipeline for structural inference and supplemental analysis of protein-protein interaction networks from sets of mass spectrometry bait-prey experiment data. To the best of our knowledge the pipeline so designed is the first to be publicly available for such use. A demonstration web site for SEBINI can be accessed from https://www.emsl.pnl.gov/NIT/NIT.html. Source code and PostgreSQL database schema are available under open source license. Contact: ronald.taylor@pnl.gov. For commercial use, some algorithms included in SEBINI require licensing from the original developers. The

  18. Recapitulating the α-helix: nonpeptidic, low-molecular-weight ligands for the modulation of helix-mediated protein-protein interactions.

    PubMed

    Lanning, Maryanna; Fletcher, Steven

    2013-12-01

    Protein-protein interactions play critical roles in a wide variety of biological processes, and their dysregulations contribute to the pathogenesis of several diseases, including cancer. Chemical entities that can abrogate aberrant protein-protein interactions may provide novel therapeutic agents. A large number of protein-protein interactions are mediated by protein secondary structure, the most commonly encountered form of which is the α-helix. Accordingly, over the last decade, there has been a flood of nonpeptidic small molecules that recapitulate the projection and chemical nature of key side chains of the canonical α-helix as a strategy to disrupt helix-mediated protein-protein interactions. In this review, we discuss recent advances (post 2006) in the design of synthetic α-helix mimetics, which include single-faced and two-faced/amphipathic structures, for the modulation of protein-protein interactions. PMID:24261892

  19. Abundance and Temperature Dependency of Protein-Protein Interaction Revealed by Interface Structure Analysis and Stability Evolution

    NASA Astrophysics Data System (ADS)

    He, Yi-Ming; Ma, Bin-Guang

    2016-05-01

    Protein complexes are major forms of protein-protein interactions and implement essential biological functions. The subunit interface in a protein complex is related to its thermostability. Though the roles of interface properties in thermal adaptation have been investigated for protein complexes, the relationship between the interface size and the expression level of the subunits remains unknown. In the present work, we studied this relationship and found a positive correlation in thermophiles rather than mesophiles. Moreover, we found that the protein interaction strength in complexes is not only temperature-dependent but also abundance-dependent. The underlying mechanism for the observed correlation was explored by simulating the evolution of protein interface stability, which highlights the avoidance of misinteraction. Our findings make more complete the picture of the mechanisms for protein complex thermal adaptation and provide new insights into the principles of protein-protein interactions.

  20. Abundance and Temperature Dependency of Protein-Protein Interaction Revealed by Interface Structure Analysis and Stability Evolution

    PubMed Central

    He, Yi-Ming; Ma, Bin-Guang

    2016-01-01

    Protein complexes are major forms of protein-protein interactions and implement essential biological functions. The subunit interface in a protein complex is related to its thermostability. Though the roles of interface properties in thermal adaptation have been investigated for protein complexes, the relationship between the interface size and the expression level of the subunits remains unknown. In the present work, we studied this relationship and found a positive correlation in thermophiles rather than mesophiles. Moreover, we found that the protein interaction strength in complexes is not only temperature-dependent but also abundance-dependent. The underlying mechanism for the observed correlation was explored by simulating the evolution of protein interface stability, which highlights the avoidance of misinteraction. Our findings make more complete the picture of the mechanisms for protein complex thermal adaptation and provide new insights into the principles of protein-protein interactions. PMID:27220911

  1. Controllability of protein-protein interaction phosphorylation-based networks: Participation of the hub 14-3-3 protein family.

    PubMed

    Uhart, Marina; Flores, Gabriel; Bustos, Diego M

    2016-01-01

    Posttranslational regulation of protein function is an ubiquitous mechanism in eukaryotic cells. Here, we analyzed biological properties of nodes and edges of a human protein-protein interaction phosphorylation-based network, especially of those nodes critical for the network controllability. We found that the minimal number of critical nodes needed to control the whole network is 29%, which is considerably lower compared to other real networks. These critical nodes are more regulated by posttranslational modifications and contain more binding domains to these modifications than other kinds of nodes in the network, suggesting an intra-group fast regulation. Also, when we analyzed the edges characteristics that connect critical and non-critical nodes, we found that the former are enriched in domain-to-eukaryotic linear motif interactions, whereas the later are enriched in domain-domain interactions. Our findings suggest a possible structure for protein-protein interaction networks with a densely interconnected and self-regulated central core, composed of critical nodes with a high participation in the controllability of the full network, and less regulated peripheral nodes. Our study offers a deeper understanding of complex network control and bridges the controllability theorems for complex networks and biological protein-protein interaction phosphorylation-based networked systems. PMID:27195976

  2. Controllability of protein-protein interaction phosphorylation-based networks: Participation of the hub 14-3-3 protein family

    PubMed Central

    Uhart, Marina; Flores, Gabriel; Bustos, Diego M.

    2016-01-01

    Posttranslational regulation of protein function is an ubiquitous mechanism in eukaryotic cells. Here, we analyzed biological properties of nodes and edges of a human protein-protein interaction phosphorylation-based network, especially of those nodes critical for the network controllability. We found that the minimal number of critical nodes needed to control the whole network is 29%, which is considerably lower compared to other real networks. These critical nodes are more regulated by posttranslational modifications and contain more binding domains to these modifications than other kinds of nodes in the network, suggesting an intra-group fast regulation. Also, when we analyzed the edges characteristics that connect critical and non-critical nodes, we found that the former are enriched in domain-to-eukaryotic linear motif interactions, whereas the later are enriched in domain-domain interactions. Our findings suggest a possible structure for protein-protein interaction networks with a densely interconnected and self-regulated central core, composed of critical nodes with a high participation in the controllability of the full network, and less regulated peripheral nodes. Our study offers a deeper understanding of complex network control and bridges the controllability theorems for complex networks and biological protein-protein interaction phosphorylation-based networked systems. PMID:27195976

  3. Protein-Protein Interaction Network could reveal the relationship between the breast and colon cancer

    PubMed Central

    Zamanian-Azodi, Mona; Rezaei-Tavirani, Mostafa; Rahmati-Rad, Sara; Hasanzadeh, Hadi; Rezaei Tavirani, Majid; Seyyedi, Samaneh Sadat

    2015-01-01

    Aim: This study is aimed to elicit the possible correlation between breast and colon cancer from molecular prospective by analyzing and comparing pathway-based biomarkers. Background: Breast and colon cancer are known to be frequent causes of morbidity and mortality in men and women around the world. There is some evidence that while the incident of breast cancer in young women is high, it is reported lower in the aged women. In fact, aged women are more prone to colorectal cancer than older men. . In addition, many studies showed that several biomarkers are common among these malignancies. Patients and methods: The genes were retrieved and compared from KEGG database and WikiPathway, and subsequently, protein-protein interaction (PPI) network was constructed and analyzed using Cytoscape v:3.2.1 software and related algorithms. Results: More than forty common genes were identified among these malignancies; however, by pathways comparison, twenty genes are related to both breast and colon cancer. Centrality and cluster screening identified hub genes, including SMAD2, SMAD3, (SMAD4, MYC), JUN, BAD, TP53. These seven genes are enriched in regulation of transforming growth factor beta receptor signaling pathway, positive regulation of Rac protein signal transduction, positive regulation of mitochondrial outer membrane permeabilization involved in apoptotic signaling pathway, and positive regulation of mitotic metaphase/anaphase transition respectively. Conclusion: As there are numerous genes frequent between colorectal cancer and breast cancer, there may be a common molecular origin for these malignancies occurrences. It seems that breast cancer in females interferes with the rate of colorectal cancer incidence. PMID:26328044

  4. Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics.

    PubMed

    Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Gui, Jie; Nie, Ru

    2016-01-01

    Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research. PMID:27571061

  5. Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

    PubMed Central

    CHEN, CHEN; SHEN, HONG; ZHANG, LI-GUO; LIU, JIAN; CAO, XIAO-GE; YAO, AN-LIANG; KANG, SHAO-SAN; GAO, WEI-XING; HAN, HUI; CAO, FENG-HONG; LI, ZHI-GUO

    2016-01-01

    Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa. PMID:27121963

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

    PubMed

    Wu, Hao; Gao, Lin; Dong, Jihua; Yang, Xiaofei

    2014-01-01

    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. PMID:24642838

  7. Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer.

    PubMed

    Chen, Chen; Shen, Hong; Zhang, Li-Guo; Liu, Jian; Cao, Xiao-Ge; Yao, An-Liang; Kang, Shao-San; Gao, Wei-Xing; Han, Hui; Cao, Feng-Hong; Li, Zhi-Guo

    2016-06-01

    Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa. PMID:27121963

  8. Learning an enriched representation from unlabeled data for protein-protein interaction extraction

    PubMed Central

    2010-01-01

    Background Extracting protein-protein interactions from biomedical literature is an important task in biomedical text mining. Supervised machine learning methods have been used with great success in this task but they tend to suffer from data sparseness because of their restriction to obtain knowledge from limited amount of labelled data. In this work, we study the use of unlabeled biomedical texts to enhance the performance of supervised learning for this task. We use feature coupling generalization (FCG) – a recently proposed semi-supervised learning strategy – to learn an enriched representation of local contexts in sentences from 47 million unlabeled examples and investigate the performance of the new features on AIMED corpus. Results The new features generated by FCG achieve a 60.1 F-score and produce significant improvement over supervised baselines. The experimental analysis shows that FCG can utilize well the sparse features which have little effect in supervised learning. The new features perform better in non-linear classifiers than linear ones. We combine the new features with local lexical features, obtaining an F-score of 63.5 on AIMED corpus, which is comparable with the current state-of-the-art results. We also find that simple Boolean lexical features derived only from local contexts are able to achieve competitive results against most syntactic feature/kernel based methods. Conclusions FCG creates a lot of opportunities for designing new features, since a lot of sparse features ignored by supervised learning can be utilized well. Interestingly, our results also demonstrate that the state-of-the art performance can be achieved without using any syntactic information in this task. PMID:20406505

  9. Detecting Overlapping Protein Complexes by Rough-Fuzzy Clustering in Protein-Protein Interaction Networks

    PubMed Central

    Wu, Hao; Gao, Lin; Dong, Jihua; Yang, Xiaofei

    2014-01-01

    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. PMID:24642838

  10. PADPIN: protein-protein interaction networks of angiogenesis, arteriogenesis, and inflammation in peripheral arterial disease

    PubMed Central

    Vijay, Chaitanya G.; Annex, Brian H.; Bader, Joel S.; Popel, Aleksander S.

    2015-01-01

    Peripheral arterial disease (PAD) results from an obstruction of blood flow in the arteries other than the heart, most commonly the arteries that supply the legs. The complexity of the known signaling pathways involved in PAD, including various growth factor pathways and their cross talks, suggests that analyses of high-throughput experimental data could lead to a new level of understanding of the disease as well as novel and heretofore unanticipated potential targets. Such bioinformatic analyses have not been systematically performed for PAD. We constructed global protein-protein interaction networks of angiogenesis (Angiome), immune response (Immunome), and arteriogenesis (Arteriome) using our previously developed algorithm GeneHits. The term “PADPIN” refers to the angiome, immunome, and arteriome in PAD. Here we analyze four microarray gene expression datasets from ischemic and nonischemic gastrocnemius muscles at day 3 posthindlimb ischemia (HLI) in two genetically different C57BL/6 and BALB/c mouse strains that display differential susceptibility to HLI to identify potential targets and signaling pathways in angiogenesis, immune, and arteriogenesis networks. We hypothesize that identification of the differentially expressed genes in ischemic and nonischemic muscles between the strains that recovers better (C57BL/6) vs. the strain that recovers more poorly (BALB/c) will help for the prediction of target genes in PAD. Our bioinformatics analysis identified several genes that are differentially expressed between the two mouse strains with known functions in PAD including TLR4, THBS1, and PRKAA2 and several genes with unknown functions in PAD including EphA4, TSPAN7, SLC22A4, and EIF2a. PMID:26058837

  11. Identifying transient protein-protein interactions in EphB2 signaling by Blue Native PAGE and Mass Spectrometry

    PubMed Central

    Darie, Costel C.; Deinhardt, Katrin; Zhang, Guoan; Cardasis, Helene S.; Chao, Moses V.; Neubert, Thomas A.

    2012-01-01

    Receptor tyrosine kinases (RTKs) are proteins that upon ligand stimulation undergo dimerization and autophosphorylation. Eph receptors (EphRs) are RTKs that are found in different cell types, from both tissues that are developing and from mature tissues and play important roles in the development of the central nervous system and peripheral nervous system. EphRs also play roles in synapse formation, neural crest formation, angiogenesis and in remodeling the vascular system. Interaction of EphRs with their ephrin ligands leads to activation of signal transduction pathways and to formation of many transient protein-protein interactions that ultimately leads to cytoskeletal remodeling. However, the sequence of events at the molecular level is not well-understood. We used Blue Native PAGE (BN-PAGE) and mass spectrometry (MS) to analyze the transient protein-protein interactions that resulted from stimulation of EphB2 receptors by their ephrinB1-Fc ligands. We analyzed the phosphotyrosine-containing protein complexes immunoprecipitated (pY-IPs) from the cell lysates of both unstimulated (−) and ephrinB1-Fc-stimulated (+) NG108 cells. Our experiments allowed us to identify many signaling proteins, either known to be part of EphB2 signaling or new for this pathway, which are involved in transient protein-protein interactions upon ephrinB1-Fc stimulation. These data led us to investigate the roles in EphB2 signaling of proteins such as FAK, WAVEs, and Nischarin. PMID:21932443

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

    PubMed

    López, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups. Database URL: http://hintdb.hgc.jp/htp. PMID:26708988

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

    PubMed

    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

    2016-01-01

    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. PMID:27447450

  14. GeneSense: a new approach for human gene annotation integrated with protein-protein interaction networks

    PubMed Central

    Chen, Zhongzhong; Zhang, Tianhong; Lin, Jun; Yan, Zidan; Wang, Yongren; Zheng, Weiqiang; Weng, Kevin C.

    2014-01-01

    Virtually all cellular functions involve protein-protein interactions (PPIs). As an increasing number of PPIs are identified and vast amount of information accumulated, researchers are finding different ways to interrogate the data and understand the interactions in context. However, it is widely recognized that a significant portion of the data is scattered, redundant, not considered high quality, and not readily accessible to researchers in a systematic fashion. In addition, it is challenging to identify the optimal protein targets in the current PPI networks. The GeneSense server was developed to integrate gene annotation and PPI networks in an expandable architecture that incorporates selected databases with the aim to assemble, analyze, evaluate and disseminate protein-protein association information in a comprehensive and user-friendly manner. Three network models including nodenet, leafnet and loopnet are used to identify the optimal protein targets in the complex networks. GeneSense is freely available at www.biomedsense.org/genesense.php. PMID:24667292

  15. An approach to improve kernel-based Protein-Protein Interaction extraction by learning from large-scale network data.

    PubMed

    Li, Lishuang; Guo, Rui; Jiang, Zhenchao; Huang, Degen

    2015-07-15

    Protein-Protein Interaction extraction (PPIe) from biomedical literatures is an important task in biomedical text mining and has achieved desirable results on the annotated datasets. However, the traditional machine learning methods on PPIe suffer badly from vocabulary gap and data sparseness, which weakens classification performance. In this work, an approach capturing external information from the web-based data is introduced to address these problems and boost the existing methods. The approach involves three kinds of word representation techniques: distributed representation, vector clustering and Brown clusters. Experimental results show that our method outperforms the state-of-the-art methods on five publicly available corpora. Our code and data are available at: http://chaoslog.com/improving-kernel-based-protein-protein-interaction-extraction-by-unsupervised-word-representation-codes-and-data.html. PMID:25864936

  16. Global multiple protein-protein interaction network alignment by combining pairwise network alignments

    PubMed Central

    2015-01-01

    Background A wealth of protein interaction data has become available in recent years, creating an urgent need for powerful analysis techniques. In this context, the problem of finding biologically meaningful correspondences between different protein-protein interaction networks (PPIN) is of particular interest. The PPIN of a species can be compared with that of other species through the process of PPIN alignment. Such an alignment can provide insight into basic problems like species evolution and network component function determination, as well as translational problems such as target identification and elucidation of mechanisms of disease spread. Furthermore, multiple PPINs can be aligned simultaneously, expanding the analytical implications of the result. While there are several pairwise network alignment algorithms, few methods are capable of multiple network alignment. Results We propose SMAL, a MNA algorithm based on the philosophy of scaffold-based alignment. SMAL is capable of converting results from any global pairwise alignment algorithms into a MNA in linear time. Using this method, we have built multiple network alignments based on combining pairwise alignments from a number of publicly available (pairwise) network aligners. We tested SMAL using PPINs of eight species derived from the IntAct repository and employed a number of measures to evaluate performance. Additionally, as part of our experimental investigations, we compared the effectiveness of SMAL while aligning up to eight input PPINs, and examined the effect of scaffold network choice on the alignments. Conclusions A key advantage of SMAL lies in its ability to create MNAs through the use of pairwise network aligners for which native MNA implementations do not exist. Experiments indicate that the performance of SMAL was comparable to that of the native MNA implementation of established methods such as IsoRankN and SMETANA. However, in terms of computational time, SMAL was significantly faster

  17. An integrated strategy for the discovery of drug targets by the analysis of protein-protein interactions

    NASA Astrophysics Data System (ADS)

    Peltier, John M.; Askovic, Srdjan; Becklin, Robert R.; Chepanoske, Cindy Lou; Ho, Yew-Seng J.; Kery, Vladimir; Lai, Shuping; Mujtaba, Tahmina; Pyne, Mike; Robbins, Paul B.; Rechenberg, Moritz Von; Richardson, Bonnie; Savage, Justin; Sheffield, Peter; Thompson, Sam; Weir, Lawrence; Widjaja, Kartika; Xu, Nafei; Zhen, Yuejun; Boniface, J. Jay

    2004-11-01

    Proteomics-based technologies have the potential to accelerate the development of drugs, but such technologies must be well integrated in order to have a positive impact. We describe, herein, a multi-step process for the discovery of protein-protein interactions. It is shown that process stages are interdependent and can influence, either positively or negatively, subsequent steps. Optimization of each step, in the context of the full process, is essential for the overall success of the experiment.

  18. Local Geometry and Evolutionary Conservation of Protein Surfaces Reveal the Multiple Recognition Patches in Protein-Protein Interactions

    PubMed Central

    Laine, Elodie; Carbone, Alessandra

    2015-01-01

    Protein-protein interactions (PPIs) are essential to all biological processes and they represent increasingly important therapeutic targets. Here, we present a new method for accurately predicting protein-protein interfaces, understanding their properties, origins and binding to multiple partners. Contrary to machine learning approaches, our method combines in a rational and very straightforward way three sequence- and structure-based descriptors of protein residues: evolutionary conservation, physico-chemical properties and local geometry. The implemented strategy yields very precise predictions for a wide range of protein-protein interfaces and discriminates them from small-molecule binding sites. Beyond its predictive power, the approach permits to dissect interaction surfaces and unravel their complexity. We show how the analysis of the predicted patches can foster new strategies for PPIs modulation and interaction surface redesign. The approach is implemented in JET2, an automated tool based on the Joint Evolutionary Trees (JET) method for sequence-based protein interface prediction. JET2 is freely available at www.lcqb.upmc.fr/JET2. PMID:26690684

  19. A cautionary note on the use of split-YFP/BiFC in plant protein-protein interaction studies.

    PubMed

    Horstman, Anneke; Tonaco, Isabella Antonia Nougalli; Boutilier, Kim; Immink, Richard G H

    2014-01-01

    Since its introduction in plants 10 years ago, the bimolecular fluorescence complementation (BiFC) method, or split-YFP (yellow fluorescent protein), has gained popularity within the plant biology field as a method to study protein-protein interactions. BiFC is based on the restoration of fluorescence after the two non-fluorescent halves of a fluorescent protein are brought together by a protein-protein interaction event. The major drawback of BiFC is that the fluorescent protein halves are prone to self-assembly independent of a protein-protein interaction event. To circumvent this problem, several modifications of the technique have been suggested, but these modifications have not lead to improvements in plant BiFC protocols. Therefore, it remains crucial to include appropriate internal controls. Our literature survey of recent BiFC studies in plants shows that most studies use inappropriate controls, and a qualitative rather than quantitative read-out of fluorescence. Therefore, we provide a cautionary note and beginner's guideline for the setup of BiFC experiments, discussing each step of the protocol, including vector choice, plant expression systems, negative controls, and signal detection. In addition, we present our experience with BiFC with respect to self-assembly, peptide linkers, and incubation temperature. With this note, we aim to provide a guideline that will improve the quality of plant BiFC experiments. PMID:24886811

  20. Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

    PubMed Central

    Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

    2015-01-01

    Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases. PMID:26608097

  1. Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

    NASA Astrophysics Data System (ADS)

    Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

    2015-11-01

    Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases.

  2. Molecular insights into the stabilization of protein-protein interactions with small molecule: The FKBP12-rapamycin-FRB case study

    NASA Astrophysics Data System (ADS)

    Chaurasia, Shilpi; Pieraccini, Stefano; De Gonda, Riccardo; Conti, Simone; Sironi, Maurizio

    2013-11-01

    Targetting protein-protein interactions is a challenging task in drug discovery process. Despite the challenges, several studies provided evidences for the development of small molecules modulating protein-protein interactions. Here we consider a typical case of protein-protein interaction stabilization: the complex between FKBP12 and FRB with rapamycin. We have analyzed the stability of the complex and characterized its interactions at the atomic level by performing free energy calculations and computational alanine scanning. It is shown that rapamycin stabilizes the complex by acting as a bridge between the two proteins; and the complex is stable only in the presence of rapamycin.

  3. Bimolecular Fluorescence Complementation (BiFC) Assay for Direct Visualization of Protein-Protein Interaction in vivo

    PubMed Central

    Lai, Hsien-Tsung; Chiang, Cheng-Ming

    2016-01-01

    Bimolecular Fluorescence Complementation (BiFC) assay is a method used to directly visualize protein-protein interaction in vivo using live-cell imaging or fixed cells. This protocol described here is based on our recent paper describing the functional association of human chromatin adaptor and transcription cofactor Brd4 with p53 tumor suppressor protein (Wu et al., 2013). BiFC was first described by Hu et al. (2002) using two non-fluorescent protein fragments of enhanced yellow fluorescent protein (EYFP), which is an Aequorea victoria GFP variant protein, fused respectively to a Rel family protein and a bZIP family transcription factor to investigate interactions between these two family members in living cells. The YFP was later improved by introducing mutations to reduce its sensitivity to pH and chloride ions, thus generating a super-enhanced YFP, named Venus fluorescent protein, without showing diminished fluorescence at 37 °C as typically observed with EYFP (Nagai et al., 2006). The fluorescence signal is regenerated by complementation of two non-fluorescent fragments (e.g., the Venus N-terminal 1–158 amino acid residues, called Venus-N, and its C-terminal 159–239 amino acid residues, named Venus-C; see Figure 1A and Gully et al., 2012; Ding et al., 2006; Kerppola, 2006) that are brought together by interaction between their respective fusion partners (e.g., Venus-N to p53, and Venus-C to the PDID domain of human Brd4; see Figure 1B and 1C). The intensity and cellular location of the regenerated fluorescence signals can be detected by fluorescence microscope. The advantages of the proximity-based BiFC assay are: first, it allows a direct visualization of spatial and temporal interaction between two partner proteins in vivo; second, the fluorescence signal provides a sensitive readout for detecting protein-protein interaction even at a low expression level comparable to that of the endogenous proteins; third, the intensity of the fluorescence signal is

  4. Protein-protein interaction and gene co-expression maps of ARFs and Aux/IAAs in Arabidopsis

    PubMed Central

    Piya, Sarbottam; Shrestha, Sandesh K.; Binder, Brad; Stewart, C. Neal; Hewezi, Tarek

    2014-01-01

    The phytohormone auxin regulates nearly all aspects of plant growth and development. Based on the current model in Arabidopsis thaliana, Auxin/indole-3-acetic acid (Aux/IAA) proteins repress auxin-inducible genes by inhibiting auxin response transcription factors (ARFs). Experimental evidence suggests that heterodimerization between Aux/IAA and ARF proteins are related to their unique biological functions. The objective of this study was to generate the Aux/IAA-ARF protein-protein interaction map using full length sequences and locate the interacting protein pairs to specific gene co-expression networks in order to define tissue-specific responses of the Aux/IAA-ARF interactome. Pairwise interactions between 19 ARFs and 29 Aux/IAAs resulted in the identification of 213 specific interactions of which 79 interactions were previously unknown. The incorporation of co-expression profiles with protein-protein interaction data revealed a strong correlation of gene co-expression for 70% of the ARF-Aux/IAA interacting pairs in at least one tissue/organ, indicative of the biological significance of these interactions. Importantly, ARF4-8 and 19, which were found to interact with almost all Aux-Aux/IAA showed broad co-expression relationships with Aux/IAA genes, thus, formed the central hubs of the co-expression network. Our analyses provide new insights into the biological significance of ARF-Aux/IAA associations in the morphogenesis and development of various plant tissues and organs. PMID:25566309

  5. Isotopically coded cleavable cross-linker for studying protein-protein interaction and protein complexes.

    PubMed

    Petrotchenko, Evgeniy V; Olkhovik, Vyacheslav K; Borchers, Christoph H

    2005-08-01

    An emerging approach for studying protein-protein interaction in complexes is the combination of chemical cross-linking and mass spectrometric analysis of the cross-linked peptides (cross-links) obtained after proteolysis of the complex. This approach, however, has several challenges and limitations, including the difficulty of detecting the cross-links, the potential interference from non-informative "cross-linked peptides" (dead end and intrapeptide cross-links), and unambiguous identification of the cross-links by mass spectrometry. Thus, we have synthesized an isotopically coded ethylene glycol bis(succinimidylsuccinate) derivate (D12-EGS), which contains 12 deuterium atoms for easy detection of cross-links when applied in a 1:1 mixture with its H12 counterpart and is also cleavable for releasing the cross-linked peptides allowing unambiguous identification by MS sequencing. Moreover, hydrolytic cleavage permits rapid distinguishing between different types of cross-links. Cleavage of a dead end cross-link produces a doublet with peaks 4.03 Da apart, with the lower peak appearing at a molecular mass 162 Da lower than the mass of the H12 form of the original cross-linked peptide. Cleavage of an intrapeptide cross-link leads to a doublet 8.05 Da apart and 62 Da lower than the molecular mass of the H12 form of the original cross-linked peptide. Cleavage of an interpeptide cross-link forms a pair of 4.03-Da doublets, with the lower mass member of each pair each shifted up from its unmodified molecular weight by 82 Da because of the attached portion of the cross-linker. All of this information has been incorporated into a software algorithm allowing automatic screening and detection of cross-links and cross-link types in matrix-assisted laser desorption/ionization mass spectra. In summary, the ease of detection of these species through the use of an isotopically coded cleavable cross-linker and our software algorithm, followed by mass spectrometric sequencing of the

  6. A Consistent Experimental and Modeling Approach to Light-Scattering Studies of Protein-Protein Interactions in Solution

    PubMed Central

    Asthagiri, D.; Paliwal, A.; Abras, D.; Lenhoff, A. M.; Paulaitis, M. E.

    2005-01-01

    The osmotic second virial coefficient, B2, obtained by light scattering from protein solutions has two principal components: the Donnan contribution and a contribution due to protein-protein interactions in the limit of infinite dilution. The Donnan contribution accounts for electroneutrality in a multicomponent solution of (poly)electrolytes. The importance of distinguishing this ideal contribution to B2 is emphasized, thereby allowing us to model the interaction part of B2 by molecular computations. The model for protein-protein interactions that we use here extends earlier work (Neal et al., 1998) by accounting for long-range electrostatic interactions and the specific hydration of the protein by strongly associated water molecules. Our model predictions are compared with measurements of B2 for lysozyme at 25°C over pH from 5.0 to 9.0, and 7–60 mM ionic strength. We find that B2 is positive at all solution conditions and decreases with increasing ionic strength, as expected, whereas the interaction part of B2 is negative at all conditions and becomes progressively less negative with increasing ionic strength. Although long-range electrostatic interactions dominate this contribution, particularly at low ionic strength, short-range electrostatic/dispersion interactions with specific hydration are essential for an accurate description of B2 derived from experiment. PMID:15792969

  7. A plasmid-based expression system to study protein-protein interactions at the Golgi in vivo.

    PubMed

    Bera, Sujoy; Raghuram, Vijeta; Mikhaylova, Marina; Kreutz, Michael R

    2016-06-01

    There is still an unmet need for simple methods to verify, visualize, and confirm protein-protein interactions in vivo. Here we describe a plasmid-based system to study such interactions. The system is based on the transmembrane domain (TMD) of the EF-hand Ca(2+) sensor protein calneuron-2. We show that fusion of 28 amino acids that include the TMD of calneuron-2 to proteins of interest results in prominent localization on the cytoplasmic side of the Golgi. The recruitment of binding partners to the protein of interest fused to this sequence can then be easily visualized by fluorescent tags. PMID:26973219

  8. Tuning protein-protein interactions using cosolvents: specific effects of ionic and non-ionic additives on protein phase behavior.

    PubMed

    Hansen, Jan; Platten, Florian; Wagner, Dana; Egelhaaf, Stefan U

    2016-04-21

    Cosolvents are routinely used to modulate the (thermal) stability of proteins and, hence, their interactions with proteins have been studied intensely. However, less is known about their specific effects on protein-protein interactions, which we characterize in terms of the protein phase behavior. We analyze the phase behavior of lysozyme solutions in the presence of sodium chloride (NaCl), guanidine hydrochloride (GuHCl), glycerol, and dimethyl sulfoxide (DMSO). We experimentally determined the crystallization boundary (XB) and, in combination with data on the cloud-point temperatures (CPTs), the crystallization gap. In agreement with other studies, our data indicate that the additives might affect the protein phase behavior through electrostatic screening and additive-specific contributions. At high salt concentrations, where electrostatic interactions are screened, both the CPT and the XB are found to be linear functions of the additive concentration. Their slopes quantify the additive-specific changes of the phase behavior and thus of the protein-protein interactions. While the specific effect of NaCl is to induce attractions between proteins, DMSO, glycerol and GuHCl (with increasing strength) weaken attractions and/or induce repulsions. Except for DMSO, changes of the CPT are stronger than those of the XB. Furthermore, the crystallization gap widens in the case of GuHCl and glycerol and narrows in the case of NaCl. We relate these changes to colloidal interaction models, namely square-well and patchy interactions. PMID:27020538

  9. Identification of protein-protein interaction and topologies in living cells by chemical cross-linking and mass spectrometry

    SciTech Connect

    Zhang, Haizhen; Tang, Xiaoting; Munske, Gerhard R.; Tolic, Nikola; Anderson, Gordon A.; Bruce, James E.

    2008-10-20

    Current chemical cross-linking methods are commonly employed for mapping sites of interaction and three-dimensional structure in purified, known protein complexes. When applied in vivo in combination of co-immunoprecipitation methods, information on the sites of interaction between proteins are unattainable due to overwhelming sample complexity. We present results from a novel cross-linking strategy that allow simultaneous protein-protein interaction and surface topology measurement in vivo without any prior knowledge of the system. The strategy consists of: (i) cross-linking reaction: intact cell labeling with protein interaction reporters (PIRs); (ii) two-stage mass spectrometric analysis: stage 1 identification of PIR-labeled proteins and construction of a restricted database by 2D-LC/MS/MS; and stage 2 analysis of PIR-labeled peptides by multiplexed LC/FTICR-MS; (iii) data analysis: identification of cross-linked peptides and proteins of origin using accurate mass and other constraints. This strategy was applied to Shewanella oneidensis MR-1 bacterial cells and successfully identified a protein-protein interaction between SecA and a small outer membrane lipoprotein as well as their sites of interaction in vivo.

  10. Over-producing soluble protein complex and validating protein-protein interaction through a new bacterial co-expression system.

    PubMed

    Zeng, Jumei; Zhang, Lei; Li, Yuqing; Wang, Yi; Wang, Mingchao; Duan, Xin; He, Zheng-Guo

    2010-01-01

    Many proteins exert their functions through a protein complex and protein-protein interactions. However, the study of these types of interactions is complicated when dealing with toxic or hydrophobic proteins. It is difficult to use the popular Escherichia coli host for their expression, as these proteins in all likelihood require a critical partner protein to ensure their proper folding and stability. In the present study, we have developed a novel co-expression vector, pHEX, which is compatible with, and thus can be partnered with, many commercially available E. coli vectors, such as pET, pGEX and pMAL. The pHEX contains the p15A origin of replication and a T7 promoter, which can over-produce a His-tagged recombinant protein. The new co-expression system was demonstrated to efficiently co-produce and co-purify heterodimeric protein complexes, for example PE25/PPE41 (Rv2430c/Rv2431c) and ESAT6/CFP10 (Rv3874/Rv3875), from the human pathogen Mycobacterium tuberculosis H37Rv. Furthermore, the system was also effectively used to characterize protein-protein interactions through convenient affinity tags. Using an in vivo pull-down assay, for the first time we have confirmed the presence of three pairs of PE/PPE-related novel protein interactions in this pathogen. In summary, a convenient and efficient co-expression vector system has been successfully developed. The new system should be applicable to any protein complex or any protein-protein interaction of interest in a wide range of biological organisms. PMID:19747546

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

    PubMed Central

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

    2015-01-01

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

  12. Toward a rigorous network of protein-protein interactions of the model sulfate reducer Desulfovibrio vulgaris Hildenborough

    SciTech Connect

    Chhabra, S.R.; Joachimiak, M.P.; Petzold, C.J.; Zane, G.M.; Price, M.N.; Gaucher, S.; Reveco, S.A.; Fok, V.; Johanson, A.R.; Batth, T.S.; Singer, M.; Chandonia, J.M.; Joyner, D.; Hazen, T.C.; Arkin, A.P.; Wall, J.D.; Singh, A.K.; Keasling, J.D.

    2011-05-01

    Protein–protein interactions offer an insight into cellular processes beyond what may be obtained by the quantitative functional genomics tools of proteomics and transcriptomics. The aforementioned tools have been extensively applied to study E. coli and other aerobes and more recently to study the stress response behavior of Desulfovibrio 5 vulgaris Hildenborough, a model anaerobe and sulfate reducer. In this paper we present the first attempt to identify protein-protein interactions in an obligate anaerobic bacterium. We used suicide vector-assisted chromosomal modification of 12 open reading frames encoded by this sulfate reducer to append an eight amino acid affinity tag to the carboxy-terminus of the chosen proteins. Three biological replicates of the 10 ‘pulled-down’ proteins were separated and analyzed using liquid chromatography-mass spectrometry. Replicate agreement ranged between 35% and 69%. An interaction network among 12 bait and 90 prey proteins was reconstructed based on 134 bait-prey interactions computationally identified to be of high confidence. We discuss the biological significance of several unique metabolic features of D. vulgaris revealed by this protein-protein interaction data 15 and protein modifications that were observed. These include the distinct role of the putative carbon monoxide-induced hydrogenase, unique electron transfer routes associated with different oxidoreductases, and the possible role of methylation in regulating sulfate reduction.

  13. Investigating the importance of Delaunay-based definition of atomic interactions in scoring of protein-protein docking results.

    PubMed

    Jafari, Rahim; Sadeghi, Mehdi; Mirzaie, Mehdi

    2016-05-01

    The approaches taken to represent and describe structural features of the macromolecules are of major importance when developing computational methods for studying and predicting their structures and interactions. This study attempts to explore the significance of Delaunay tessellation for the definition of atomic interactions by evaluating its impact on the performance of scoring protein-protein docking prediction. Two sets of knowledge-based scoring potentials are extracted from a training dataset of native protein-protein complexes. The potential of the first set is derived using atomic interactions extracted from Delaunay tessellated structures. The potential of the second set is calculated conventionally, that is, using atom pairs whose interactions were determined by their separation distances. The scoring potentials were tested against two different docking decoy sets and their performances were compared. The results show that, if properly optimized, the Delaunay-based scoring potentials can achieve higher success rate than the usual scoring potentials. These results and the results of a previous study on the use of Delaunay-based potentials in protein fold recognition, all point to the fact that Delaunay tessellation of protein structure can provide a more realistic definition of atomic interaction, and therefore, if appropriately utilized, may be able to improve the accuracy of pair potentials. PMID:27060891

  14. Development of FRET assay into quantitative and high-throughput screening technology platforms for protein-protein interactions.

    PubMed

    Song, Yang; Madahar, Vipul; Liao, Jiayu

    2011-04-01

    Förster resonance energy transfer (FRET) technology has been widely used in biological and biomedical research and is a very powerful tool in elucidating protein interactions in many cellular processes. Ubiquitination and SUMOylation are multi-step cascade reactions, involving multiple enzymes and protein-protein interactions. Here we report the development of dissociation constant (K (d)) determination for protein-protein interaction and cell-based high-throughput screening (HTS) assay in SUMOylation cascade using FRET technology. These developments are based on steady state and high efficiency of fluorescent energy transfer between CyPet and YPet fused with SUMO1 and Ubc9, respectively. The developments in theoretical and experimental procedures for protein interaction K (d) determination and cell-based HTS provide novel tools in affinity measurement and protein interaction inhibitor screening. The K (d) determined by FRET between SUMO1 and Ubc9 is compatible with those determined with other traditional approaches, such as isothermal titration calorimetry (ITC) and surface plasmon resonance (SPR). The FRET-based HTS is pioneer in cell-based HTS. Both K (d) determination and cell-based HTS, carried out in 384-well plate format, provide powerful tools for large-scale and high-throughput applications. PMID:21174150

  15. Enhancements to the Rosetta Energy Function Enable Improved Identification of Small Molecules that Inhibit Protein-Protein Interactions

    PubMed Central

    Karanicolas, John

    2015-01-01

    Protein-protein interactions are among today’s most exciting and promising targets for therapeutic intervention. To date, identifying small-molecules that selectively disrupt these interactions has proven particularly challenging for virtual screening tools, since these have typically been optimized to perform well on more “traditional” drug discovery targets. Here, we test the performance of the Rosetta energy function for identifying compounds that inhibit protein interactions, when these active compounds have been hidden amongst pools of “decoys.” Through this virtual screening benchmark, we gauge the effect of two recent enhancements to the functional form of the Rosetta energy function: the new “Talaris” update and the “pwSHO” solvation model. Finally, we conclude by developing and validating a new weight set that maximizes Rosetta’s ability to pick out the active compounds in this test set. Looking collectively over the course of these enhancements, we find a marked improvement in Rosetta’s ability to identify small-molecule inhibitors of protein-protein interactions. PMID:26484863

  16. Inhibition of the p53/hDM2 protein-protein interaction by cyclometallated iridium(III) compounds

    PubMed Central

    Liu, Li-Juan; He, Bingyong; Miles, Jennifer A.; Wang, Wanhe; Mao, Zhifeng; Che, Weng Ian; Lu, Jin-Jian; Chen, Xiu-Ping; Wilson, Andrew J.; Ma, Dik-Lung; Leung, Chung-Hang

    2016-01-01

    Inactivation of the p53 transcription factor by mutation or other mechanisms is a frequent event in tumorigenesis. One of the major endogenous negative regulators of p53 in humans is hDM2, a ubiquitin E3 ligase that binds to p53 causing proteasomal p53 degradation. In this work, a library of organometallic iridium(III) compounds were synthesized and evaluated for their ability to disrupt the p53/hDM2 protein-protein interaction. The novel cyclometallated iridium(III) compound 1 [Ir(eppy)2(dcphen)](PF6) (where eppy = 2-(4-ethylphenyl)pyridine and dcphen = 4, 7-dichloro-1, 10-phenanthroline) blocked the interaction of p53/hDM2 in human amelanotic melanoma cells. Finally, 1 exhibited anti-proliferative activity and induced apoptosis in cancer cell lines consistent with inhibition of the p53/hDM2 interaction. Compound 1 represents the first reported organometallic p53/hDM2 protein-protein interaction inhibitor. PMID:26883110

  17. Multiple protein-protein interactions converging on the Prp38 protein during activation of the human spliceosome.

    PubMed

    Schütze, Tonio; Ulrich, Alexander K C; Apelt, Luise; Will, Cindy L; Bartlick, Natascha; Seeger, Martin; Weber, Gert; Lührmann, Reinhard; Stelzl, Ulrich; Wahl, Markus C

    2016-02-01

    Spliceosomal Prp38 proteins contain a conserved amino-terminal domain, but only higher eukaryotic orthologs also harbor a carboxy-terminal RS domain, a hallmark of splicing regulatory SR proteins. We show by crystal structure analysis that the amino-terminal domain of human Prp38 is organized around three pairs of antiparallel α-helices and lacks similarities to RNA-binding domains found in canonical SR proteins. Instead, yeast two-hybrid analyses suggest that the amino-terminal domain is a versatile protein-protein interaction hub that possibly binds 12 other spliceosomal proteins, most of which are recruited at the same stage as Prp38. By quantitative, alanine surface-scanning two-hybrid screens and biochemical analyses we delineated four distinct interfaces on the Prp38 amino-terminal domain. In vitro interaction assays using recombinant proteins showed that Prp38 can bind at least two proteins simultaneously via two different interfaces. Addition of excess Prp38 amino-terminal domain to in vitro splicing assays, but not of an interaction-deficient mutant, stalled splicing at a precatalytic stage. Our results show that human Prp38 is an unusual SR protein, whose amino-terminal domain is a multi-interface protein-protein interaction platform that might organize the relative positioning of other proteins during splicing. PMID:26673105

  18. An optimized mRFP-based bimolecular fluorescence complementation system for the detection of protein-protein interactions in planta.

    PubMed

    Zilian, Eva; Maiss, Edgar

    2011-06-01

    An existing bimolecular fluorescence complementation (BiFC) system, based on a monomeric red fluorescent protein (mRFP), has been optimized for the investigation of protein-protein interactions in planta. The expression plasmids, encoding the N-terminal amino acids (aa) 1-168 and the C-terminal aa 169-225 of the mRFP, allow N- or C-terminal fusion of a split mRFP, with the genes of interest. Two major improvements over the original vectors have been made. Firstly, the coding sequence of a GGGSGGG-linker has been integrated between mRFP sequences and the genes of interest. Secondly, a modified mini binary vector (∼3.5 kb) was introduced as the backbone for the plant expression plasmids. Based on the results of yeast two-hybrid studies with plant viral proteins, interaction of viral proteins was tested in Nicotiana benthamiana plants and monitored by confocal laser scanning microscopy (CLSM). Plum pox virus coat protein and mutants thereof served as controls. The system was validated using the N-protein of Capsicum chlorosis virus for which a self-interaction was shown for the first time, the Tobacco mosaic virus coat protein and BC1 and BV1 of the Tomato yellow leaf curl Thailand virus. This optimized BiFC system provides a convenient alternative to other BiFC, as well as yeast two-hybrid assays, for detecting protein-protein interactions. PMID:21473882

  19. Validation of a novel shotgun proteomic workflow for the discovery of protein-protein interactions: focus on ZNF521.

    PubMed

    Bernaudo, Francesca; Monteleone, Francesca; Mesuraca, Maria; Krishnan, Shibu; Chiarella, Emanuela; Scicchitano, Stefania; Cuda, Giovanni; Morrone, Giovanni; Bond, Heather M; Gaspari, Marco

    2015-04-01

    The study of protein-protein interactions is increasingly relying on mass spectrometry (MS). The classical approach of separating immunoprecipitated proteins by SDS-PAGE followed by in-gel digestion is long and labor-intensive. Besides, it is difficult to integrate it with most quantitative MS-based workflows, except for stable isotopic labeling of amino acids in cell culture (SILAC). This work describes a fast, flexible and quantitative workflow for the discovery of novel protein-protein interactions. A cleavable cross-linker, dithiobis[succinimidyl propionate] (DSP), is utilized to stabilize protein complexes before immunoprecipitation. Protein complex detachment from the antibody is achieved by limited proteolysis. Finally, protein quantitation is performed via (18)O labeling. The workflow has been optimized concerning (i) DSP concentration and (ii) incubation times for limited proteolysis, using the stem cell-associated transcription cofactor ZNF521 as a model target. The interaction of ZNF521 with the core components of the nuclear remodelling and histone deacetylase (NuRD) complex, already reported in the literature, was confirmed. Additionally, interactions with newly discovered molecular partners of potentially relevant functional role, such as ZNF423, Spt16, Spt5, were discovered and validated by Western blotting. PMID:25774781

  20. Conformational selection in a protein-protein interaction revealed by dynamic pathway analysis

    SciTech Connect

    Chakrabarti, Kalyan S.; Agafonov, Roman V.; Pontiggia, Francesco; Otten, Renee; Higgins, Matthew K.; Schertler, Gebhard F. X.; Oprian, Daniel D.; Kern, Dorothee

    2015-12-24

    Molecular recognition plays a central role in biology, and protein dynamics has been acknowledged to be important in this process. However, it is highly debated whether conformational changes happen before ligand binding to produce a binding-competent state (conformational selection) or are caused in response to ligand binding (induced fit). Proposals for both mechanisms in protein/protein recognition have been primarily based on structural arguments. However, the distinction between them is a question of the probabilities of going via these two opposing pathways. Here we present a direct demonstration of exclusive conformational selection in protein/protein recognition by measuring the flux for rhodopsin kinase binding to its regulator recoverin, an important molecular recognition in the vision system. Using NMR spectroscopy, stopped-flow kinetics and isothermal titration calorimetry we show that recoverin populates a minor conformation in solution that exposes a hydrophobic binding pocket responsible for binding rhodopsin kinase. Lastly, protein dynamics in free recoverin limits the overall rate of binding.

  1. Conformational selection in a protein-protein interaction revealed by dynamic pathway analysis

    DOE PAGESBeta

    Chakrabarti, Kalyan S.; Agafonov, Roman V.; Pontiggia, Francesco; Otten, Renee; Higgins, Matthew K.; Schertler, Gebhard F. X.; Oprian, Daniel D.; Kern, Dorothee

    2015-12-24

    Molecular recognition plays a central role in biology, and protein dynamics has been acknowledged to be important in this process. However, it is highly debated whether conformational changes happen before ligand binding to produce a binding-competent state (conformational selection) or are caused in response to ligand binding (induced fit). Proposals for both mechanisms in protein/protein recognition have been primarily based on structural arguments. However, the distinction between them is a question of the probabilities of going via these two opposing pathways. Here we present a direct demonstration of exclusive conformational selection in protein/protein recognition by measuring the flux for rhodopsinmore » kinase binding to its regulator recoverin, an important molecular recognition in the vision system. Using NMR spectroscopy, stopped-flow kinetics and isothermal titration calorimetry we show that recoverin populates a minor conformation in solution that exposes a hydrophobic binding pocket responsible for binding rhodopsin kinase. Lastly, protein dynamics in free recoverin limits the overall rate of binding.« less

  2. Conformational Selection in a Protein-Protein Interaction revealed by Dynamic Pathway Analysis

    PubMed Central

    Chakrabarti, Kalyan S.; Agafonov, Roman V.; Pontiggia, Francesco; Otten, Renee; Higgins, Matthew K.; Schertler, Gebhard F. X.; Oprian, Daniel D.; Kern, Dorothee

    2015-01-01

    SUMMARY Molecular recognition plays a central role in biology, and protein dynamics has been acknowledged to be important in this process. However, it is highly debated whether conformational changes happen before ligand binding to produce a binding-competent state (conformational selection) or are caused in response to ligand binding (induced fit). Proposals for both mechanisms in protein/protein recognition have been primarily based on structural arguments. However, the distinction between them is a question of the probabilities of going via these two opposing pathways. Here we present a direct demonstration of exclusive conformational selection in protein/protein recognition by measuring the flux for rhodopsin kinase binding to its regulator recoverin, an important molecular recognition in the vision system. Using NMR spectroscopy, stopped-flow kinetics and isothermal titration calorimetry we show that recoverin populates a minor conformation in solution that exposes a hydrophobic binding pocket responsible for binding rhodopsin kinase. Protein dynamics in free recoverin limits the overall rate of binding. PMID:26725117

  3. Fragment-Based Protein-Protein Interaction Antagonists of a Viral Dimeric Protease.

    PubMed

    Gable, Jonathan E; Lee, Gregory M; Acker, Timothy M; Hulce, Kaitlin R; Gonzalez, Eric R; Schweigler, Patrick; Melkko, Samu; Farady, Christopher J; Craik, Charles S

    2016-04-19

    Fragment-based drug discovery has shown promise as an approach for challenging targets such as protein-protein interfaces. We developed and applied an activity-based fragment screen against dimeric Kaposi's sarcoma-associated herpesvirus protease (KSHV Pr) using an optimized fluorogenic substrate. Dose-response determination was performed as a confirmation screen, and NMR spectroscopy was used to map fragment inhibitor binding to KSHV Pr. Kinetic assays demonstrated that several initial hits also inhibit human cytomegalovirus protease (HCMV Pr). Binding of these hits to HCMV Pr was also confirmed by NMR spectroscopy. Despite the use of a target-agnostic fragment library, more than 80 % of confirmed hits disrupted dimerization and bound to a previously reported pocket at the dimer interface of KSHV Pr, not to the active site. One class of fragments, an aminothiazole scaffold, was further explored using commercially available analogues. These compounds demonstrated greater than 100-fold improvement of inhibition. This study illustrates the power of fragment-based screening for these challenging enzymatic targets and provides an example of the potential druggability of pockets at protein-protein interfaces. PMID:26822284

  4. Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences

    PubMed Central

    2009-01-01

    Background Protein-protein interactions underlie many important biological processes. Computational prediction methods can nicely complement experimental approaches for identifying protein-protein interactions. Recently, a unique category of sequence-based prediction methods has been put forward - unique in the sense that it does not require homologous protein sequences. This enables it to be universally applicable to all protein sequences unlike many of previous sequence-based prediction methods. If effective as claimed, these new sequence-based, universally applicable prediction methods would have far-reaching utilities in many areas of biology research. Results Upon close survey, I realized that many of these new methods were ill-tested. In addition, newer methods were often published without performance comparison with previous ones. Thus, it is not clear how good they are and whether there are significant performance differences among them. In this study, I have implemented and thoroughly tested 4 different methods on large-scale, non-redundant data sets. It reveals several important points. First, significant performance differences are noted among different methods. Second, data sets typically used for training prediction methods appear significantly biased, limiting the general applicability of prediction methods trained with them. Third, there is still ample room for further developments. In addition, my analysis illustrates the importance of complementary performance measures coupled with right-sized data sets for meaningful benchmark tests. Conclusions The current study reveals the potentials and limits of the new category of sequence-based protein-protein interaction prediction methods, which in turn provides a firm ground for future endeavours in this important area of contemporary bioinformatics. PMID:20003442

  5. PRISM: a web server and repository for prediction of protein-protein interactions and modeling their 3D complexes.

    PubMed

    Baspinar, Alper; Cukuroglu, Engin; Nussinov, Ruth; Keskin, Ozlem; Gursoy, Attila

    2014-07-01

    The PRISM web server enables fast and accurate prediction of protein-protein interactions (PPIs). The prediction algorithm is knowledge-based. It combines structural similarity and accounts for evolutionary conservation in the template interfaces. The predicted models are stored in its repository. Given two protein structures, PRISM will provide a structural model of their complex if a matching template interface is available. Users can download the complex structure, retrieve the interface residues and visualize the complex model. The PRISM web server is user friendly, free and open to all users at http://cosbi.ku.edu.tr/prism. PMID:24829450

  6. Discovery of a Potent Inhibitor of Replication Protein A Protein-Protein Interactions Using a Fragment Linking Approach

    PubMed Central

    Frank, Andreas O.; Feldkamp, Michael D.; Kennedy, J. Phillip; Waterson, Alex G.; Pelz, Nicholas F.; Patrone, James D.; Vangamudi, Bhavatarini; Camper, DeMarco V.; Rossanese, Olivia W.; Chazin, Walter J.; Fesik, Stephen W.

    2013-01-01

    Replication protein A (RPA), the major eukaryotic single-stranded DNA (ssDNA) binding protein, is involved in nearly all cellular DNA transactions. The RPA N-terminal domain (RPA70N) is a recruitment site for proteins involved in DNA damage response and repair. Selective inhibition of these protein-protein interactions has the potential to inhibit the DNA damage response and sensitize cancer cells to DNA-damaging agents without affecting other functions of RPA. To discover a potent, selective inhibitor of the RPA70N protein-protein interactions to test this hypothesis, we used NMR spectroscopy to identify fragment hits that bind to two adjacent sites in the basic cleft of RPA70N. High-resolution X-ray crystal structures of RPA70N-ligand complexes revealed how these fragments bind to RPA and guided the design of linked compounds that simultaneously occupy both sites. We have synthesized linked molecules that bind to RPA70N with submicromolar affinity and minimal disruption of RPA’s interaction with ssDNA. PMID:24147804

  7. Photolytic Cross-Linking to Probe Protein-Protein and Protein-Matrix Interactions in Lyophilized Powders.

    PubMed

    Iyer, Lavanya K; Moorthy, Balakrishnan S; Topp, Elizabeth M

    2015-09-01

    Protein structure and local environment in lyophilized formulations were probed using high-resolution solid-state photolytic cross-linking with mass spectrometric analysis (ssPC-MS). In order to characterize structure and microenvironment, protein-protein, protein-excipient, and protein-water interactions in lyophilized powders were identified. Myoglobin (Mb) was derivatized in solution with the heterobifunctional probe succinimidyl 4,4'-azipentanoate (SDA) and the structural integrity of the labeled protein (Mb-SDA) confirmed using CD spectroscopy and liquid chromatography/mass spectrometry (LC-MS). Mb-SDA was then formulated with and without excipients (raffinose, guanidine hydrochloride (Gdn HCl)) and lyophilized. The freeze-dried powder was irradiated with ultraviolet light at 365 nm for 30 min to produce cross-linked adducts that were analyzed at the intact protein level and after trypsin digestion. SDA-labeling produced Mb carrying up to five labels, as detected by LC-MS. Following lyophilization and irradiation, cross-linked peptide-peptide, peptide-water, and peptide-raffinose adducts were detected. The exposure of Mb side chains to the matrix was quantified based on the number of different peptide-peptide, peptide-water, and peptide-excipient adducts detected. In the absence of excipients, peptide-peptide adducts involving the CD, DE, and EF loops and helix H were common. In the raffinose formulation, peptide-peptide adducts were more distributed throughout the molecule. The Gdn HCl formulation showed more protein-protein and protein-water adducts than the other formulations, consistent with protein unfolding and increased matrix interactions. The results demonstrate that ssPC-MS can be used to distinguish excipient effects and characterize the local protein environment in lyophilized formulations with high resolution. PMID:26204425

  8. Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles

    PubMed Central

    Brender, Jeffrey R.; Zhang, Yang

    2015-01-01

    The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies. PMID:26506533

  9. Protein-protein interactions within the Fatty Acid Synthase-II system of Mycobacterium tuberculosis are essential for mycobacterial viability.

    PubMed

    Veyron-Churlet, Romain; Guerrini, Olivier; Mourey, Lionel; Daffé, Mamadou; Zerbib, Didier

    2004-12-01

    Despite the existence of efficient chemotherapy, tuberculosis remains a leading cause of mortality worldwide. New drugs are urgently needed to reduce the potential impact of the emergence of multidrug-resistant strains of the causative agent Mycobacterium tuberculosis (Mtb). The front-line antibiotic isoniazid (INH), and several other drugs, target the biosynthesis of mycolic acids and especially the Fatty Acid Synthase-II (FAS-II) elongation system. This biosynthetic pathway is essential and specific for mycobacteria and still represents a valuable system for the search of new anti-tuberculous agents. Several data, in the literature, suggest the existence of protein-protein interactions within the FAS-II system. These interactions themselves might serve as targets for a new generation of drugs directed against Mtb. By using an extensive in vivo yeast two-hybrid approach and in vitro co-immunoprecipitation, we have demonstrated the existence of both homotypic and heterotypic interactions between the known components of FAS-II. The condensing enzymes KasA, KasB and mtFabH interact with each other and with the reductases MabA and InhA. Furthermore, we have designed and constructed point mutations of the FAS-II reductase MabA, able to disrupt its homotypic interactions and perturb the interaction pattern of this protein within FAS-II. Finally, we showed by a transdominant genetic approach that these mutants are dominant negative in both non-pathogenic and pathogenic mycobacteria. These data allowed us to draw a dynamic model of the organization of FAS-II. They also represent an important step towards the design of a new generation of anti-tuberculous agents, as being inhibitors of essential protein-protein interactions. PMID:15554959

  10. A linear classifier based on entity recognition tools and a statistical approach to method extraction in the protein-protein interaction literature

    PubMed Central

    2011-01-01

    Background We participated, as Team 81, in the Article Classification and the Interaction Method subtasks (ACT and IMT, respectively) of the Protein-Protein Interaction task of the BioCreative III Challenge. For the ACT, we pursued an extensive testing of available Named Entity Recognition and dictionary tools, and used the most promising ones to extend our Variable Trigonometric Threshold linear classifier. Our main goal was to exploit the power of available named entity recognition and dictionary tools to aid in the classification of documents relevant to Protein-Protein Interaction (PPI). For the IMT, we focused on obtaining evidence in support of the interaction methods used, rather than on tagging the document with the method identifiers. We experimented with a primarily statistical approach, as opposed to employing a deeper natural language processing strategy. In a nutshell, we exploited classifiers, simple pattern matching for potential PPI methods within sentences, and ranking of candidate matches using statistical considerations. Finally, we also studied the benefits of integrating the method extraction approach that we have used for the IMT into the ACT pipeline. Results For the ACT, our linear article classifier leads to a ranking and classification performance significantly higher than all the reported submissions to the challenge in terms of Area Under the Interpolated Precision and Recall Curve, Mathew’s Correlation Coefficient, and F-Score. We observe that the most useful Named Entity Recognition and Dictionary tools for classification of articles relevant to protein-protein interaction are: ABNER, NLPROT, OSCAR 3 and the PSI-MI ontology. For the IMT, our results are comparable to those of other systems, which took very different approaches. While the performance is not very high, we focus on providing evidence for potential interaction detection methods. A significant majority of the evidence sentences, as evaluated by independent annotators, are

  11. A bacterial two-hybrid system that utilizes Gateway cloning for rapid screening of protein-protein interactions.

    PubMed

    Karna, S L Rajasekhar; Zogaj, Xhavit; Barker, Jeffrey R; Seshu, Janakiram; Dove, Simon L; Klose, Karl E

    2010-11-01

    Comprehensive clone sets representing the entire genome now exist for a large number of organisms. The Gateway entry clone sets are a particularly useful means to study gene function, given the ease of introduction into any Gateway-suitable destination vector. We have adapted a bacterial two-hybrid system for use with Gateway entry clone sets, such that potential interactions between proteins encoded within these clone sets can be determined by new destination vectors. We show that utilizing the Gateway clone sets for Francisella tularensis and Vibrio cholerae, known interactions between F. tularensis IglA and IglB and V. cholerae VipA and VipB could be confirmed with these destination vectors. Moreover, the introduction of unique tags into each vector allowed for visualization of the expressed hybrid proteins via Western immunoblot. This Gateway-suitable bacterial two-hybrid system provides a new tool for rapid screening of protein-protein interactions. PMID:21091448

  12. Engineering of soybean mosaic virus as a versatile tool for studying protein-protein interactions in soybean.

    PubMed

    Seo, Jang-Kyun; Choi, Hong-Soo; Kim, Kook-Hyung

    2016-01-01

    Transient gene expression approaches are valuable tools for rapid introduction of genes of interest and characterization of their functions in plants. Although agroinfiltration is the most effectively and routinely used method for transient expression of multiple genes in various plant species, this approach has been largely unsuccessful in soybean. In this study, we engineered soybean mosaic virus (SMV) as a dual-gene delivery vector to simultaneously deliver and express two genes in soybean cells. We further show the application of the SMV-based dual vector for a bimolecular fluorescence complementation assay to visualize in vivo protein-protein interactions in soybean and for a co-immunoprecipitation assay to identify cellular proteins interacting with SMV helper component protease. This approach provides a rapid and cost-effective tool for transient introduction of multiple traits into soybean and for in vivo characterization of the soybean cellular protein interaction network. PMID:26926710

  13. Constrained peptides with target-adapted cross-links as inhibitors of a pathogenic protein-protein interaction.

    PubMed

    Glas, Adrian; Bier, David; Hahne, Gernot; Rademacher, Christoph; Ottmann, Christian; Grossmann, Tom N

    2014-02-24

    Bioactive conformations of peptides can be stabilized by macrocyclization, resulting in increased target affinity and activity. Such macrocyclic peptides proved useful as modulators of biological functions, in particular as inhibitors of protein-protein interactions (PPI). However, most peptide-derived PPI inhibitors involve stabilized α-helices, leaving a large number of secondary structures unaddressed. Herein, we present a rational approach towards stabilization of an irregular peptide structure, using hydrophobic cross-links that replace residues crucially involved in target binding. The molecular basis of this interaction was elucidated by X-ray crystallography and isothermal titration calorimetry. The resulting cross-linked peptides inhibit the interaction between human adaptor protein 14-3-3 and virulence factor exoenzyme S. Taking into consideration that irregular peptide structures participate widely in PPIs, this approach provides access to novel peptide-derived inhibitors. PMID:24504455

  14. Sample Preparation for Mass Spectrometry Analysis of Protein-Protein Interactions in Cancer Cell Lines and Tissues.

    PubMed

    Beigbeder, Alice; Vélot, Lauriane; James, D Andrew; Bisson, Nicolas

    2016-01-01

    A precisely controlled network of protein-protein interactions constitutes the basis for functional signaling pathways. This equilibrium is more often than not disrupted in cancer cells, by the aberrant expression or activation of oncogenic proteins. Therefore, the analysis of protein interaction networks in cancer cells has become crucial to expand our comprehension of the molecular underpinnings of tumor formation and progression. This protocol describes a sample preparation method for the analysis of signaling complexes by mass spectrometry (MS), following the affinity purification of a protein of interest from a cancer cell line or a solid tumor. In particular, we provide a spin tip-based protease digestion procedure that offers a more rapid and controlled alternative to other gel-based and gel-free methods. This sample preparation protocol represents a useful strategy to identify protein interactions and to gain insight into the molecular mechanisms that contribute to a given cancer phenotype. PMID:27581032

  15. Design, synthesis, and biological evaluation of novel FAK scaffold inhibitors targeting the FAK-VEGFR3 protein-protein interaction.

    PubMed

    Gogate, Priyanka N; Ethirajan, Manivannan; Kurenova, Elena V; Magis, Andrew T; Pandey, Ravindra K; Cance, William G

    2014-06-10

    Focal adhesion kinase (FAK) and vascular endothelial growth factor receptor 3 (VEGFR3) are tyrosine kinases, which function as key modulators of survival and metastasis signals in cancer cells. Previously, we reported that small molecule chlorpyramine hydrochloride (C4) specifically targets the interaction between FAK and VEGFR3 and exhibits anti-tumor efficacy. In this study, we designed and synthesized a series of 1 (C4) analogs on the basis of structure activity relationship and molecular modeling. The resulting new compounds were evaluated for their binding to the FAT domain of FAK and anti-cancer activity. Amongst all tested analogs, compound 29 augmented anti-proliferative activity in multiple cancer cell lines with stronger binding to the FAT domain of FAK and disrupted the FAK-VEGFR3 interaction. In conclusion, we hope that this work will contribute to further studies of more potent and selective FAK-VEGFR3 protein-protein interaction inhibitors. PMID:24780592

  16. Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning.

    PubMed

    Maheshwari, Surabhi; Brylinski, Michal

    2015-01-01

    The identification of protein-protein interactions is vital for understanding protein function, elucidating interaction mechanisms, and for practical applications in drug discovery. With the exponentially growing protein sequence data, fully automated computational methods that predict interactions between proteins are becoming essential components of system-level function inference. A thorough analysis of protein complex structures demonstrated that binding site locations as well as the interfacial geometry are highly conserved across evolutionarily related proteins. Because the conformational space of protein-protein interactions is highly covered by experimental structures, sensitive protein threading techniques can be used to identify suitable templates for the accurate prediction of interfacial residues. Toward this goal, we developed eFindSite(PPI) , an algorithm that uses the three-dimensional structure of a target protein, evolutionarily remotely related templates and machine learning techniques to predict binding residues. Using crystal structures, the average sensitivity (specificity) of eFindSite(PPI) in interfacial residue prediction is 0.46 (0.92). For weakly homologous protein models, these values only slightly decrease to 0.40-0.43 (0.91-0.92) demonstrating that eFindSite(PPI) performs well not only using experimental data but also tolerates structural imperfections in computer-generated structures. In addition, eFindSite(PPI) detects specific molecular interactions at the interface; for instance, it correctly predicts approximately one half of hydrogen bonds and aromatic interactions, as well as one third of salt bridges and hydrophobic contacts. Comparative benchmarks against several dimer datasets show that eFindSite(PPI) outperforms other methods for protein-binding residue prediction. It also features a carefully tuned confidence estimation system, which is particularly useful in large-scale applications using raw genomic data. eFindSite(PPI) is

  17. AraPPISite: a database of fine-grained protein-protein interaction site annotations for Arabidopsis thaliana.

    PubMed

    Li, Hong; Yang, Shiping; Wang, Chuan; Zhou, Yuan; Zhang, Ziding

    2016-09-01

    Knowledge about protein interaction sites provides detailed information of protein-protein interactions (PPIs). To date, nearly 20,000 of PPIs from Arabidopsis thaliana have been identified. Nevertheless, the interaction site information has been largely missed by previously published PPI databases. Here, AraPPISite, a database that presents fine-grained interaction details for A. thaliana PPIs is established. First, the experimentally determined 3D structures of 27 A. thaliana PPIs are collected from the Protein Data Bank database and the predicted 3D structures of 3023 A. thaliana PPIs are modeled by using two well-established template-based docking methods. For each experimental/predicted complex structure, AraPPISite not only provides an interactive user interface for browsing interaction sites, but also lists detailed evolutionary and physicochemical properties of these sites. Second, AraPPISite assigns domain-domain interactions or domain-motif interactions to 4286 PPIs whose 3D structures cannot be modeled. In this case, users can easily query protein interaction regions at the sequence level. AraPPISite is a free and user-friendly database, which does not require user registration or any configuration on local machines. We anticipate AraPPISite can serve as a helpful database resource for the users with less experience in structural biology or protein bioinformatics to probe the details of PPIs, and thus accelerate the studies of plant genetics and functional genomics. AraPPISite is available at http://systbio.cau.edu.cn/arappisite/index.html . PMID:27338257

  18. Consequences of inducing intrinsic disorder in a high-affinity protein-protein interaction.

    PubMed

    Papadakos, Grigorios; Sharma, Amit; Lancaster, Lorna E; Bowen, Rebecca; Kaminska, Renata; Leech, Andrew P; Walker, Daniel; Redfield, Christina; Kleanthous, Colin

    2015-04-29

    The kinetic and thermodynamic consequences of intrinsic disorder in protein-protein recognition are controversial. We address this by inducing one partner of the high-affinity colicin E3 rRNase domain-Im3 complex (K(d) ≈ 10(-12) M) to become an intrinsically disordered protein (IDP). Through a variety of biophysical measurements, we show that a single alanine mutation at Tyr507 within the hydrophobic core of the isolated colicin E3 rRNase domain causes the enzyme to become an IDP (E3 rRNase(IDP)). E3 rRNase(IDP) binds stoichiometrically to Im3 and forms a structure that is essentially identical to the wild-type complex. However, binding of E3 rRNase(IDP) to Im3 is 4 orders of magnitude weaker than that of the folded rRNase, with thermodynamic parameters reflecting the disorder-to-order transition on forming the complex. Critically, pre-steady-state kinetic analysis of the E3 rRNase(IDP)-Im3 complex demonstrates that the decrease in affinity is mostly accounted for by a drop in the electrostatically steered association rate. Our study shows that, notwithstanding the advantages intrinsic disorder brings to biological systems, this can come at severe kinetic and thermodynamic cost. PMID:25856265

  19. Analysis of the aplyronine A-induced protein-protein interaction between actin and tubulin by surface plasmon resonance.

    PubMed

    Hirayama, Yuichiro; Yamagishi, Kota; Suzuki, Tomohiro; Kawagishi, Hirokazu; Kita, Masaki; Kigoshi, Hideo

    2016-06-15

    The antitumor macrolide aplyronine A induces protein-protein interaction (PPI) between actin and tubulin to exert highly potent biological activities. The interactions and binding kinetics of these molecules were analyzed by the surface plasmon resonance with biotinylated aplyronines or tubulin as ligands. Strong binding was observed for tubulin and actin with immobilized aplyronine A. These PPIs were almost completely inhibited by one equivalent of either aplyronine A or C, or mycalolide B. In contrast, a non-competitive actin-depolymerizing agent, latrunculin A, highly accelerated their association. Significant binding was also observed for immobilized tubulin with an actin-aplyronine A complex, and the dissociation constant KD was 1.84μM. Our method could be used for the quantitative analysis of the PPIs between two polymerizing proteins stabilized with small agents. PMID:27161875

  20. Cyclic and Macrocyclic Peptides as Chemical Tools To Recognise Protein Surfaces and Probe Protein-Protein Interactions.

    PubMed

    Cardote, Teresa A F; Ciulli, Alessio

    2016-04-19

    Targeting protein surfaces and protein-protein interactions (PPIs) with small molecules is a frontier goal of chemical biology and provides attractive therapeutic opportunities in drug discovery. The molecular properties of protein surfaces, including their shallow features and lack of deep binding pockets, pose significant challenges, and as a result have proved difficult to target. Peptides are ideal candidates for this mission due to their ability to closely mimic many structural features of protein interfaces. However, their inherently low intracellular stability and permeability and high in vivo clearance have thus far limited their biological applications. One way to improve these properties is to constrain the secondary structure of linear peptides by cyclisation. Herein we review various classes of cyclic and macrocyclic peptides as chemical probes of protein surfaces and modulators of PPIs. The growing interest in this area and recent advances provide evidence of the potential of developing peptide-like molecules that specifically target these interactions. PMID:26563831

  1. PACSAB: Coarse-Grained Force Field for the Study of Protein-Protein Interactions and Conformational Sampling in Multiprotein Systems.

    PubMed

    Emperador, Agustí; Sfriso, Pedro; Villarreal, Marcos Ariel; Gelpí, Josep Lluis; Orozco, Modesto

    2015-12-01

    Molecular dynamics simulations of proteins are usually performed on a single molecule, and coarse-grained protein models are calibrated using single-molecule simulations, therefore ignoring intermolecular interactions. We present here a new coarse-grained force field for the study of many protein systems. The force field, which is implemented in the context of the discrete molecular dynamics algorithm, is able to reproduce the properties of folded and unfolded proteins, in both isolation, complexed forming well-defined quaternary structures, or aggregated, thanks to its proper evaluation of protein-protein interactions. The accuracy and computational efficiency of the method makes it a universal tool for the study of the structure, dynamics, and association/dissociation of proteins. PMID:26597989

  2. An integrative C. elegans protein-protein interaction network with reliability assessment based on a probabilistic graphical model.

    PubMed

    Huang, Xiao-Tai; Zhu, Yuan; Chan, Leanne Lai Hang; Zhao, Zhongying; Yan, Hong

    2016-01-01

    In Caenorhabditis elegans, a large number of protein-protein interactions (PPIs) are identified by different experiments. However, a comprehensive weighted PPI network, which is essential for signaling pathway inference, is not yet available in this model organism. Therefore, we firstly construct an integrative PPI network in C. elegans with 12,951 interactions involving 5039 proteins from seven molecular interaction databases. Then, a reliability score based on a probabilistic graphical model (RSPGM) is proposed to assess PPIs. It assumes that the random number of interactions between two proteins comes from the Bernoulli distribution to avoid multi-links. The main parameter of the RSPGM score contains a few latent variables which can be considered as several common properties between two proteins. Validations on high-confidence yeast datasets show that RSPGM provides more accurate evaluation than other approaches, and the PPIs in the reconstructed PPI network have higher biological relevance than that in the original network in terms of gene ontology, gene expression, essentiality and the prediction of known protein complexes. Furthermore, this weighted integrative PPI network in C. elegans is employed on inferring interaction path of the canonical Wnt/β-catenin pathway as well. Most genes on the inferred interaction path have been validated to be Wnt pathway components. Therefore, RSPGM is essential and effective for evaluating PPIs and inferring interaction path. Finally, the PPI network with RSPGM scores can be queried and visualized on a user interactive website, which is freely available at . PMID:26555698

  3. Time-domain fluorescence lifetime imaging microscopy: a quantitative method to follow transient protein-protein interactions in living cells.

    PubMed

    Padilla-Parra, Sergi; Audugé, Nicolas; Tramier, Marc; Coppey-Moisan, Maïté

    2015-06-01

    Quantitative analysis in Förster resonance energy transfer (FRET) imaging studies of protein-protein interactions within live cells is still a challenging issue. Many cellular biology applications aim at the determination of the space and time variations of the relative amount of interacting fluorescently tagged proteins occurring in cells. This relevant quantitative parameter can be, at least partially, obtained at a pixel-level resolution by using fluorescence lifetime imaging microscopy (FLIM). Indeed, fluorescence decay analysis of a two-component system (FRET and no FRET donor species), leads to the intrinsic FRET efficiency value (E) and the fraction of the donor-tagged protein that undergoes FRET (fD). To simultaneously obtain fD and E values from a two-exponential fit, data must be acquired with a high number of photons, so that the statistics are robust enough to reduce fitting ambiguities. This is a time-consuming procedure. However, when fast-FLIM acquisitions are used to monitor dynamic changes in protein-protein interactions at high spatial and temporal resolutions in living cells, photon statistics and time resolution are limited. In this case, fitting procedures are unreliable, even for single lifetime donors. We introduce the concept of a minimal fraction of donor molecules involved in FRET (mfD), obtained from the mathematical minimization of fD. Here, we discuss different FLIM techniques and the compromises that must be made between precision and time invested in acquiring FLIM measurements. We show that mfD constitutes an interesting quantitative parameter for fast FLIM because it gives quantitative information about transient interactions in live cells. PMID:26034312

  4. Protein-protein interactions involving voltage-gated sodium channels: Post-translational regulation, intracellular trafficking and functional expression.

    PubMed

    Shao, Dongmin; Okuse, Kenji; Djamgoz, Mustafa B A

    2009-07-01

    Voltage-gated sodium channels (VGSCs), classically known to play a central role in excitability and signalling in nerves and muscles, have also been found to be expressed in a range of 'non-excitable' cells, including lymphocytes, fibroblasts and endothelia. VGSC abnormalities are associated with various diseases including epilepsy, long-QT syndrome 3, Brugada syndrome, sudden infant death syndrome and, more recently, various human cancers. Given their pivotal role in a wide range of physiological and pathophysiological processes, regulation of functional VGSC expression has been the subject of intense study. An emerging theme is post-translational regulation and macro-molecular complexing by protein-protein interactions and intracellular trafficking, leading to changes in functional VGSC expression in plasma membrane. This partially involves endoplasmic reticulum associated degradation and ubiquitin-proteasome system. Several proteins have been shown to associate with VGSCs. Here, we review the interactions involving VGSCs and the following proteins: p11, ankyrin, syntrophin, beta-subunit of VGSC, papin, ERM and Nedd4 proteins. Protein kinases A and C, as well as Ca(2+)-calmodulin dependent kinase II that have also been shown to regulate intracellular trafficking of VGSCs by changing the balance of externalization vs. internalization, and an effort is made to separate these effects from the short-term phosphorylation of mature proteins in plasma membrane. Two further modulatory mechanisms are reciprocal interactions with the cytoskeleton and, late-stage, activity-dependent regulation. Thus, the review gives an updated account of the range of post-translational molecular mechanisms regulating functional VGSC expression. However, many details of VGSC subtype-specific regulation and pathophysiological aspects remain unknown and these are highlighted throughout for completeness. PMID:19401147

  5. Bio::Homology::InterologWalk - A Perl module to build putative protein-protein interaction networks through interolog mapping

    PubMed Central

    2011-01-01

    Background Protein-protein interaction (PPI) data are widely used to generate network models that aim to describe the relationships between proteins in biological systems. The fidelity and completeness of such networks is primarily limited by the paucity of protein interaction information and by the restriction of most of these data to just a few widely studied experimental organisms. In order to extend the utility of existing PPIs, computational methods can be used that exploit functional conservation between orthologous proteins across taxa to predict putative PPIs or 'interologs'. To date most interolog prediction efforts have been restricted to specific biological domains with fixed underlying data sources and there are no software tools available that provide a generalised framework for 'on-the-fly' interolog prediction. Results We introduce Bio::Homology::InterologWalk, a Perl module to retrieve, prioritise and visualise putative protein-protein interactions through an orthology-walk method. The module uses orthology and experimental interaction data to generate putative PPIs and optionally collates meta-data into an Interaction Prioritisation Index that can be used to help prioritise interologs for further analysis. We show the application of our interolog prediction method to the genomic interactome of the fruit fly, Drosophila melanogaster. We analyse the resulting interaction networks and show that the method proposes new interactome members and interactions that are candidates for future experimental investigation. Conclusions Our interolog prediction tool employs the Ensembl Perl API and PSICQUIC enabled protein interaction data sources to generate up to date interologs 'on-the-fly'. This represents a significant advance on previous methods for interolog prediction as it allows the use of the latest orthology and protein interaction data for all of the genomes in Ensembl. The module outputs simple text files, making it easy to customise the results by

  6. Bimolecular Fluorescence Complementation (BiFC) Assay for Protein-Protein Interaction in Onion Cells Using the Helios Gene Gun

    PubMed Central

    Hollender, Courtney A.; Liu, Zhongchi

    2010-01-01

    Investigation of gene function in diverse organisms relies on knowledge of how the gene products interact with each other in their normal cellular environment. The Bimolecular Fluorescence Complementation (BiFC) Assay1 allows researchers to visualize protein-protein interactions in living cells and has become an essential research tool. This assay is based on the facilitated association of two fragments of a fluorescent protein (GFP) that are each fused to a potential interacting protein partner. The interaction of the two protein partners would facilitate the association of the N-terminal and C-terminal fragment of GFP, leading to fluorescence. For plant researchers, onion epidermal cells are an ideal experimental system for conducting the BiFC assay because of the ease in obtaining and preparing onion tissues and the direct visualization of fluorescence with minimal background fluorescence. The Helios Gene Gun (BioRad) is commonly used for bombarding plasmid DNA into onion cells. We demonstrate the use of Helios Gene Gun to introduce plasmid constructs for two interacting Arabidopsis thaliana transcription factors, SEUSS (SEU) and LEUNIG HOMOLOG (LUH)2 and the visualization of their interactions mediated by BiFC in onion epidermal cells. PMID:20567209

  7. Bimolecular fluorescence complementation (BiFC) assay for protein-protein interaction in onion cells using the helios gene gun.

    PubMed

    Hollender, Courtney A; Liu, Zhongchi

    2010-01-01

    Investigation of gene function in diverse organisms relies on knowledge of how the gene products interact with each other in their normal cellular environment. The Bimolecular Fluorescence Complementation (BiFC) Assay(1) allows researchers to visualize protein-protein interactions in living cells and has become an essential research tool. This assay is based on the facilitated association of two fragments of a fluorescent protein (GFP) that are each fused to a potential interacting protein partner. The interaction of the two protein partners would facilitate the association of the N-terminal and C-terminal fragment of GFP, leading to fluorescence. For plant researchers, onion epidermal cells are an ideal experimental system for conducting the BiFC assay because of the ease in obtaining and preparing onion tissues and the direct visualization of fluorescence with minimal background fluorescence. The Helios Gene Gun (BioRad) is commonly used for bombarding plasmid DNA into onion cells. We demonstrate the use of Helios Gene Gun to introduce plasmid constructs for two interacting Arabidopsis thaliana transcription factors, SEUSS (SEU) and LEUNIG HOMOLOG (LUH)(2) and the visualization of their interactions mediated by BiFC in onion epidermal cells. PMID:20567209

  8. Mining for Candidate Genes Related to Pancreatic Cancer Using Protein-Protein Interactions and a Shortest Path Approach

    PubMed Central

    Yuan, Fei; Zhang, Yu-Hang; Wan, Sibao; Wang, ShaoPeng; Kong, Xiang-Yin

    2015-01-01

    Pancreatic cancer (PC) is a highly malignant tumor derived from pancreas tissue and is one of the leading causes of death from cancer. Its molecular mechanism has been partially revealed by validating its oncogenes and tumor suppressor genes; however, the available data remain insufficient for medical workers to design effective treatments. Large-scale identification of PC-related genes can promote studies on PC. In this study, we propose a computational method for mining new candidate PC-related genes. A large network was constructed using protein-protein interaction information, and a shortest path approach was applied to mine new candidate genes based on validated PC-related genes. In addition, a permutation test was adopted to further select key candidate genes. Finally, for all discovered candidate genes, the likelihood that the genes are novel PC-related genes is discussed based on their currently known functions. PMID:26613085

  9. A split horseradish peroxidase for the detection of intercellular protein-protein interactions and sensitive visualization of synapses.

    PubMed

    Martell, Jeffrey D; Yamagata, Masahito; Deerinck, Thomas J; Phan, Sébastien; Kwa, Carolyn G; Ellisman, Mark H; Sanes, Joshua R; Ting, Alice Y

    2016-07-01

    Intercellular protein-protein interactions (PPIs) enable communication between cells in diverse biological processes, including cell proliferation, immune responses, infection, and synaptic transmission, but they are challenging to visualize because existing techniques have insufficient sensitivity and/or specificity. Here we report a split horseradish peroxidase (sHRP) as a sensitive and specific tool for the detection of intercellular PPIs. The two sHRP fragments, engineered through screening of 17 cut sites in HRP followed by directed evolution, reconstitute into an active form when driven together by an intercellular PPI, producing bright fluorescence or contrast for electron microscopy. Fusing the sHRP fragments to the proteins neurexin (NRX) and neuroligin (NLG), which bind each other across the synaptic cleft, enabled sensitive visualization of synapses between specific sets of neurons, including two classes of synapses in the mouse visual system. sHRP should be widely applicable to studying mechanisms of communication between a variety of cell types. PMID:27240195

  10. Changes in flexibility upon binding: Application of the self-consistent pair contact probability method to protein-protein interactions

    NASA Astrophysics Data System (ADS)

    Canino, Lawrence S.; Shen, Tongye; McCammon, J. Andrew

    2002-12-01

    We extend the self-consistent pair contact probability method to the evaluation of the partition function for a protein complex at thermodynamic equilibrium. Specifically, we adapt the method for multichain models and introduce a parametrization for amino acid-specific pairwise interactions. This method is similar to the Gaussian network model but allows for the adjusting of the strengths of native state contacts. The method is first validated on a high resolution x-ray crystal structure of bovine Pancreatic Phospholipase A2 by comparing calculated B-factors with reported values. We then examine binding-induced changes in flexibility in protein-protein complexes, comparing computed results with those obtained from x-ray crystal structures and molecular dynamics simulations. In particular, we focus on the mouse acetylcholinesterase:fasciculin II and the human α-thrombin:thrombomodulin complexes.

  11. A review of in silico approaches for analysis and prediction of HIV-1-human protein-protein interactions.

    PubMed

    Bandyopadhyay, Sanghamitra; Ray, Sumanta; Mukhopadhyay, Anirban; Maulik, Ujjwal

    2015-09-01

    The computational or in silico approaches for analysing the HIV-1-human protein-protein interaction (PPI) network, predicting different host cellular factors and PPIs and discovering several pathways are gaining popularity in the field of HIV research. Although there exist quite a few studies in this regard, no previous effort has been made to review these works in a comprehensive manner. Here we review the computational approaches that are devoted to the analysis and prediction of HIV-1-human PPIs. We have broadly categorized these studies into two fields: computational analysis of HIV-1-human PPI network and prediction of novel PPIs. We have also presented a comparative assessment of these studies and proposed some methodologies for discussing the implication of their results. We have also reviewed different computational techniques for predicting HIV-1-human PPIs and provided a comparative study of their applicability. We believe that our effort will provide helpful insights to the HIV research community. PMID:25479794

  12. Electrostatic Similarities between Protein and Small Molecule Ligands Facilitate the Design of Protein-Protein Interaction Inhibitors

    PubMed Central

    Zhang, Kam Y. J.

    2013-01-01

    One of the underlying principles in drug discovery is that a biologically active compound is complimentary in shape and molecular recognition features to its receptor. This principle infers that molecules binding to the same receptor may share some common features. Here, we have investigated whether the electrostatic similarity can be used for the discovery of small molecule protein-protein interaction inhibitors (SMPPIIs). We have developed a method that can be used to evaluate the similarity of electrostatic potentials between small molecules and known protein ligands. This method was implemented in a software called EleKit. Analyses of all available (at the time of research) SMPPII structures indicate that SMPPIIs bear some similarities of electrostatic potential with the ligand proteins of the same receptor. This is especially true for the more polar SMPPIIs. Retrospective analysis of several successful SMPPIIs has shown the applicability of EleKit in the design of new SMPPIIs. PMID:24130741

  13. Bimolecular Fluorescence Complementation (BiFC) Analysis of Protein-Protein Interactions and Assessment of Subcellular Localization in Live Cells.

    PubMed

    Pratt, Evan P S; Owens, Jake L; Hockerman, Gregory H; Hu, Chang-Deng

    2016-01-01

    Bimolecular fluorescence complementation (BiFC) is a fluorescence imaging technique used to visualize protein-protein interactions (PPIs) in live cells and animals. One unique application of BiFC is to reveal subcellular localization of PPIs. The superior signal-to-noise ratio of BiFC in comparison with fluorescence resonance energy transfer or bioluminescence resonance energy transfer enables its wide applications. Here, we describe how confocal microscopy can be used to detect and quantify PPIs and their subcellular localization. We use basic leucine zipper transcription factor proteins as an example to provide a step-by-step BiFC protocol using a Nikon A1 confocal microscope and NIS-Elements imaging software. The protocol given below can be readily adapted for use with other confocal microscopes or imaging software. PMID:27515079

  14. Characterizing alpha helical properties of Ebola viral proteins as potential targets for inhibition of alpha-helix mediated protein-protein interactions.

    PubMed

    Chakraborty, Sandeep; Rao, Basuthkar J; Asgeirsson, Bjarni; Dandekar, Abhaya

    2014-01-01

    Ebola, considered till recently as a rare and endemic disease, has dramatically transformed into a potentially global humanitarian crisis. The genome of Ebola, a member of the Filoviridae family, encodes seven proteins. Based on the recently implemented software (PAGAL) for analyzing the hydrophobicity and amphipathicity properties of alpha helices (AH) in proteins, we characterize the helices in the Ebola proteome. We demonstrate that AHs with characteristically unique features are involved in critical interactions with the host proteins. For example, the Ebola virus membrane fusion subunit, GP2, from the envelope glycoprotein ectodomain has an AH with a large hydrophobic moment. The neutralizing antibody (KZ52) derived from a human survivor of the 1995 Kikwit outbreak recognizes a protein epitope on this AH, emphasizing the critical nature of this secondary structure in the virulence of the Ebola virus. Our method ensures a comprehensive list of such `hotspots'. These helices probably are or can be the target of molecules designed to inhibit AH mediated protein-protein interactions. Further, by comparing the AHs in proteins of the related Marburg viruses, we are able to elicit subtle changes in the proteins that might render them ineffective to previously successful drugs. Such differences are difficult to identify by a simple sequence or structural alignment. Thus, analyzing AHs in the small Ebola proteome can aid rational design aimed at countering the `largest Ebola epidemic, affecting multiple countries in West Africa' ( http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/index.html). PMID:25717367

  15. Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

    PubMed Central

    Filteau, Marie; Leducq, Jean-Baptiste; Dubé, Alexandre K.; Landry, Christian R.

    2015-01-01

    Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein’s function, regulation and localization often depend on its interactions with other proteins. Here, we describe a protocol for the yeast protein-fragment complementation assay (PCA), a powerful method to detect direct and proximal associations between proteins in living cells. The interaction between two proteins, each fused to a dihydrofolate reductase (DHFR) protein fragment, translates into growth of yeast strains in presence of the drug methotrexate (MTX). Differential fitness, resulting from different amounts of reconstituted DHFR enzyme, can be quantified on high-density colony arrays, allowing to differentiate interacting from non-interacting bait-prey pairs. The high-throughput protocol presented here is performed using a robotic platform that parallelizes mating of bait and prey strains carrying complementary DHFR-fragment fusion proteins and the survival assay on MTX. This protocol allows to systematically test for thousands of protein-protein interactions (PPIs) involving bait proteins of interest and offers several advantages over other PPI detection assays, including the study of proteins expressed from their endogenous promoters without the need for modifying protein localization and for the assembly of complex reporter constructs. PMID:25867901

  16. CapsidMaps: Protein-protein interaction pattern discovery platform for the structural analysis of virus capsids using Google Maps

    PubMed Central

    Carrillo-Tripp, Mauricio; Montiel-García, Daniel Jorge; Brooks, Charles L.; Reddy, Vijay

    2016-01-01

    Structural analysis and visualization of protein-protein interactions is a challenging task since it is difficult to appreciate easily the extent of all contacts made by the residues forming the interfaces. In the case of viruses, structural analysis becomes even more demanding because several interfaces coexist and, in most cases, these are formed by hundreds of contacting residues that belong to multiple interacting coat proteins. CapsidMaps is an interactive analysis and visualization tool that is designed to benefit the structural virology community. Developed as an improved extension of the φ-ψ Explorer, here we describe the details of its design and implementation. We present results of analysis of a spherical virus to showcase the features and utility of the new tool. CapsidMaps also facilitates the comparison of quaternary interactions between two spherical virus particles by computing a similarity (S)-score. The tool can also be used to identify residues that are solvent exposed and in the process of locating antigenic epitope regions as well as residues forming the inside surface of the capsid that interact with the nucleic acid genome. CapsidMaps is part of the VIPERdb Science Gateway, and is freely available as a web-based and cross-browser compliant application at http://viperdb.scripps.edu. PMID:25697908

  17. Cooperative DNA Recognition Modulated by an Interplay between Protein-Protein Interactions and DNA-Mediated Allostery

    PubMed Central

    Merino, Felipe; Bouvier, Benjamin; Cojocaru, Vlad

    2015-01-01

    Highly specific transcriptional regulation depends on the cooperative association of transcription factors into enhanceosomes. Usually, their DNA-binding cooperativity originates from either direct interactions or DNA-mediated allostery. Here, we performed unbiased molecular simulations followed by simulations of protein-DNA unbinding and free energy profiling to study the cooperative DNA recognition by OCT4 and SOX2, key components of enhanceosomes in pluripotent cells. We found that SOX2 influences the orientation and dynamics of the DNA-bound configuration of OCT4. In addition SOX2 modifies the unbinding free energy profiles of both DNA-binding domains of OCT4, the POU specific and POU homeodomain, despite interacting directly only with the first. Thus, we demonstrate that the OCT4-SOX2 cooperativity is modulated by an interplay between protein-protein interactions and DNA-mediated allostery. Further, we estimated the change in OCT4-DNA binding free energy due to the cooperativity with SOX2, observed a good agreement with experimental measurements, and found that SOX2 affects the relative DNA-binding strength of the two OCT4 domains. Based on these findings, we propose that available interaction partners in different biological contexts modulate the DNA exploration routes of multi-domain transcription factors such as OCT4. We consider the OCT4-SOX2 cooperativity as a paradigm of how specificity of transcriptional regulation is achieved through concerted modulation of protein-DNA recognition by different types of interactions. PMID:26067358

  18. An inter-species protein-protein interaction network across vast evolutionary distance.

    PubMed

    Zhong, Quan; Pevzner, Samuel J; Hao, Tong; Wang, Yang; Mosca, Roberto; Menche, Jörg; Taipale, Mikko; Taşan, Murat; Fan, Changyu; Yang, Xinping; Haley, Patrick; Murray, Ryan R; Mer, Flora; Gebreab, Fana; Tam, Stanley; MacWilliams, Andrew; Dricot, Amélie; Reichert, Patrick; Santhanam, Balaji; Ghamsari, Lila; Calderwood, Michael A; Rolland, Thomas; Charloteaux, Benoit; Lindquist, Susan; Barabási, Albert-László; Hill, David E; Aloy, Patrick; Cusick, Michael E; Xia, Yu; Roth, Frederick P; Vidal, Marc

    2016-01-01

    In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical-versus-functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an "inter-interactome" approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter-interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra-species networks. Although substantially reduced relative to intra-species networks, the levels of functional overlap in the yeast-human inter-interactome network uncover significant remnants of co-functionality widely preserved in the two proteomes beyond human-yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co-functionality. Such non-functional interactions, however, represent a reservoir from which nascent functional interactions may arise. PMID:27107014

  19. Improved silicon nanowire field-effect transistors for fast protein-protein interaction screening.

    PubMed

    Lin, Ti-Yu; Li, Bor-Ran; Tsai, Sheng-Ta; Chen, Chien-Wei; Chen, Chung-Hsuan; Chen, Yit-Tsong; Pan, Chien-Yuan

    2013-02-21

    Understanding how proteins interact with each other is the basis for studying the biological mechanisms behind various physiological activities. Silicon nanowire field-effect transistors (SiNW-FETs) are sensitive sensors used to detect biomolecular interactions in real-time. However, the majority of the applications that use SiNW-FETs are for known interactions between different molecules. To explore the capability of SiNW-FETs as fast screening devices to identify unknown interacting molecules, we applied mass spectrometry (MS) to analyze molecules reversibly bound to the SiNW-FETs. Calmodulin (CaM) is a Ca(2+)-sensing protein that is ubiquitously expressed in cells and its interaction with target molecules is Ca(2+)-dependent. By modifying the SiNW-FET surface with glutathione, glutathione S-transferase (GST)-tagged CaM binds reversibly to the SiNW-FET. We first verified the Ca(2+)-dependent interaction between GST-CaM and purified troponin I, which is involved in muscle contraction, through the conductance changes of the SiNW-FET. Furthermore, the cell lysate containing overexpressed Ca(2+)/CaM-dependent protein kinase IIα induced a conductance change in the GST-CaM-modified SiNW-FET. The bound proteins were eluted and subsequently identified by MS as CaM and kinase. In another example, candidate proteins from neuronal cell lysates interacting with calneuron I (CalnI), a CaM-like protein, were captured with a GST-CalnI-modified SiNW-FET. The proteins that interacted with CalnI were eluted and verified by MS. The Ca(2+)-dependent interaction between GST-CalnI and one of the candidates, heat shock protein 70, was re-confirmed via the SiNW-FET measurement. Our results demonstrate the effectiveness of combining MS with SiNW-FETs to quickly screen interacting molecules from cell lysates. PMID:23235921

  20. PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine

    PubMed Central

    Donaldson, Ian; Martin, Joel; de Bruijn, Berry; Wolting, Cheryl; Lay, Vicki; Tuekam, Brigitte; Zhang, Shudong; Baskin, Berivan; Bader, Gary D; Michalickova, Katerina; Pawson, Tony; Hogue, Christopher WV

    2003-01-01

    Background The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND) seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND. Results Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days. Conclusions Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at . Current capabilities allow searching for human, mouse and yeast protein-interaction information. PMID:12689350

  1. Protein-protein interactions in two potyviruses using the yeast two-hybrid system.

    PubMed

    Lin, Lin; Shi, Yuhong; Luo, Zhaopeng; Lu, Yuwen; Zheng, Hongying; Yan, Fei; Chen, Jiong; Chen, Jianping; Adams, M J; Wu, Yunfeng

    2009-06-01

    Interactions between all ten mature proteins of the potyviruses Soybean mosaic virus (Pinellia ternata isolate) and Shallot yellow stripe virus were investigated using yeast two-hybrid (Y2H) assays. Consistently strong self-interactions were found between the pairs of HC-Pro, VPg, NIa-Pro, NIb and CP in both viruses. Apart from the NIb, such interactions have been previously reported for some other potyviruses. The 6K1/NIa-Pro combination gave a consistently moderate to strong interaction in both directions for both viruses. This interaction occurred even when the 6K1 of SMV-P was truncated to eliminate the C-terminal motif that acts as a recognition site for cleavage by the NIa-Pro. Many other interactions occurred only in one direction or only for one of the two viruses. When taken together with other published reports, the data suggest that interactions detected by Y2H should be regarded as only preliminary indications. PMID:19189854

  2. Complex architecture of major histocompatibility complex class II promoters: reiterated motifs and conserved protein-protein interactions.

    PubMed Central

    Jabrane-Ferrat, N; Fontes, J D; Boss, J M; Peterlin, B M

    1996-01-01

    The S box (also known as at the H, W, or Z box) is the 5'-most element of the conserved upstream sequences in promoters of major histocompatibility complex class II genes. It is important for their B-cell-specific and interferon gamma-inducible expression. In this study, we demonstrate that the S box represents a duplication of the downstream X box. First, RFX, which is composed of the RFX5-p36 heterodimer that binds to the X box, also binds to the S box and its 5'-flanking sequence. Second, NF-Y, which binds to the Y box and increases interactions between RFX and the X box, also increases the binding of RFX to the S box. Third, RFXs bound to S and X boxes interact with each other in a spatially constrained manner. Finally, we confirmed these protein-protein and protein-DNA interactions by expressing a hybrid RFX5-VP16 protein in cells. We conclude that RFX binds to S and X boxes and that complex interactions between RFX and NF-Y direct B-cell-specific and interferon gamma-inducible expression or major histocompatibility complex class II genes. PMID:8756625

  3. Detecting protein-protein interactions with a novel matrix-based protein sequence representation and support vector machines.

    PubMed

    You, Zhu-Hong; Li, Jianqiang; Gao, Xin; He, Zhou; Zhu, Lin; Lei, Ying-Ke; Ji, Zhiwei

    2015-01-01

    Proteins and their interactions lie at the heart of most underlying biological processes. Consequently, correct detection of protein-protein interactions (PPIs) is of fundamental importance to understand the molecular mechanisms in biological systems. Although the convenience brought by high-throughput experiment in technological advances makes it possible to detect a large amount of PPIs, the data generated through these methods is unreliable and may not be completely inclusive of all possible PPIs. Targeting at this problem, this study develops a novel computational approach to effectively detect the protein interactions. This approach is proposed based on a novel matrix-based representation of protein sequence combined with the algorithm of support vector machine (SVM), which fully considers the sequence order and dipeptide information of the protein primary sequence. When performed on yeast PPIs datasets, the proposed method can reach 90.06% prediction accuracy with 94.37% specificity at the sensitivity of 85.74%, indicating that this predictor is a useful tool to predict PPIs. Achieved results also demonstrate that our approach can be a helpful supplement for the interactions that have been detected experimentally. PMID:26000305

  4. Constraints imposed by nonfunctional protein-protein interactions on gene expression and proteome size

    NASA Astrophysics Data System (ADS)

    Zhang, Jingshan; Maslov, Sergei; Shakhnovich, Eugene

    2009-03-01

    Crowded intracellular environments present a challenge for proteins to form functional specific complexes while reducing nonfunctional interactions with promiscuous nonfunctional partners. Here we show how nonfunctional interactions limit the proteome diversity and the average concentration of co-expressed and co-localized proteins. We use yeast compartments to verify our hypothesis that the yeast proteome has evolved to operate closely to the upper limit of its size, while keeping individual protein concentrations sufficiently low to reduce nonfunctional interactions. These findings have implication for conceptual understanding of intracellular compartmentalization, multicellularity, and differentiation.

  5. The Interactions of the Student Aid Office.

    ERIC Educational Resources Information Center

    Rutter, Thomas; Braxton, Larry

    The interactions of student financial aid offices with other campus offices, government agencies, and private groups are described. Campus groups that interact with the financial aid office include the following: campus veterans affairs office, student affairs office/dean of students, recruitment staff, campus registrar, chief executive officer,…

  6. A fluorescent reporter for mapping cellular protein-protein interactions in time and space

    PubMed Central

    Moreno, Daniel; Neller, Joachim; Kestler, Hans A; Kraus, Johann; Dünkler, Alexander; Johnsson, Nils

    2013-01-01

    We introduce a fluorescent reporter for monitoring protein–protein interactions in living cells. The method is based on the Split-Ubiquitin method and uses the ratio of two auto-fluorescent reporter proteins as signal for interaction (SPLIFF). The mating of two haploid yeast cells initiates the analysis and the interactions are followed online by two-channel time-lapse microscopy of the diploid cells during their first cell cycle. Using this approach we could with high spatio-temporal resolution visualize the differences between the interactions of the microtubule binding protein Stu2p with two of its binding partners, monitor the transient association of a Ran-GTPase with its receptors at the nuclear pore, and distinguish between protein interactions at the polar cortical domain at different phases of polar growth. These examples further demonstrate that protein–protein interactions identified from large-scale screens can be effectively followed up by high-resolution single-cell analysis. PMID:23511205

  7. Kinetic Measurements Reveal Enhanced Protein-Protein Interactions at Intercellular Junctions

    PubMed Central

    Shashikanth, Nitesh; Kisting, Meridith A.; Leckband, Deborah E.

    2016-01-01

    The binding properties of adhesion proteins are typically quantified from measurements with soluble fragments, under conditions that differ radically from the confined microenvironment of membrane bound proteins in adhesion zones. Using classical cadherin as a model adhesion protein, we tested the postulate that confinement within quasi two-dimensional intercellular gaps exposes weak protein interactions that are not detected in solution binding assays. Micropipette-based measurements of cadherin-mediated, cell-cell binding kinetics identified a unique kinetic signature that reflects both adhesive (trans) bonds between cadherins on opposing cells and lateral (cis) interactions between cadherins on the same cell. In solution, proposed lateral interactions were not detected, even at high cadherin concentrations. Mutations postulated to disrupt lateral cadherin association altered the kinetic signatures, but did not affect the adhesive (trans) binding affinity. Perturbed kinetics further coincided with altered cadherin distributions at junctions, wound healing dynamics, and paracellular permeability. Intercellular binding kinetics thus revealed cadherin interactions that occur within confined, intermembrane gaps but not in solution. Findings further demonstrate the impact of these revealed interactions on the organization and function of intercellular junctions. PMID:27009566

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

    PubMed

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

    2009-05-01

    Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to pi-related interactions, especially pi . . . pi interactions. PMID:19273533

  9. STRING v9.1: protein-protein interaction networks, with increased coverage and integration

    PubMed Central

    Franceschini, Andrea; Szklarczyk, Damian; Frankild, Sune; Kuhn, Michael; Simonovic, Milan; Roth, Alexander; Lin, Jianyi; Minguez, Pablo; Bork, Peer; von Mering, Christian; Jensen, Lars J.

    2013-01-01

    Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made—particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks. PMID:23203871

  10. Kinetic Measurements Reveal Enhanced Protein-Protein Interactions at Intercellular Junctions.

    PubMed

    Shashikanth, Nitesh; Kisting, Meridith A; Leckband, Deborah E

    2016-01-01

    The binding properties of adhesion proteins are typically quantified from measurements with soluble fragments, under conditions that differ radically from the confined microenvironment of membrane bound proteins in adhesion zones. Using classical cadherin as a model adhesion protein, we tested the postulate that confinement within quasi two-dimensional intercellular gaps exposes weak protein interactions that are not detected in solution binding assays. Micropipette-based measurements of cadherin-mediated, cell-cell binding kinetics identified a unique kinetic signature that reflects both adhesive (trans) bonds between cadherins on opposing cells and lateral (cis) interactions between cadherins on the same cell. In solution, proposed lateral interactions were not detected, even at high cadherin concentrations. Mutations postulated to disrupt lateral cadherin association altered the kinetic signatures, but did not affect the adhesive (trans) binding affinity. Perturbed kinetics further coincided with altered cadherin distributions at junctions, wound healing dynamics, and paracellular permeability. Intercellular binding kinetics thus revealed cadherin interactions that occur within confined, intermembrane gaps but not in solution. Findings further demonstrate the impact of these revealed interactions on the organization and function of intercellular junctions. PMID:27009566

  11. Computational approaches for prediction of pathogen-host protein-protein interactions

    PubMed Central

    Nourani, Esmaeil; Khunjush, Farshad; Durmuş, Saliha

    2015-01-01

    Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key parts of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI) systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. These have motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multitask learning methods are preferred. Here, we present an overview of these computational approaches for predicting PHI systems, discussing their weakness and abilities, with future directions. PMID:25759684

  12. Computational approaches for prediction of pathogen-host protein-protein interactions.

    PubMed

    Nourani, Esmaeil; Khunjush, Farshad; Durmuş, Saliha

    2015-01-01

    Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key parts of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI) systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. These have motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multitask learning methods are preferred. Here, we present an overview of these computational approaches for predicting PHI systems, discussing their weakness and abilities, with future directions. PMID:25759684

  13. Inhibition of CDC25B Phosphatase Through Disruption of Protein-Protein Interaction

    SciTech Connect

    Lund, George; Dudkin, Sergii; Borkin, Dmitry; Ni, Wendi; Grembecka, Jolanta; Cierpicki, Tomasz

    2015-04-29

    CDC25 phosphatases are key cell cycle regulators and represent very attractive but challenging targets for anticancer drug discovery. Here, we explored whether fragment-based screening represents a valid approach to identify inhibitors of CDC25B. This resulted in identification of 2-fluoro-4-hydroxybenzonitrile, which directly binds to the catalytic domain of CDC25B. Interestingly, NMR data and the crystal structure demonstrate that this compound binds to the pocket distant from the active site and adjacent to the protein–protein interaction interface with CDK2/Cyclin A substrate. Furthermore, we developed a more potent analogue that disrupts CDC25B interaction with CDK2/Cyclin A and inhibits dephosphorylation of CDK2. Based on these studies, we provide a proof of concept that targeting CDC25 phosphatases by inhibiting their protein–protein interactions with CDK2/Cyclin A substrate represents a novel, viable opportunity to target this important class of enzymes.

  14. A magnetic nanoparticles relaxation sensor for protein-protein interaction detection at ultra-low magnetic field.

    PubMed

    Wang, Wei; Ma, Peixiang; Dong, Hui; Krause, Hans-Joachim; Zhang, Yi; Willbold, Dieter; Offenhaeusser, Andreas; Gu, Zhongwei

    2016-06-15

    Functionalized magnetic nanoparticles (MNPs) can serve as magnetic relaxation sensors (MRSs) to detect different biological targets, because the clustering of magnetic particle may cause the spin-spin relaxation time (T2) decrease of the surrounding water protons. However, the application of MNPs in clinical NMR systems faces the challenge of poor stability at magnetic field strengths in the order of tesla. The recently developed ultra-low field (ULF) NMR technique working at microtesla (μT) range then becomes a candidate. Herein, we incorporated superconducting quantum interference device (SQUID) as the detector in the ultra-low field system to enhance the sensitivity. We functionalized the Fe3O4 nanoparticles with the gama-aminobutyrate type A receptor-associated proteins (GABARAP), which specifically interact with calreticulin (CRT). As a result of the interaction between GABARAP and CRT, the clustering of the functionalized MNPs generates local magnetic fields, which accelerate the dephasing of the water protons in the vicinity. We analyzed the relation between T2 values and the CRT concentrations at 211μT and the low detection limit for CRT is 10 pg/ml, which is superior to the immunoblot system. The high sensitivity of the ULF NMR system for protein-protein interaction detection demonstrates the potential to use this inexpensive, portable system for quick biochemical and clinical assays. PMID:26914374

  15. System-level comparison of protein-protein interactions between viruses and the human type I interferon system network.

    PubMed

    Navratil, V; de Chassey, B; Meyniel, L; Pradezynski, F; André, P; Rabourdin-Combe, C; Lotteau, V

    2010-07-01

    Innate immunity has evolved complex molecular pathways to protect organisms from viral infections. One pivotal line of cellular defense is the induction of the antiviral effect of interferon. To circumvent this primary response and achieve their own replication, viruses have developed complex molecular strategies. Here, we provide a systems-level study of the human type I interferon system subversion by the viral proteome, by reconstructing the underlying protein-protein interaction network. At this network level, viruses establish a massive and a gradual attack, from receptors to transcription factors, by interacting preferentially with highly connected and central proteins as well as interferon-induced proteins. We also demonstrate that viruses significantly target 22% of the proteins directly interacting with the type I interferon system network, suggesting the relevance of our network-based method to identify new candidates involved in the regulation of the antiviral response. Finally, based on the comparative analysis of interactome profiles across four viral families, we provide evidence of common and differential targeting strategies. PMID:20459142

  16. Analysis of vaccinia virus-host protein-protein interactions: validations of yeast two-hybrid screenings.

    PubMed

    Zhang, Leiliang; Villa, Nancy Y; Rahman, Masmudur M; Smallwood, Sherin; Shattuck, Donna; Neff, Chris; Dufford, Max; Lanchbury, Jerry S; Labaer, Joshua; McFadden, Grant

    2009-09-01

    Vaccinia virus, a large double-stranded DNA virus, is the prototype of the Orthopoxvirus genus, which includes several pathogenic poxviruses of humans, such as monkeypox virus and variola virus. Here, we report a comprehensive yeast two-hybrid (Y2H) screening for the protein-protein interactions between vaccinia and human proteins. A total of 109 novel vaccinia-human protein interactions were detected among 33 viral proteins. To validate subsets of those interactions, we constructed an ORFeome library of vaccinia virus strain WR using the Gateway plasmid cloning system. By co-expressing selected vaccinia and host proteins in a variety of expression systems, we found that at least 17 of the Y2H hits identified between vaccinia and human proteins can be verified by independent methods using GST pull-down assays, representing a 63% validation rate for the Y2H hits examined (17/27). Because the cloned ORFs are conveniently transferable from the entry vectors to various destination expression vectors, the vaccinia ORFeome library will be a useful resource for future high-throughput functional proteomic experiments. PMID:19637933

  17. BRET: NanoLuc-Based Bioluminescence Resonance Energy Transfer Platform to Monitor Protein-Protein Interactions in Live Cells.

    PubMed

    Mo, Xiu-Lei; Fu, Haian

    2016-01-01

    Bioluminescence resonance energy transfer (BRET) is a prominent biophysical technology for monitoring molecular interactions, and has been widely used to study protein-protein interactions (PPI) in live cells. This technology requires proteins of interest to be associated with an energy donor (i.e., luciferase) and an acceptor (e.g., fluorescent protein) molecule. Upon interaction of the proteins of interest, the donor and acceptor will be brought into close proximity and energy transfer of chemical reaction-induced luminescence to its corresponding acceptor will result in an increased emission at an acceptor-defined wavelength, generating the BRET signal. We leverage the advantages of the superior optical properties of the NanoLuc(®) luciferase (NLuc) as a BRET donor coupled with Venus, a yellow fluorescent protein, as acceptor. We term this NLuc-based BRET platform "BRET(n)". BRET(n) has been demonstrated to have significantly improved assay performance, compared to previous BRET technologies, in terms of sensitivity and scalability. This chapter describes a step-by-step practical protocol for developing a BRET(n) assay in a multi-well plate format to detect PPIs in live mammalian cells. PMID:27317001

  18. Evolution of acyl-ACP-thioesterases and β-ketoacyl-ACP-synthases revealed by protein-protein interactions

    PubMed Central

    Beld, Joris; Blatti, Jillian L.; Behnke, Craig; Mendez, Michael; Burkart, Michael D.

    2014-01-01

    The fatty acid synthase (FAS) is a conserved primary metabolic enzyme complex capable of tolerating cross-species engineering of domains for the development of modified and overproduced fatty acids. In eukaryotes, acyl-acyl carrier protein thioesterases (TEs) off-load mature cargo from the acyl carrier protein (ACP), and plants have developed TEs for short/medium-chain fatty acids. We showed that engineering plant TEs into the green microalga Chlamydomonas reinhardtii does not result in the predicted shift in fatty acid profile. Since fatty acid biosynthesis relies on substrate recognition and protein-protein interactions between the ACP and its partner enzymes, we hypothesized that plant TEs and algal ACP do not functionally interact. Phylogenetic analysis revealed major evolutionary differences between FAS enzymes, including TEs and ketoacyl synthases (KSs), in which the former is present only in some species, whereas the latter is present in all, and has a common ancestor. In line with these results, TEs appeared to be selective towards their ACP partners whereas KSs showed promiscuous behavior across bacterial, plant and algal species. Based on phylogenetic analyses, in silico docking, in vitro mechanistic crosslinking and in vivo algal engineering, we propose that phylogeny can predict effective interactions between ACPs and partner enzymes. PMID:25110394

  19. Large-scale identification of potential drug targets based on the topological features of human protein-protein interaction network.

    PubMed

    Li, Zhan-Chao; Zhong, Wen-Qian; Liu, Zhi-Qing; Huang, Meng-Hua; Xie, Yun; Dai, Zong; Zou, Xiao-Yong

    2015-04-29

    Identifying potential drug target proteins is a crucial step in the process of drug discovery and plays a key role in the study of the molecular mechanisms of disease. Based on the fact that the majority of proteins exert their functions through interacting with each other, we propose a method to recognize target proteins by using the human protein-protein interaction network and graph theory. In the network, vertexes and edges are weighted by using the confidence scores of interactions and descriptors of protein primary structure, respectively. The novel network topological features are defined and employed to characterize protein using existing databases. A widely used minimum redundancy maximum relevance and random forests algorithm are utilized to select the optimal feature subset and construct model for the identification of potential drug target proteins at the proteome scale. The accuracies of training set and test set are 89.55% and 85.23%. Using the constructed model, 2127 potential drug target proteins have been recognized and 156 drug target proteins have been validated in the database of drug target. In addition, some new drug target proteins can be considered as targets for treating diseases of mucopolysaccharidosis, non-arteritic anterior ischemic optic neuropathy, Bernard-Soulier syndrome and pseudo-von Willebrand, etc. It is anticipated that the proposed method may became a powerful high-throughput virtual screening tool of drug target. PMID:25847157

  20. A simple feature construction method for predicting upstream/downstream signal flow in human protein-protein interaction networks

    PubMed Central

    Mei, Suyu; Zhu, Hao

    2015-01-01

    Signaling pathways play important roles in understanding the underlying mechanism of cell growth, cell apoptosis, organismal development and pathways-aberrant diseases. Protein-protein interaction (PPI) networks are commonly-used infrastructure to infer signaling pathways. However, PPI networks generally carry no information of upstream/downstream relationship between interacting proteins, which retards our inferring the signal flow of signaling pathways. In this work, we propose a simple feature construction method to train a SVM (support vector machine) classifier to predict PPI upstream/downstream relations. The domain based asymmetric feature representation naturally embodies domain-domain upstream/downstream relations, providing an unconventional avenue to predict the directionality between two objects. Moreover, we propose a semantically interpretable decision function and a macro bag-level performance metric to satisfy the need of two-instance depiction of an interacting protein pair. Experimental results show that the proposed method achieves satisfactory cross validation performance and independent test performance. Lastly, we use the trained model to predict the PPIs in HPRD, Reactome and IntAct. Some predictions have been validated against recent literature. PMID:26648121

  1. PROTEIN-PROTEIN INTERACTIONS IN CASEIN PRECIPITATED BY PRESSURIZED CARBON DIOXIDE (C02 CASEIN)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Despite much research on the casein micelles, the colloidal calcium-phosphate complexes of skim milk, the molecular interactions involved in the casein micelles remain elusive. We investigate casein precipitated by pressurized carbon dioxide (CO2 casein) to elucidate the mechanism of the formation o...

  2. A general system for studying protein-protein interactions in gram-negative bacteria

    SciTech Connect

    Pelletier, Dale A.; Hurst, G. B.; Foote, Linda J.; Lankford, Patricia K.; McKeown, Cathy K.; Lu, Tse-Yuan S.; Schmoyer, Denise D.; Shah, Manesh B.; Hervey IV, W. J.; McDonald, W. Hayes; Hooker, Brian S.; Cannon, William R.; Daly, Don S.; Gilmore, Jason M.; Wiley, H. S.; Auberry, Deanna L.; Wang, Yisong; Larimer, Frank; Kennel, S. J.; Doktycz, M. J.; Morrell-Falvey, Jennifer; Owens, Elizabeth T.; Buchanan, M. V.

    2008-08-01

    One of the most promising of the emerging methods for large-scale studies of interactions among proteins is co-isolation of an affinity-tagged protein and its interaction partners, followed by mass spectrometric identification of the co-purifying proteins. We describe a methodology for systematically identifying the proteins that interact with affinity-tagged “bait” proteins expressed from a medium copy plasmid, which are based on a broad host range (pBBR1MCS5) vector backbone that has been modified to incorporate the Gateway DEST plasmid multiple cloning region. This construct was designed to facilitate expression of fusion proteins bearing an affinity tag, across a range of Gram negative bacterial hosts. We demonstrate the performance of 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 from the RNA polymerase complex from these two species compared favorably with those for both plasmid- and chromosomally-encoded affinity-tagged fusion proteins expressed in a model organism, E. coli.

  3. The variable C-terminus of cysteine string proteins modulates exocytosis and protein-protein interactions.

    PubMed

    Boal, Frédéric; Zhang, Hui; Tessier, Céline; Scotti, Pier; Lang, Jochen

    2004-12-28

    Cysteine string proteins (Csps) are vesicle proteins involved in neurotransmission and hormone exocytosis. They are composed of distinct domains: a variable N-terminus, a J-domain followed by a linker region, a cysteine-rich string, and a C-terminus which diverges among isoforms. Their precise function and interactions are not fully understood. Using insulin exocytosis as a model, we show that the linker region and the C-terminus, but not the variable N-terminus, regulate overall secretion. Moreover, endogenous Csp1 binds in a calcium-dependent manner to monomeric VAMP2, and this interaction requires the C-terminus of Csp. The interaction is isoform specific as recombinant Csp1 binds VAMP1 and VAMP7, but not VAMP3. Cross-linking in permeabilized clonal beta-cells revealed homodimerization of Csp which is stimulated by Ca(2+) and again modulated by the variant C-terminus. Our data suggest that both interactions of Csp occur during exocytosis and may explain the effect of the variant C-terminus of this chaperon protein on peptide hormone secretion. PMID:15610015

  4. The Role of Protein-Protein and Protein-Membrane Interactions on P450 Function.

    PubMed

    Scott, Emily E; Wolf, C Roland; Otyepka, Michal; Humphreys, Sara C; Reed, James R; Henderson, Colin J; McLaughlin, Lesley A; Paloncýová, Markéta; Navrátilová, Veronika; Berka, Karel; Anzenbacher, Pavel; Dahal, Upendra P; Barnaba, Carlo; Brozik, James A; Jones, Jeffrey P; Estrada, D Fernando; Laurence, Jennifer S; Park, Ji Won; Backes, Wayne L

    2016-04-01

    This symposium summary, sponsored by the ASPET, was held at Experimental Biology 2015 on March 29, 2015, in Boston, Massachusetts. The symposium focused on: 1) the interactions of cytochrome P450s (P450s) with their redox partners; and 2) the role of the lipid membrane in their orientation and stabilization. Two presentations discussed the interactions of P450s with NADPH-P450 reductase (CPR) and cytochrome b5. First, solution nuclear magnetic resonance was used to compare the protein interactions that facilitated either the hydroxylase or lyase activities of CYP17A1. The lyase interaction was stimulated by the presence of b5 and 17α-hydroxypregnenolone, whereas the hydroxylase reaction was predominant in the absence of b5. The role of b5 was also shown in vivo by selective hepatic knockout of b5 from mice expressing CYP3A4 and CYP2D6; the lack of b5 caused a decrease in the clearance of several substrates. The role of the membrane on P450 orientation was examined using computational methods, showing that the proximal region of the P450 molecule faced the aqueous phase. The distal region, containing the substrate-access channel, was associated with the membrane. The interaction of NADPH-P450 reductase (CPR) with the membrane was also described, showing the ability of CPR to "helicopter" above the membrane. Finally, the endoplasmic reticulum (ER) was shown to be heterogeneous, having ordered membrane regions containing cholesterol and more disordered regions. Interestingly, two closely related P450s, CYP1A1 and CYP1A2, resided in different regions of the ER. The structural characteristics of their localization were examined. These studies emphasize the importance of P450 protein organization to their function. PMID:26851242

  5. Thermodynamic and structural investigation of bi-specificity in protein-protein interactions

    PubMed Central

    Zhao, Huaying; Naganathan, Saranga; Beckett, Dorothy

    2009-01-01

    The ability of a single protein to interact with multiple protein partners is central to many biological processes. However, the physical chemical and structural basis of the multispecificity is not understood. In Escherichia coli the protein, BirA, can self-associate to a homo-dimer or form a hetero-dimer with the biotin carboxyl carrier protein of the biotin-dependent carboxylase, acetyl CoA carboxylase. The first interaction results in binding of BirA to the biotin operator sequence to repress transcription initiation at the biotin biosynthetic operon and the second is a prerequisite to post-translational biotin addition to the carrier protein for use in metabolism. A single surface on BirA is used for both interactions and previous studies indicate that, despite the structural differences between the alternative partners, the two dimerization reactions are isoenergetic. In this work the underlying thermodynamic driving forces and the sequence determinants of the two interactions were investigated in order to elucidate the energetic and structural underpinnings of the dual specificity. Combined measurements of the temperature and salt dependencies of hetero-dimerization indicate a modest unfavorable enthalpy and no dependence on salt concentration. By contrast, homo-dimerization is characterized by a very large unfavorable enthalpy and a modest dependence on salt concentration. Measurements of the function of BirA variants with single amino acid replacements in the alternative dimerization reactions indicate that although considerable overlap in structural determinants for both interactions exists, hotspots specific for one but not the other were detected. PMID:19361526

  6. An expanded genetic code in Candida albicans to study protein-protein interactions in vivo.

    PubMed

    Palzer, Silke; Bantel, Yannick; Kazenwadel, Franziska; Berg, Michael; Rupp, Steffen; Sohn, Kai

    2013-06-01

    For novel insights into the pathogenicity of Candida albicans, studies on molecular interactions of central virulence factors are crucial. Since methods for the analysis of direct molecular interactions of proteins in vivo are scarce, we expanded the genetic code of C. albicans with the synthetic photo-cross-linking amino acid p-azido-L-phenylalanine (AzF). Interacting molecules in close proximity of this unnatural amino acid can be covalently linked by UV-induced photo-cross-link, which makes unknown interacting molecules available for downstream identification. Therefore, we applied an aminoacyl-tRNA synthetase and a suppressor tRNA pair (EcTyrtRNA(CUA)) derived from Escherichia coli, which was previously reported to be orthogonal in Saccharomyces cerevisiae. We further optimized the aminoacyl-tRNA synthetase for AzF (AzF-RS) and EcTyrtRNA(CUA) for C. albicans and identified one AzF-RS with highest charging efficiency. Accordingly, incorporation of AzF into selected model proteins such as Tsa1p or Tup1p could be considerably enhanced. Immunologic detection of C-terminally tagged Tsa1p and Tup1p upon UV irradiation in a strain background containing suppressor tRNA and optimized AzF-RS revealed not only the mutant monomeric forms of these proteins but also higher-molecular-weight complexes, strictly depending on the specific position of incorporated AzF and UV excitation. By Western blotting and tandem mass spectrometry, we could identify these higher-molecular-weight complexes as homodimers consisting of one mutant monomer and a differently tagged, wild-type version of Tsa1p or Tup1p, respectively, demonstrating that expanding the genetic code of C. albicans with the unnatural photo-cross-linker amino acid AzF and applying it for in vivo binary protein interaction analyses is feasible. PMID:23543672

  7. The role of residue stability in transient protein-protein interactions involved in enzymatic phosphate hydrolysis. A computational study.

    PubMed

    Bonet, Jaume; Caltabiano, Gianluigi; Khan, Abdul Kareem; Johnston, Michael A; Corbí, Carles; Gómez, Alex; Rovira, Xavier; Teyra, Joan; Villà-Freixa, Jordi

    2006-04-01

    Finding why protein-protein interactions (PPIs) are so specific can provide a valuable tool in a variety of fields. Statistical surveys of so-called transient complexes (like those relevant for signal transduction mechanisms) have shown a tendency of polar residues to participate in the interaction region. Following this scheme, residues in the unbound partners have to compete between interacting with water or interacting with other residues of the protein. On the other hand, several works have shown that the notion of active site electrostatic preorganization can be used to interpret the high efficiency in enzyme reactions. This preorganization can be related to the instability of the residues important for catalysis. In some enzymes, in addition, conformational changes upon binding to other proteins lead to an increase in the activity of the enzymatic partner. In this article the linear response approximation version of the semimacroscopic protein dipoles Langevin dipoles (PDLD/S-LRA) model is used to evaluate the stability of several residues in two phosphate hydrolysis enzymes upon complexation with their activating partners. In particular, the residues relevant for PPI and for phosphate hydrolysis in the CDK2/Cyclin A and Ras/GAP complexes are analyzed. We find that the evaluation of the stability of residues in these systems can be used to identify not only active site regions but it can also be used as a guide to locate "hot spots" for PPIs. We also show that conformational changes play a major role in positioning interfacing residues in a proper "energetic" orientation, ready to interact with the residues in the partner protein surface. Thus, we extend the preorganization theory to PPIs, extrapolating the results we obtained from the above-mentioned complexes to a more general case. We conclude that the correlation between stability of a residue in the surface and the likelihood that it participates in the interaction can be a general fact for transient

  8. Identification of protein-protein interactions of isoflavonoid biosynthetic enzymes with 2-hydroxyisoflavanone synthase in soybean (Glycine max (L.) Merr.).

    PubMed

    Waki, Toshiyuki; Yoo, DongChan; Fujino, Naoto; Mameda, Ryo; Denessiouk, Konstantin; Yamashita, Satoshi; Motohashi, Reiko; Akashi, Tomoyoshi; Aoki, Toshio; Ayabe, Shin-ichi; Takahashi, Seiji; Nakayama, Toru

    2016-01-15

    Metabolic enzymes, including those involved in flavonoid biosynthesis, are proposed to form weakly bound, ordered protein complexes, called "metabolons". Some hypothetical models of flavonoid biosynthetic metabolons have been proposed, in which metabolic enzymes are believed to anchor to the cytoplasmic surface of the endoplasmic reticulum (ER) via ER-bound cytochrome P450 isozymes (P450s). However, no convincing evidence for the interaction of flavonoid biosynthetic enzymes with P450s has been reported previously. Here, we analyzed binary protein-protein interactions of 2-hydroxyisoflavanone synthase 1 (GmIFS1), a P450 (CYP93C), with cytoplasmic enzymes involved in isoflavone biosynthesis in soybean. We identified binary interactions between GmIFS1 and chalcone synthase 1 (GmCHS1) and between GmIFS1 and chalcone isomerases (GmCHIs) by using a split-ubiquitin membrane yeast two-hybrid system. These binary interactions were confirmed in planta by means of bimolecular fluorescence complementation (BiFC) using tobacco leaf cells. In these BiFC analyses, fluorescence signals that arose from the interaction of these cytoplasmic enzymes with GmIFS1 generated sharp, network-like intracellular patterns, which was very similar to the ER-localized fluorescence patterns of GmIFS1 labeled with a fluorescent protein. These observations provide strong evidence that, in planta, interaction of GmCHS1 and GmCHIs with GmIFS1 takes place on ER on which GmIFS1 is located, and also provide important clues to understand how enzymes and proteins form metabolons to establish efficient metabolic flux of (iso)flavonoid biosynthesis. PMID:26694697

  9. Exploring of protein - protein interactions at the solid - aqueous interface by means of contact angle measurements.

    PubMed

    Grabowska, I; Dehaen, W; Radecka, H; Radecki, J

    2016-05-01

    In this article we present the results of the studies on interactions between the VC1 domain of the Receptor for Advanced Glycation End Products (RAGE) and its ligand, the S100B protein, performed by contact angle measurements. Histidine-tagged (His6) VC1-RAGE domain was covalently bonded to Cu(II) or Ni(II) complexes with dipyrromethene (DPM) self-assembled on gold surface. The method based on the theory of van Oss was used for the purpose of determining the Lifshitz-van der Waals (γ(LW)) component as well as the electron acceptor-electron donor (the Lewis acid-base, γ(+)-γ(-)) parameters of the VC1-RAGE-S100B complex. Moreover, the surface free energies of the interactions between the VC1 domain attached to the surface and the ligand present in the aqueous phase were determined. The specificity of the VC1- RAGE interactions with the ligand studied was also proved. PMID:26918510

  10. Protein-protein interactions in a higher-order structure direct lambda site-specific recombination.

    PubMed

    Thompson, J F; de Vargas, L M; Skinner, S E; Landy, A

    1987-06-01

    The highly directional site-specific recombination of bacteriophage lambda is tightly regulated by the binding of three different proteins to a complex array of sites. The manner in which these reactions are both stimulated and inhibited by co-operative binding of proteins to specific sites on the P arm of attP and AttR has been elucidated by correlation of nuclease protection with recombination studies of both wild-type and mutant DNAs. In addition to co-operative forces, there is a specific competitive interaction that allows the protein-DNA complex to serve as a "biological switch". This switch does not depend upon the simple occlusion of DNA binding sites by neighboring proteins; but, rather, the outcome of this competition is dependent on long-range interactions that vary between the higher-order structures of attP and attR. These higher-order structures are dependent on cooperative interactions involving three proteins binding to five or more sites. PMID:2958633

  11. NanoBRET--A Novel BRET Platform for the Analysis of Protein-Protein Interactions.

    PubMed

    Machleidt, Thomas; Woodroofe, Carolyn C; Schwinn, Marie K; Méndez, Jacqui; Robers, Matthew B; Zimmerman, Kris; Otto, Paul; Daniels, Danette L; Kirkland, Thomas A; Wood, Keith V

    2015-08-21

    Dynamic interactions between proteins comprise a key mechanism for temporal control of cellular function and thus hold promise for development of novel drug therapies. It remains technically challenging, however, to quantitatively characterize these interactions within the biologically relevant context of living cells. Although, bioluminescence resonance energy transfer (BRET) has often been used for this purpose, its general applicability has been hindered by limited sensitivity and dynamic range. We have addressed this by combining an extremely bright luciferase (Nanoluc) with a means for tagging intracellular proteins with a long-wavelength fluorophore (HaloTag). The small size (19 kDa), high emission intensity, and relatively narrow spectrum (460 nm peak intensity) make Nanoluc luciferase well suited as an energy donor. By selecting an efficient red-emitting fluorophore (635 nm peak intensity) for attachment onto the HaloTag, an overall spectral separation exceeding 175 nm was achieved. This combination of greater light intensity with improved spectral resolution results in substantially increased detection sensitivity and dynamic range over current BRET technologies. Enhanced performance is demonstrated using several established model systems, as well as the ability to image BRET in individual cells. The capabilities are further exhibited in a novel assay developed for analyzing the interactions of bromodomain proteins with chromatin in living cells. PMID:26006698

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

    PubMed

    Almeida, Rui M; Dell'Acqua, Simone; Krippahl, Ludwig; Moura, José J G; Pauleta, Sofia R

    2016-01-01

    The importance of understanding interactomes makes preeminent the study of protein interactions and protein complexes. Traditionally, protein interactions have been elucidated by experimental methods or, with lower impact, by simulation with protein docking algorithms. This article describes features and applications of the BiGGER docking algorithm, which stands at the interface of these two approaches. BiGGER is a user-friendly docking algorithm that was specifically designed to incorporate experimental data at different stages of the simulation, to either guide the search for correct structures or help evaluate the results, in order to combine the reliability of hard data with the convenience of simulations. Herein, the applications of BiGGER are described by illustrative applications divided in three Case Studies: (Case Study A) in which no specific contact data is available; (Case Study B) when different experimental data (e.g., site-directed mutagenesis, properties of the complex, NMR chemical shift perturbation mapping, electron tunneling) on one of the partners is available; and (Case Study C) when experimental data are available for both interacting surfaces, which are used during the search and/or evaluation stage of the docking. This algorithm has been extensively used, evidencing its usefulness in a wide range of different biological research fields. PMID:27517887

  13. [Monitoring the Redox States of Thioredoxin in Protein-Protein Interaction Using Intrinsic Fluorescence Probe].

    PubMed

    Wang, Pan; Guo, Ai-yu; Chang, Guan-xiao; Ran, Xia; Zhang, Yu; Guo, Li-jun

    2015-10-01

    The cellular redox states directly affect cell proliferation, differentiation and apoptosis, and the redox states changes is particularly important to the regulation of cell survival or death. Thioredoxin is a kind of oxidation regulatory protein which is widely exists in organisms, and the change of redox states is also an important process in redox regulation. In this work, we have used the site-directed mutagenesis of protein, SDS-polyacrylamide gel electrophoresis fluorescence spectroscopy and circular dichroism etc., to investigate redox states changes between TRX (E. coli) and glutathione peroxidase(GPX3) during their interaction. By observing the fluorescence spectra of TRX and its mutants, we have studied the protein interactions as well as the redox states switching between oxidation state TRX and the reduced state GPX3. The results demonstrate the presence of interactions and electron exchanges occurring between reduced GPX3 and oxidized TRX, which is of significance for revealing the physical and chemical mechanism of TRX in intracellular signal transduction. PMID:26904821

  14. Mapping Ultra-weak Protein-Protein Interactions between Heme Transporters of Staphylococcus aureus

    PubMed Central

    Abe, Ryota; Caaveiro, Jose M. M.; Kozuka-Hata, Hiroko; Oyama, Masaaki; Tsumoto, Kouhei

    2012-01-01

    Iron is an essential nutrient for the proliferation of Staphylococcus aureus during bacterial infections. The iron-regulated surface determinant (Isd) system of S. aureus transports and metabolizes iron porphyrin (heme) captured from the host organism. Transportation of heme across the thick cell wall of this bacterium requires multiple relay points. The mechanism by which heme is physically transferred between Isd transporters is largely unknown because of the transient nature of the interactions involved. Herein, we show that the IsdC transporter not only passes heme ligand to another class of Isd transporter, as previously known, but can also perform self-transfer reactions. IsdA shows a similar ability. A genetically encoded photoreactive probe was used to survey the regions of IsdC involved in self-dimerization. We propose an updated model that explicitly considers self-transfer reactions to explain heme delivery across the cell wall. An analogous photo-cross-linking strategy was employed to map transient interactions between IsdC and IsdE transporters. These experiments identified a key structural element involved in the rapid and specific transfer of heme from IsdC to IsdE. The resulting structural model was validated with a chimeric version of the homologous transporter IsdA. Overall, our results show that the ultra-weak interactions between Isd transporters are governed by bona fide protein structural motifs. PMID:22427659

  15. Investigating CFTR and KCa3.1 Protein/Protein Interactions

    PubMed Central

    Trinh, Nguyen Thu Ngan; Luo, Yishan; Wiseman, Paul W.; Hanrahan, John W.; Brochiero, Emmanuelle; Sauvé, Rémy

    2016-01-01

    In epithelia, Cl- channels play a prominent role in fluid and electrolyte transport. Of particular importance is the cAMP-dependent cystic fibrosis transmembrane conductance regulator Cl- channel (CFTR) with mutations of the CFTR encoding gene causing cystic fibrosis. The bulk transepithelial transport of Cl- ions and electrolytes needs however to be coupled to an increase in K+ conductance in order to recycle K+ and maintain an electrical driving force for anion exit across the apical membrane. In several epithelia, this K+ efflux is ensured by K+ channels, including KCa3.1, which is expressed at both the apical and basolateral membranes. We show here for the first time that CFTR and KCa3.1 can physically interact. We first performed a two-hybrid screen to identify which KCa3.1 cytosolic domains might mediate an interaction with CFTR. Our results showed that both the N-terminal fragment M1-M40 of KCa3.1 and part of the KCa3.1 calmodulin binding domain (residues L345-A400) interact with the NBD2 segment (G1237-Y1420) and C- region of CFTR (residues T1387-L1480), respectively. An association of CFTR and F508del-CFTR with KCa3.1 was further confirmed in co-immunoprecipitation experiments demonstrating the formation of immunoprecipitable CFTR/KCa3.1 complexes in CFBE cells. Co-expression of KCa3.1 and CFTR in HEK cells did not impact CFTR expression at the cell surface, and KCa3.1 trafficking appeared independent of CFTR stimulation. Finally, evidence is presented through cross-correlation spectroscopy measurements that KCa3.1 and CFTR colocalize at the plasma membrane and that KCa3.1 channels tend to aggregate consequent to an enhanced interaction with CFTR channels at the plasma membrane following an increase in intracellular Ca2+ concentration. Altogether, these results suggest 1) that the physical interaction KCa3.1/CFTR can occur early during the biogenesis of both proteins and 2) that KCa3.1 and CFTR form a dynamic complex, the formation of which depends on

  16. Investigating CFTR and KCa3.1 Protein/Protein Interactions.

    PubMed

    Klein, Hélène; Abu-Arish, Asmahan; Trinh, Nguyen Thu Ngan; Luo, Yishan; Wiseman, Paul W; Hanrahan, John W; Brochiero, Emmanuelle; Sauvé, Rémy

    2016-01-01

    In epithelia, Cl- channels play a prominent role in fluid and electrolyte transport. Of particular importance is the cAMP-dependent cystic fibrosis transmembrane conductance regulator Cl- channel (CFTR) with mutations of the CFTR encoding gene causing cystic fibrosis. The bulk transepithelial transport of Cl- ions and electrolytes needs however to be coupled to an increase in K+ conductance in order to recycle K+ and maintain an electrical driving force for anion exit across the apical membrane. In several epithelia, this K+ efflux is ensured by K+ channels, including KCa3.1, which is expressed at both the apical and basolateral membranes. We show here for the first time that CFTR and KCa3.1 can physically interact. We first performed a two-hybrid screen to identify which KCa3.1 cytosolic domains might mediate an interaction with CFTR. Our results showed that both the N-terminal fragment M1-M40 of KCa3.1 and part of the KCa3.1 calmodulin binding domain (residues L345-A400) interact with the NBD2 segment (G1237-Y1420) and C- region of CFTR (residues T1387-L1480), respectively. An association of CFTR and F508del-CFTR with KCa3.1 was further confirmed in co-immunoprecipitation experiments demonstrating the formation of immunoprecipitable CFTR/KCa3.1 complexes in CFBE cells. Co-expression of KCa3.1 and CFTR in HEK cells did not impact CFTR expression at the cell surface, and KCa3.1 trafficking appeared independent of CFTR stimulation. Finally, evidence is presented through cross-correlation spectroscopy measurements that KCa3.1 and CFTR colocalize at the plasma membrane and that KCa3.1 channels tend to aggregate consequent to an enhanced interaction with CFTR channels at the plasma membrane following an increase in intracellular Ca2+ concentration. Altogether, these results suggest 1) that the physical interaction KCa3.1/CFTR can occur early during the biogenesis of both proteins and 2) that KCa3.1 and CFTR form a dynamic complex, the formation of which depends on

  17. Rational Design, Synthesis and Evaluation of Coumarin Derivatives as Protein-protein Interaction Inhibitors.

    PubMed

    De Luca, Laura; Agharbaoui, Fatima E; Gitto, Rosaria; Buemi, Maria Rosa; Christ, Frauke; Debyser, Zeger; Ferro, Stefania

    2016-09-01

    Herein we describe the design and synthesis of a new series of coumarin derivatives searching for novel HIV-1 integrase (IN) allosteric inhibitors. All new obtained compounds were tested in order to evaluate their ability to inhibit the interaction between the HIV-1 IN enzyme and the nuclear protein lens epithelium growth factor LEDGF/p75. A combined approach of docking and molecular dynamic simulations has been applied to clarify the activity of the new compounds. Specifically, the binding free energies by using the method of molecular mechanics-generalized Born surface area (MM-GBSA) was calculated, whereas hydrogen bond occupancies were monitored throughout simulations methods. PMID:27546050

  18. Mapping protein-protein interactions with phage-displayed combinatorial peptide libraries.

    SciTech Connect

    Kay, B. K.; Castagnoli, L.; Biosciences Division; Univ. of Rome

    2003-01-01

    This unit describes the process and analysis of affinity selecting bacteriophage M13 from libraries displaying combinatorial peptides fused to either a minor or major capsid protein. Direct affinity selection uses target protein bound to a microtiter plate followed by purification of selected phage by ELISA. Alternatively, there is a bead-based affinity selection method. These methods allow one to readily isolate peptide ligands that bind to a protein target of interest and use the consensus sequence to search proteomic databases for putative interacting proteins.

  19. Probing the Small-Molecule Inhibition of an Anticancer Therapeutic Protein-Protein Interaction Using a Solid-State Nanopore.

    PubMed

    Kwak, Dong-Kyu; Chae, Hongsik; Lee, Mi-Kyung; Ha, Ji-Hyang; Goyal, Gaurav; Kim, Min Jun; Kim, Ki-Bum; Chi, Seung-Wook

    2016-05-01

    Nanopore sensing is an emerging technology for the single-molecule-based detection of various biomolecules. In this study, we probed the anticancer therapeutic p53 transactivation domain (p53TAD)/MDM2 interaction and its inhibition with a small-molecule MDM2 antagonist, Nutlin-3, using low-noise solid-state nanopores. Although the translocation of positively charged MDM2 through a nanopore was detected at the applied negative voltage, this MDM2 translocation was almost completely blocked upon formation of the MDM2/GST-p53TAD complex owing to charge conversion. In combination with NMR data, the nanopore measurements showed that the addition of Nutlin-3 rescued MDM2 translocation, indicating that Nutlin-3 disrupted the MDM2/GST-p53TAD complex, thereby releasing MDM2. Taken together, our results reveal that solid-state nanopores can be a valuable platform for the ultrasensitive, picomole-scale screening of small-molecule drugs against protein-protein interaction (PPI) targets. PMID:27038437

  20. Identification of a novel contactin-associated transmembrane receptor with multiple domains implicated in protein-protein interactions.

    PubMed Central

    Peles, E; Nativ, M; Lustig, M; Grumet, M; Schilling, J; Martinez, R; Plowman, G D; Schlessinger, J

    1997-01-01

    Receptor protein tyrosine phosphatase beta (RPTPbeta) expressed on the surface of glial cells binds to the glycosylphosphatidylinositol (GPI)-anchored recognition molecule contactin on neuronal cells leading to neurite outgrowth. We describe the cloning of a novel contactin-associated transmembrane receptor (p190/Caspr) containing a mosaic of domains implicated in protein-protein interactions. The extracellular domain of Caspr contains a neurophilin/coagulation factor homology domain, a region related to fibrinogen beta/gamma, epidermal growth factor-like repeats, neurexin motifs as well as unique PGY repeats found in a molluscan adhesive protein. The cytoplasmic domain of Caspr contains a proline-rich sequence capable of binding to a subclass of SH3 domains of signaling molecules. Caspr and contactin exist as a complex in rat brain and are bound to each other by means of lateral (cis) interactions in the plasma membrane. We propose that Caspr may function as a signaling component of contactin, enabling recruitment and activation of intracellular signaling pathways in neurons. The binding of RPTPbeta to the contactin-Caspr complex could provide a mechanism for cell-cell communication between glial cells and neurons during development. PMID:9118959

  1. Revisiting topological properties and models of protein-protein interaction networks from the perspective of dataset evolution.

    PubMed

    Shao, Mingyu; Zhou, Shuigeng; Guan, Jihong

    2015-08-01

    Protein-protein interaction (PPI) networks are crucial for organisms. Many research efforts have thus been devoted to the study on the topological properties and models of PPI networks. However, existing studies did not always report consistent results on the topological properties of PPI networks. Although a number of PPI network models have been introduced, yet in the literature there is no convincing conclusion on which model is best for describing PPI networks. This situation is primarily caused by the incompleteness of current PPI datasets. To solve this problem, in this study, the authors propose to revisit the topological properties and models of PPI networks from the perspective of PPI dataset evolution. Concretely, they used 12 PPI datasets of Arabidopsis thaliana and 10 PPI datasets of Saccharomyces cerevisiae from different Biological General Repository for Interaction Datasets (BioGRID) database versions, and compared the topological properties of these datasets and the fitting capabilities of five typical PPI network models over these datasets. PMID:26243826

  2. Protein-protein interaction network construction for cancer using a new L1/2-penalized Net-SVM model.

    PubMed

    Chai, H; Huang, H H; Jiang, H K; Liang, Y; Xia, L Y

    2016-01-01

    Identifying biomarker genes and characterizing interaction pathways with high-dimensional and low-sample size microarray data is a major challenge in computational biology. In this field, the construction of protein-protein interaction (PPI) networks using disease-related selected genes has garnered much attention. Support vector machines (SVMs) are commonly used to classify patients, and a number of useful tools such as lasso, elastic net, SCAD, or other regularization methods can be combined with SVM models to select genes that are related to a disease. In the current study, we propose a new Net-SVM model that is different from other SVM models as it is combined with L1/2-norm regularization, which has good performance with high-dimensional and low-sample size microarray data for cancer classification, gene selection, and PPI network construction. Both simulation studies and real data experiments demonstrated that our proposed method outperformed other regularization methods such as lasso, SCAD, and elastic net. In conclusion, our model may help to select fewer but more relevant genes, and can be used to construct simple and informative PPI networks that are highly relevant to cancer. PMID:27525863

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

    PubMed

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

    2016-04-19

    Small-molecule modulation of protein-protein interactions (PPIs) is one of the most promising new areas in drug discovery. In the vast majority of cases only inhibition or disruption of PPIs is realized, whereas the complementary strategy of targeted stabilization of PPIs is clearly under-represented. Here, we report the example of a semi-synthetic natural product derivative-ISIR-005-that stabilizes the cancer-relevant interaction of the adaptor protein 14-3-3 and Gab2. The crystal structure of ISIR-005 in complex with 14-3-3 and the binding motif of Gab2 comprising two phosphorylation sites (Gab2pS210pT391) showed how the stabilizing molecule binds to the rim-of-the-interface of the protein complex. Only in the direct vicinity of 14-3-3/Gab2pT391 site is a pre-formed pocket occupied by ISIR-005; binding of the Gab2pS210 motif to 14-3-3 does not create an interface pocket suitable for the molecule. Accordingly, ISIR-005 only stabilizes the binding of the Gab2pT391 but not the Gab2pS210 site. This study represents structural and biochemical proof of the druggability of the 14-3-3/Gab2 PPI interface with important implications for the development of PPI stabilizers. PMID:26644359

  4. Enabling systematic interrogation of protein-protein interactions in live cells with a versatile ultra-high-throughput biosensor platform.

    PubMed

    Mo, Xiu-Lei; Luo, Yin; Ivanov, Andrei A; Su, Rina; Havel, Jonathan J; Li, Zenggang; Khuri, Fadlo R; Du, Yuhong; Fu, Haian

    2016-06-01

    Large-scale genomics studies have generated vast resources for in-depth understanding of vital biological and pathological processes. A rising challenge is to leverage such enormous information to rapidly decipher the intricate protein-protein interactions (PPIs) for functional characterization and therapeutic interventions. While a number of powerful technologies have been employed to detect PPIs, a singular PPI biosensor platform with both high sensitivity and robustness in a mammalian cell environment remains to be established. Here we describe the development and integration of a highly sensitive NanoLuc luciferase-based bioluminescence resonance energy transfer technology, termed BRET(n), which enables ultra-high-throughput (uHTS) PPI detection in live cells with streamlined co-expression of biosensors in a miniaturized format. We further demonstrate the application of BRET(n) in uHTS format in chemical biology research, including the discovery of chemical probes that disrupt PRAS40 dimerization and pathway connectivity profiling among core members of the Hippo signaling pathway. Such hippo pathway profiling not only confirmed previously reported PPIs, but also revealed two novel interactions, suggesting new mechanisms for regulation of Hippo signaling. Our BRET(n) biosensor platform with uHTS capability is expected to accelerate systematic PPI network mapping and PPI modulator-based drug discovery. PMID:26578655

  5. InterEvDock: a docking server to predict the structure of protein-protein interactions using evolutionary information.

    PubMed

    Yu, Jinchao; Vavrusa, Marek; Andreani, Jessica; Rey, Julien; Tufféry, Pierre; Guerois, Raphaël

    2016-07-01

    The structural modeling of protein-protein interactions is key in understanding how cell machineries cross-talk with each other. Molecular docking simulations provide efficient means to explore how two unbound protein structures interact. InterEvDock is a server for protein docking based on a free rigid-body docking strategy. A systematic rigid-body docking search is performed using the FRODOCK program and the resulting models are re-scored with InterEvScore and SOAP-PP statistical potentials. The InterEvScore potential was specifically designed to integrate co-evolutionary information in the docking process. InterEvDock server is thus particularly well suited in case homologous sequences are available for both binding partners. The server returns 10 structures of the most likely consensus models together with 10 predicted residues most likely involved in the interface. In 91% of all complexes tested in the benchmark, at least one residue out of the 10 predicted is involved in the interface, providing useful guidelines for mutagenesis. InterEvDock is able to identify a correct model among the top10 models for 49% of the rigid-body cases with evolutionary information, making it a unique and efficient tool to explore structural interactomes under an evolutionary perspective. The InterEvDock web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock/. PMID:27131368

  6. All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning

    PubMed Central

    Airola, Antti; Pyysalo, Sampo; Björne, Jari; Pahikkala, Tapio; Ginter, Filip; Salakoski, Tapio

    2008-01-01

    Background Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure. Results We evaluate the proposed method on five publicly available PPI corpora, providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. We additionally perform a detailed evaluation of the effects of training and testing on different resources, providing insight into the challenges involved in applying a system beyond the data it was trained on. Our method is shown to achieve state-of-the-art performance with respect to comparable evaluations, with 56.4 F-score and 84.8 AUC on the AImed corpus. Conclusion We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided. PMID:19025688

  7. Small-molecule inhibitors that target protein-protein interactions in the RAD51 family of recombinases.

    PubMed

    Scott, Duncan E; Coyne, Anthony G; Venkitaraman, Ashok; Blundell, Tom L; Abell, Chris; Hyvönen, Marko

    2015-02-01

    The development of small molecules that inhibit protein-protein interactions continues to be a challenge in chemical biology and drug discovery. Herein we report the development of indole-based fragments that bind in a shallow surface pocket of a humanised surrogate of RAD51. RAD51 is an ATP-dependent recombinase that plays a key role in the repair of double-strand DNA breaks. It both self-associates, forming filament structures with DNA, and interacts with the BRCA2 protein through a common "FxxA" tetrapeptide motif. We elaborated previously identified fragment hits that target the FxxA motif site and developed small-molecule inhibitors that are approximately 500-fold more potent than the initial fragments. The lead compounds were shown to compete with the BRCA2-derived Ac-FHTA-NH2 peptide and the self-association peptide of RAD51, but they had no effect on ATP binding. This study is the first reported elaboration of small-molecular-weight fragments against this challenging target. PMID:25470112

  8. Characterization of Ca2+-Dependent Protein-Protein Interactions within the Ca2+ Release Units of Cardiac Sarcoplasmic Reticulum

    PubMed Central

    Rani, Shilpa; Park, Chang Sik; Sreenivasaiah, Pradeep Kumar; Kim, Do Han

    2016-01-01

    In the heart, excitation-contraction (E-C) coupling is mediated by Ca2+ release from sarcoplasmic reticulum (SR) through the interactions of proteins forming the Ca2+ release unit (CRU). Among them, calsequestrin (CSQ) and histidine-rich Ca2+ binding protein (HRC) are known to bind the charged luminal region of triadin (TRN) and thus directly or indirectly regulate ryanodine receptor 2 (RyR2) activity. However, the mechanisms of CSQ and HRC mediated regulation of RyR2 activity through TRN have remained unclear. We first examined the minimal KEKE motif of TRN involved in the interactions with CSQ2, HRC and RyR2 using TRN deletion mutants and in vitro binding assays. The results showed that CSQ2, HRC and RyR2 share the same KEKE motif region on the distal part of TRN (aa 202–231). Second, in vitro binding assays were conducted to examine the Ca2+ dependence of protein-protein interactions (PPI). The results showed that TRN-HRC interaction had a bell-shaped Ca2+ dependence, which peaked at pCa4, whereas TRN-CSQ2 or TRN-RyR2 interaction did not show such Ca2+ dependence pattern. Third, competitive binding was conducted to examine whether CSQ2, HRC, or RyR2 affects the TRN-HRC or TRN-CSQ2 binding at pCa4. Among them, only CSQ2 or RyR2 competitively inhibited TRN-HRC binding, suggesting that HRC can confer functional refractoriness to CRU, which could be beneficial for reloading of Ca2+ into SR at intermediate Ca2+ concentrations. PMID:26674963

  9. An integrative in silico approach for discovering candidates for drug-targetable protein-protein interactions in interactome data

    PubMed Central

    Sugaya, Nobuyoshi; Ikeda, Kazuyoshi; Tashiro, Toshiyuki; Takeda, Shizu; Otomo, Jun; Ishida, Yoshiko; Shiratori, Akiko; Toyoda, Atsushi; Noguchi, Hideki; Takeda, Tadayuki; Kuhara, Satoru; Sakaki, Yoshiyuki; Iwayanagi, Takao

    2007-01-01

    Background Protein-protein interactions (PPIs) are challenging but attractive targets for small chemical drugs. Whole PPIs, called the 'interactome', have been emerged in several organisms, including human, based on the recent development of high-throughput screening (HTS) technologies. Individual PPIs have been targeted by small drug-like chemicals (SDCs), however, interactome data have not been fully utilized for exploring drug targets due to the lack of comprehensive methodology for utilizing these data. Here we propose an integrative in silico approach for discovering candidates for drug-targetable PPIs in interactome data. Results Our novel in silico screening system comprises three independent assessment procedures: i) detection of protein domains responsible for PPIs, ii) finding SDC-binding pockets on protein surfaces, and iii) evaluating similarities in the assignment of Gene Ontology (GO) terms between specific partner proteins. We discovered six candidates for drug-targetable PPIs by applying our in silico approach to original human PPI data composed of 770 binary interactions produced by our HTS yeast two-hybrid (HTS-Y2H) assays. Among them, we further examined two candidates, RXRA/NRIP1 and CDK2/CDKN1A, with respect to their biological roles, PPI network around each candidate, and tertiary structures of the interacting domains. Conclusion An integrative in silico approach for discovering candidates for drug-targetable PPIs was applied to original human PPIs data. The system excludes false positive interactions and selects reliable PPIs as drug targets. Its effectiveness was demonstrated by the discovery of the six promising candidate target PPIs. Inhibition or stabilization of the two interactions may have potential therapeutic effects against human diseases. PMID:17705877

  10. Brownian dynamics study of the influences of electrostatic interaction and diffusion on protein-protein association kinetics.

    PubMed Central

    Zhou, H X

    1993-01-01

    A unified model is presented for protein-protein association processes that are under the influences of electrostatic interaction and diffusion (e.g., protein oligomerization, enzyme catalysis, electron and energy transfer). The proteins are modeled as spheres that bear point charges and undergo translational and rotational Brownian motion. Before association can occur the two spheres have to be aligned properly to form a reaction complex via diffusion. The reaction complex can either go on to form the product or it can dissociate into the separate reactants through diffusion. The electrostatic interaction, like diffusion, influences every step except the one that brings the reaction complex into the product. The interaction potential is obtained by extending the Kirkwood-Tanford protein model (Tanford, C., and J. G. Kirkwood. 1957. J. Am. Chem. Soc. 79:5333-5339) to two charge-embedded spheres and solving the consequent equations under a particular basis set. The time-dependent association rate coefficient is then obtained through Brownian dynamics simulations according an algorithm developed earlier (Zhou, H.-X. 1990. J. Phys. Chem. 94:8794-8800). This method is applied to a model system of the cytochrome c and cytochrome c peroxidase association process and the results confirm the experimental dependence of the association rate constant on the solution ionic strength. An important conclusion drawn from this study is that when the product is formed by very specific alignment of the reactants, as is often the case, the effect of the interaction potential is simply to scale the association rate constant by a Boltzmann factor. This explains why mutations in the interface of the reaction complex have strong influences on the association rate constant whereas those away from the interface have minimal effects. It comes about because the former mutations change the interaction potential of the reaction complex significantly and the latter ones do not. PMID:8396447

  11. Thioflavin S (NSC71948) interferes with Bcl-2-associated athanogene (BAG-1)-mediated protein-protein interactions.

    PubMed

    Sharp, Adam; Crabb, Simon J; Johnson, Peter W M; Hague, Angela; Cutress, Ramsey; Townsend, Paul A; Ganesan, A; Packham, Graham

    2009-11-01

    The C-terminal BAG domain is thought to play a key role in BAG-1-induced survival and proliferation by mediating protein-protein interactions, for example, with heat shock proteins HSC70 and HSP70, and with RAF-1 kinase. Here, we have identified thioflavin S (NSC71948) as a potential small-molecule chemical inhibitor of these interactions. NSC71948 inhibited the interaction of BAG-1 and HSC70 in vitro and decreased BAG-1:HSC70 and BAG-1:HSP70 binding in intact cells. NSC71948 also reduced binding between BAG-1 and RAF-1, but had no effect on the interaction between two unrelated proteins, BIM and MCL-1. NSC71948 functionally reversed the ability of BAG-1 to promote vitamin D3 receptor-mediated transactivation, an activity of BAG-1 that depends on HSC70/HSP70 binding, and reduced phosphorylation of p44/42 mitogen-activate protein kinase. NSC71948 can be used to stain amyloid fibrils; however, structurally related compounds, thioflavin T and BTA-1, had no effect on BAG-1:HSC70 binding, suggesting that structural features important for amyloid fibril binding and inhibition of BAG-1:HSC70 binding may be separable. We demonstrated that NSC71948 inhibited the growth of BAG-1 expressing human ZR-75-1 breast cancer cells and wild-type, but not BAG-1-deficient, mouse embryo fibroblasts. Taken together, these data suggest that NSC71948 may be a useful molecule to investigate the functional significance of BAG-1 C-terminal protein interactions. However, it is important to recognize that NSC71948 may exert additional "off-target" effects. Inhibition of BAG-1 function may be an attractive strategy to inhibit the growth of BAG-1-overexpressing cancers, and further screens of additional compound collections may be warranted. PMID:19690191

  12. Co-evolution analysis to predict protein-protein interactions within influenza virus envelope.

    PubMed

    Mintaev, Ramil R; Alexeevski, Andrei V; Kordyukova, Larisa V

    2014-04-01

    Interactions between integral membrane proteins hemagglutinin (HA), neuraminidase (NA), M2 and membrane-associated matrix protein M1 of influenza A virus are thought to be crucial for assembly of functionally competent virions. We hypothesized that the amino acid residues located at the interface of two different proteins are under physical constraints and thus probably co-evolve. To predict co-evolving residue pairs, the EvFold ( http://evfold.org ) program searching the (nontransitive) Direct Information scores was applied for large samplings of amino acid sequences from Influenza Research Database ( http://www.fludb.org/ ). Having focused on the HA, NA, and M2 cytoplasmic tails as well as C-terminal domain of M1 (being the less conserved among the protein domains) we captured six pairs of correlated positions. Among them, there were one, two, and three position pairs for HA-M2, HA-M1, and M2-M1 protein pairs, respectively. As expected, no co-varying positions were found for NA-HA, NA-M1, and NA-M2 pairs obviously due to high conservation of the NA cytoplasmic tail. The sum of frequencies calculated for two major amino acid patterns observed in pairs of correlated positions was up to 0.99 meaning their high to extreme evolutionary sustainability. Based on the predictions a hypothetical model of pair-wise protein interactions within the viral envelope was proposed. PMID:24712535

  13. Simulation of Coarse-Grained Protein-Protein Interactions with Graphics Processing Units.

    PubMed

    Tunbridge, Ian; Best, Robert B; Gain, James; Kuttel, Michelle M

    2010-11-01

    We report a hybrid parallel central and graphics processing units (CPU-GPU) implementation of a coarse-grained model for replica exchange Monte Carlo (REMC) simulations of protein assemblies. We describe the design, optimization, validation, and benchmarking of our algorithms, particularly the parallelization strategy, which is specific to the requirements of GPU hardware. Performance evaluation of our hybrid implementation shows scaled speedup as compared to a single-core CPU; reference simulations of small 100 residue proteins have a modest speedup of 4, while large simulations with thousands of residues are up to 1400 times faster. Importantly, the combination of coarse-grained models with highly parallel GPU hardware vastly increases the length- and time-scales accessible for protein simulation, making it possible to simulate much larger systems of interacting proteins than have previously been attempted. As a first step toward the simulation of the assembly of an entire viral capsid, we have demonstrated that the chosen coarse-grained model, together with REMC sampling, is capable of identifying the correctly bound structure, for a pair of fragments from the human hepatitis B virus capsid. Our parallel solution can easily be generalized to other interaction functions and other types of macromolecules and has implications for the parallelization of similar N-body problems that require random access lookups. PMID:26617104

  14. Weak protein-protein interactions revealed by immiscible filtration assisted by surface tension.

    PubMed

    Berry, Scott M; Chin, Emily N; Jackson, Shawn S; Strotman, Lindsay N; Goel, Mohit; Thompson, Nancy E; Alexander, Caroline M; Miyamoto, Shigeki; Burgess, Richard R; Beebe, David J

    2014-02-15

    Biological mechanisms are often mediated by transient interactions between multiple proteins. The isolation of intact protein complexes is essential to understanding biochemical processes and an important prerequisite for identifying new drug targets and biomarkers. However, low-affinity interactions are often difficult to detect. Here, we use a newly described method called immiscible filtration assisted by surface tension (IFAST) to isolate proteins under defined binding conditions. This method, which gives a near-instantaneous isolation, enables significantly higher recovery of transient complexes compared to current wash-based protocols, which require reequilibration at each of several wash steps, resulting in protein loss. The method moves proteins, or protein complexes, captured on a solid phase through one or more immiscible-phase barriers that efficiently exclude the passage of nonspecific material in a single operation. We use a previously described polyol-responsive monoclonal antibody to investigate the potential of this new method to study protein binding. In addition, difficult-to-isolate complexes involving the biologically and clinically important Wnt signaling pathway were isolated. We anticipate that this simple, rapid method to isolate intact, transient complexes will enable the discoveries of new signaling pathways, biomarkers, and drug targets. PMID:24215910

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

    NASA Astrophysics Data System (ADS)

    Ebel, Christine; Faou, Pierre; Zaccai, Giuseppe

    1999-01-01

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

  16. Weak protein-protein interactions revealed by immiscible filtration assisted by surface tension (IFAST)

    PubMed Central

    Berry, Scott M.; Chin, Emily N.; Jackson, Shawn S.; Strotman, Lindsay N.; Goel, Mohit; Thompson, Nancy E.; Alexander, Caroline M.; Miyamoto, Shigeki; Burgess, Richard R.; Beebe, David J.

    2013-01-01

    Biological mechanisms are often mediated by transient interactions between multiple proteins. The isolation of intact protein complexes is essential to understanding biochemical processes and an important prerequisite for identifying new drug targets and biomarkers. However, low-affinity interactions are often difficult to detect. Here, we use a newly described method called immiscible filtration assisted by surface tension (IFAST) to isolate proteins under defined binding conditions. This method, that gives a near-instantaneous isolation, enables significantly higher recovery of transient complexes as compared to current wash-based protocols, which require re-equilibration at each of several wash steps, resulting in protein loss. The method moves proteins, or protein complexes, captured on a solid phase through one or more immiscible phase barriers that efficiently exclude the passage of non-specific material in a single operation. We use a previously described polyol-responsive monoclonal antibody (PR-mAb) to investigate the potential of this new method to study protein-binding. In addition, difficult-to-isolate complexes involving the biologically and clinically important Wnt signaling pathway were isolated. We anticipate that this simple, rapid method to isolate intact, transient complexes will enable the discoveries of new signaling pathways, biomarkers, and drug targets. PMID:24215910

  17. Mapping protein-protein interactions with phage-displayed combinatorial peptide libraries and alanine scanning.

    PubMed

    Kokoszka, Malgorzata E; Kay, Brian K

    2015-01-01

    One avenue for inferring the function of a protein is to learn what proteins it may bind to in the cell. Among the various methodologies, one way for doing so is to affinity select peptide ligands from a phage-displayed combinatorial peptide library and then to examine if the proteins that carry such peptide sequences interact with the target protein in the cell. With the protocols described in this chapter, a laboratory with skills in microbiology, molecular biology, and protein biochemistry can readily identify peptides in the library that bind selectively, and with micromolar affinity, to a given target protein on the time scale of 2 months. To illustrate this approach, we use a library of bacteriophage M13 particles, which display 12-mer combinatorial peptides, to affinity select different peptide ligands for two different targets, the SH3 domain of the human Lyn protein tyrosine kinase and a segment of the yeast serine/threonine protein kinase Cbk1. The binding properties of the selected peptide ligands are then dissected by sequence alignment, Kunkel mutagenesis, and alanine scanning. Finally, the peptide ligands can be used to predict cellular interacting proteins and serve as the starting point for drug discovery. PMID:25616333

  18. MutaBind estimates and interprets the effects of sequence variants on protein-protein interactions.

    PubMed

    Li, Minghui; Simonetti, Franco L; Goncearenco, Alexander; Panchenko, Anna R

    2016-07-01

    Proteins engage in highly selective interactions with their macromolecular partners. Sequence variants that alter protein binding affinity may cause significant perturbations or complete abolishment of function, potentially leading to diseases. There exists a persistent need to develop a mechanistic understanding of impacts of variants on proteins. To address this need we introduce a new computational method MutaBind to evaluate the effects of sequence variants and disease mutations on protein interactions and calculate the quantitative changes in binding affinity. The MutaBind method uses molecular mechanics force fields, statistical potentials and fast side-chain optimization algorithms. The MutaBind server maps mutations on a structural protein complex, calculates the associated changes in binding affinity, determines the deleterious effect of a mutation, estimates the confidence of this prediction and produces a mutant structural model for download. MutaBind can be applied to a large number of problems, including determination of potential driver mutations in cancer and other diseases, elucidation of the effects of sequence variants on protein fitness in evolution and protein design. MutaBind is available at http://www.ncbi.nlm.nih.gov/projects/mutabind/. PMID:27150810

  19. Peptides interfering with protein-protein interactions in the ethylene signaling pathway delay tomato fruit ripening

    PubMed Central

    Bisson, Melanie M. A.; Kessenbrock, Mareike; Müller, Lena; Hofmann, Alexander; Schmitz, Florian; Cristescu, Simona M.; Groth, Georg

    2016-01-01

    The plant hormone ethylene is involved in the regulation of several processes with high importance for agricultural applications, e.g. ripening, aging and senescence. Previous work in our group has identified a small peptide (NOP-1) derived from the nuclear localization signal of the Arabidopsis ethylene regulator ETHYLENE INSENSITIVE-2 (EIN2) C-terminal part as efficient inhibitor of ethylene responses. Here, we show that NOP-1 is also able to efficiently disrupt EIN2-ETR1 complex formation in tomato, indicating that the NOP-1 inhibition mode is conserved across plant species. Surface application of NOP-1 on green tomato fruits delays ripening similar to known inhibitors of ethylene perception (MCP) and ethylene biosynthesis (AVG). Fruits treated with NOP-1 showed similar ethylene production as untreated controls underlining that NOP-1 blocks ethylene signaling by targeting an essential interaction in this pathway, while having no effect on ethylene biosynthesis. PMID:27477591

  20. SAFE Software and FED Database to Uncover Protein-Protein Interactions using Gene Fusion Analysis.

    PubMed

    Tsagrasoulis, Dimosthenis; Danos, Vasilis; Kissa, Maria; Trimpalis, Philip; Koumandou, V Lila; Karagouni, Amalia D; Tsakalidis, Athanasios; Kossida, Sophia

    2012-01-01

    Domain Fusion Analysis takes advantage of the fact that certain proteins in a given proteome A, are found to have statistically significant similarity with two separate proteins in another proteome B. In other words, the result of a fusion event between two separate proteins in proteome B is a specific full-length protein in proteome A. In such a case, it can be safely concluded that the protein pair has a common biological function or even interacts physically. In this paper, we present the Fusion Events Database (FED), a database for the maintenance and retrieval of fusion data both in prokaryotic and eukaryotic organisms and the Software for the Analysis of Fusion Events (SAFE), a computational platform implemented for the automated detection, filtering and visualization of fusion events (both available at: http://www.bioacademy.gr/bioinformatics/projects/ProteinFusion/index.htm). Finally, we analyze the proteomes of three microorganisms using these tools in order to demonstrate their functionality. PMID:22267904

  1. Peptides interfering with protein-protein interactions in the ethylene signaling pathway delay tomato fruit ripening.

    PubMed

    Bisson, Melanie M A; Kessenbrock, Mareike; Müller, Lena; Hofmann, Alexander; Schmitz, Florian; Cristescu, Simona M; Groth, Georg

    2016-01-01

    The plant hormone ethylene is involved in the regulation of several processes with high importance for agricultural applications, e.g. ripening, aging and senescence. Previous work in our group has identified a small peptide (NOP-1) derived from the nuclear localization signal of the Arabidopsis ethylene regulator ETHYLENE INSENSITIVE-2 (EIN2) C-terminal part as efficient inhibitor of ethylene responses. Here, we show that NOP-1 is also able to efficiently disrupt EIN2-ETR1 complex formation in tomato, indicating that the NOP-1 inhibition mode is conserved across plant species. Surface application of NOP-1 on green tomato fruits delays ripening similar to known inhibitors of ethylene perception (MCP) and ethylene biosynthesis (AVG). Fruits treated with NOP-1 showed similar ethylene production as untreated controls underlining that NOP-1 blocks ethylene signaling by targeting an essential interaction in this pathway, while having no effect on ethylene biosynthesis. PMID:27477591

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

    DOEpatents

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

    2002-10-15

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

  3. MicroRNA-Regulated Protein-Protein Interaction Networks and Their Functions in Breast Cancer

    PubMed Central

    Lee, Chia-Hsien; Kuo, Wen-Hong; Lin, Chen-Ching; Oyang, Yen-Jen; Huang, Hsuan-Cheng; Juan, Hsueh-Fen

    2013-01-01

    MicroRNAs, which are small endogenous RNA regulators, have been associated with various types of cancer. Breast cancer is a major health threat for women worldwide. Many miRNAs were reported to be associated with the progression and carcinogenesis of breast cancer. In this study, we aimed to discover novel breast cancer-related miRNAs and to elucidate their functions. First, we identified confident miRNA-target pairs by combining data from miRNA target prediction databases and expression profiles of miRNA and mRNA. Then, miRNA-regulated protein interaction networks (PINs) were constructed with confident pairs and known interaction data in the human protein reference database (HPRD). Finally, the functions of miRNA-regulated PINs were elucidated by functional enrichment analysis. From the results, we identified some previously reported breast cancer-related miRNAs and functions of the PINs, e.g., miR-125b, miR-125a, miR-21, and miR-497. Some novel miRNAs without known association to breast cancer were also found, and the putative functions of their PINs were also elucidated. These include miR-139 and miR-383. Furthermore, we validated our results by receiver operating characteristic (ROC) curve analysis using our miRNA expression profile data, gene expression-based outcome for breast cancer online (GOBO) survival analysis, and a literature search. Our results may provide new insights for research in breast cancer-associated miRNAs. PMID:23722663

  4. Mutagenesis Mapping of the Protein-Protein Interaction Underlying FusB-Type Fusidic Acid Resistance

    PubMed Central

    Cox, Georgina; Edwards, Thomas A.

    2013-01-01

    FusB-type proteins represent the predominant mechanism of resistance to fusidic acid in staphylococci and act by binding to and modulating the function of the drug target (elongation factor G [EF-G]). To gain further insight into this antibiotic resistance mechanism, we sought to identify residues important for the interaction of FusB with EF-G and thereby delineate the binding interface within the FusB–EF-G complex. Replacement with alanine of any one of four conserved residues within the C-terminal domain of FusB (F156, K184, Y187, and F208) abrogated the ability of the protein to confer resistance to fusidic acid; the purified mutant proteins also lost the ability to bind S. aureus EF-G in vitro. E. coli EF-G, which is not ordinarily able to bind FusB-type proteins, was rendered competent for binding to FusB following deletion of a 3-residue tract (529SNP531) from domain IV of the protein. This study has identified key regions of both FusB and EF-G that are important for the interaction between the proteins, findings which corroborate our previous in silico prediction for the architecture of the complex formed between the resistance protein and the drug target (G. Cox, G. S. Thompson, H. T. Jenkins, F. Peske, A. Savelsbergh, M. V. Rodnina, W. Wintermeyer, S. W. Homans, T. A. Edwards, and A. J. O'Neill, Proc. Natl. Acad. Sci. U. S. A. 109:2102-2107, 2012). PMID:23836182

  5. Mutagenesis mapping of the protein-protein interaction underlying FusB-type fusidic acid resistance.

    PubMed

    Cox, Georgina; Edwards, Thomas A; O'Neill, Alex J

    2013-10-01

    FusB-type proteins represent the predominant mechanism of resistance to fusidic acid in staphylococci and act by binding to and modulating the function of the drug target (elongation factor G [EF-G]). To gain further insight into this antibiotic resistance mechanism, we sought to identify residues important for the interaction of FusB with EF-G and thereby delineate the binding interface within the FusB-EF-G complex. Replacement with alanine of any one of four conserved residues within the C-terminal domain of FusB (F156, K184, Y187, and F208) abrogated the ability of the protein to confer resistance to fusidic acid; the purified mutant proteins also lost the ability to bind S. aureus EF-G in vitro. E. coli EF-G, which is not ordinarily able to bind FusB-type proteins, was rendered competent for binding to FusB following deletion of a 3-residue tract (529SNP531) from domain IV of the protein. This study has identified key regions of both FusB and EF-G that are important for the interaction between the proteins, findings which corroborate our previous in silico prediction for the architecture of the complex formed between the resistance protein and the drug target (G. Cox, G. S. Thompson, H. T. Jenkins, F. Peske, A. Savelsbergh, M. V. Rodnina, W. Wintermeyer, S. W. Homans, T. A. Edwards, and A. J. O'Neill, Proc. Natl. Acad. Sci. U. S. A. 109:2102-2107, 2012). PMID:23836182

  6. Assembly of the cysteine synthase complex and the regulatory role of protein-protein interactions.

    PubMed

    Kumaran, Sangaralingam; Yi, Hankuil; Krishnan, Hari B; Jez, Joseph M

    2009-04-10

    Macromolecular assemblies play critical roles in regulating cellular functions. The cysteine synthase complex (CSC), which is formed by association of serine O-acetyltransferase (SAT) and O-acetylserine sulfhydrylase (OASS), acts as a sensor and modulator of thiol metabolism by responding to changes in nutrient conditions. Here we examine the oligomerization and energetics of formation of the soybean CSC. Biophysical examination of the CSC by size exclusion chromatography and sedimentation ultracentrifugation indicates that this assembly (complex M(r) approximately 330,000) consists of a single SAT trimer (trimer M(r) approximately 110,000) and three OASS dimers (dimer M(r) approximately 70,000). Analysis of the SAT-OASS interaction by isothermal titration calorimetry reveals negative cooperativity with three distinct binding events during CSC formation with K(d) values of 0.3, 7.5, and 78 nm. The three binding events are also observed using surface plasmon resonance with comparable affinities. The stability of the CSC derives from rapid association and extremely slow dissociation of OASS with SAT and requires the C terminus of SAT for the interaction. Steady-state kinetic analysis shows that CSC formation enhances SAT activity and releases SAT from substrate inhibition and feedback inhibition by cysteine, the final product of the biosynthesis pathway. Cysteine inhibits SAT and the CSC with K(i) values of 2 and 70 microm, respectively. These results suggest a new model for the architecture of this regulatory complex and additional control mechanisms for biochemically controlling plant cysteine biosynthesis. Based on previous work and our results, we suggest that OASS acts as an enzyme chaperone of SAT in the CSC. PMID:19213732

  7. Protein-protein, protein-RNA and protein-lipid interactions of signal-recognition particle components in the hyperthermoacidophilic archaeon Acidianus ambivalens.

    PubMed Central

    Moll, Ralf G

    2003-01-01

    The signal-recognition particle (SRP) of one of the most acidophilic and hyperthermophilic archaeal cells, Acidianus ambivalens, and its putative receptor component, FtsY (prokaryotic SRP receptor), were investigated in detail. A. ambivalens Ffh (fifty-four-homologous protein) was shown to be a soluble protein with strong affinity to membranes. In its membrane-residing form, Ffh was extracted from plasma membranes with chaotropic agents like urea, but not with agents diminishing electrostatic interactions. Using unilamellar tetraether phospholipid vesicles, both Ffh and FtsY associate independently from each other in the absence of other factors, suggesting an equilibrium of soluble and membrane-bound protein forms under in vivo conditions. The Ffh protein precipitated from cytosolic cell supernatants with anti-Ffh antibodies, together with an 7 S-alike SRP-RNA, suggesting a stable core ribonucleoprotein composed of both components under native conditions. The SRP RNA of A. ambivalens depicted a size of about 309 nucleotides like the SRP RNA of the related organism Sulfolobus acidocaldarius. A stable heterodimeric complex composed of Ffh and FtsY was absent in cytosolic supernatants, indicating a transiently formed complex during archaeal SRP targeting. The FtsY protein precipitated in cytosolic supernatants with anti-FtsY antisera as a homomeric protein lacking accessory protein components. However, under in vitro conditions, recombinantly generated Ffh and FtsY associate in a nucleotide-independent manner, supporting a structural receptor model with two interacting apoproteins. PMID:12775213

  8. Protein-protein interactions among ion channels regulate ion transport in the kidney.

    PubMed

    Boulpaep, E

    2009-01-01

    Epithelial ion transport in various organs has long been known to be controlled by extracellular agonists acting via membrane receptors or by intracellular messengers. Evidence is mounting for regulation of transport by direct interaction among membrane proteins or between a membrane transport protein and membrane-attached proteins. The membrane protein CFTR (Cystic Fibrosis Transmembrane Regulator) is widely expressed along the length of the nephron, but its role as a chloride channel does not appear to be critical for renal handling of salt and water. It is well established that the inward rectifying K channels (ROMK = Kir 1.1) in the thick ascending limb of Henle and in principal cells of the collecting duct are inhibited by millimolar concentrations of cytosolic Mg-ATP. However, the mechanism of this inhibition has been an enigma. We propose that the ATP-Binding Cassette (ABC) protein CFTR is a cofactor for Kir 1.1 regulation. Indeed, Mg-ATP sensitivity of Kir 1.1 is completely absent in two different mouse models of cystic fibrosis. In addition, the open-closed state of CFTR appears to provide a molecular gating switch that prevents or facilitates the ATP sensing of Kir 1.1. Does Mg-ATP sensing by the CFTR- Kir 1.1 complex play a role in coupling metabolism to ion transport? Physiological intracellular ATP concentrations in tubule cells are in the millimolar range, a saturating concentration for the gating of Kir 1.1 by Mg-ATP. Therefore, Kir 1.1 channels would be closed and unable to contribute to regulation of potassium secretion unless some other process modulated the CFTR-dependent ATP-sensitivity of Kir 1.1. The third component of the metabolic sensor-effector complex for Kir 1.1 regulation is most likely the AMP-regulated serine-threonine kinase, AMP kinase (AMPK). Changing levels in AMP rather than in ATP constitute the metabolic signal "sensed" by tubule cells. Because AMPK inhibits CFTR by modulating CFTR channel gating, we propose that renal K

  9. De novo design of protein-protein interactions through modification of inter-molecular helix-helix interface residues.

    PubMed

    Yagi, Sota; Akanuma, Satoshi; Yamagishi, Manami; Uchida, Tatsuya; Yamagishi, Akihiko

    2016-05-01

    For de novo design of protein-protein interactions (PPIs), information on the shape and chemical complementarity of their interfaces is generally required. Recent advances in computational PPI design have allowed for de novo design of protein complexes, and several successful examples have been reported. In addition, a simple and easy-to-use approach has also been reported that arranges leucines on a solvent-accessible region of an α-helix and places charged residues around the leucine patch to induce interactions between the two helical peptides. For this study, we adopted this approach to de novo design a new PPI between the helical bundle proteins sulerythrin and LARFH. A non-polar patch was created on an α-helix of LARFH around which arginine residues were introduced to retain its solubility. The strongest interaction found was for the LARFH variant cysLARFH-IV-3L3R and the sulerythrin mutant 6L6D (KD=0.16 μM). This artificial protein complex is maintained by hydrophobic and ionic interactions formed by the inter-molecular helical bundle structure. Therefore, by the simple and easy-to-use approach to create de novo interfaces on the α-helices, we successfully generated an artificial PPI. We also created a second LARFH variant with the non-polar patch surrounded by positively charged residues at each end. Upon mixing this LARFH variant with 6L6D, mesh-like fibrous nanostructures were observed by atomic force microscopy. Our method may, therefore, also be applicable to the de novo design of protein nanostructures. PMID:26867971

  10. Lipid-mediated Protein-protein Interactions Modulate Respiration-driven ATP Synthesis

    PubMed Central

    Nilsson, Tobias; Lundin, Camilla Rydström; Nordlund, Gustav; Ädelroth, Pia; von Ballmoos, Christoph; Brzezinski, Peter

    2016-01-01

    Energy conversion in biological systems is underpinned by membrane-bound proton transporters that generate and maintain a proton electrochemical gradient across the membrane which used, e.g. for generation of ATP by the ATP synthase. Here, we have co-reconstituted the proton pump cytochrome bo3 (ubiquinol oxidase) together with ATP synthase in liposomes and studied the effect of changing the lipid composition on the ATP synthesis activity driven by proton pumping. We found that for 100 nm liposomes, containing 5 of each proteins, the ATP synthesis rates decreased significantly with increasing fractions of DOPA, DOPE, DOPG or cardiolipin added to liposomes made of DOPC; with e.g. 5% DOPG, we observed an almost 50% decrease in the ATP synthesis rate. However, upon increasing the average distance between the proton pumps and ATP synthases, the ATP synthesis rate dropped and the lipid dependence of this activity vanished. The data indicate that protons are transferred along the membrane, between cytochrome bo3 and the ATP synthase, but only at sufficiently high protein densities. We also argue that the local protein density may be modulated by lipid-dependent changes in interactions between the two proteins complexes, which points to a mechanism by which the cell may regulate the overall activity of the respiratory chain. PMID:27063297

  11. Bio-layer interferometry for measuring kinetics of protein-protein interactions and allosteric ligand effects.

    PubMed

    Shah, Naman B; Duncan, Thomas M

    2014-01-01

    We describe the use of Bio-layer Interferometry to study inhibitory interactions of subunit ε with the catalytic complex of Escherichia coli ATP synthase. Bacterial F-type ATP synthase is the target of a new, FDA-approved antibiotic to combat drug-resistant tuberculosis. Understanding bacteria-specific auto-inhibition of ATP synthase by the C-terminal domain of subunit ε could provide a new means to target the enzyme for discovery of antibacterial drugs. The C-terminal domain of ε undergoes a dramatic conformational change when the enzyme transitions between the active and inactive states, and catalytic-site ligands can influence which of ε's conformations is predominant. The assay measures kinetics of ε's binding/dissociation with the catalytic complex, and indirectly measures the shift of enzyme-bound ε to and from the apparently nondissociable inhibitory conformation. The Bio-layer Interferometry signal is not overly sensitive to solution composition, so it can also be used to monitor allosteric effects of catalytic-site ligands on ε's conformational changes. PMID:24638157

  12. Calculating the Bimolecular Rate of Protein-Protein Association with Interacting Crowders.

    PubMed

    Yap, Eng-Hui; Head-Gordon, Teresa

    2013-05-14

    We have recently introduced a method termed Poisson-Boltzmann semianalytical method (PB-SAM) for solving the linearized Poisson-Boltzmann equation for large numbers of arbitrarily shaped dielectric cavities with controlled precision. In this work we extend the applicability of the PB-SAM approach by deriving force and torque expressions that fully account for mutual polarization in both the zero- and first-order derivatives of the surface charges, that can now be embedded into a Brownian dynamics scheme to look at electrostatic-driven mesoscale assembly and kinetics. We demonstrate the capabilities of the PB-SAM approach by simulating the protein concentration effects on the bimolecular rate of association of barnase and barstar, under periodic boundary conditions and evaluated through mean first passage times. We apply PB-SAM to the pseudo-first-order reaction rate conditions in which either barnase or barstar are in great excess relative to the other protein (124:1). This can be considered a specific case in which the PB-SAM approach can be applied to crowding conditions in which crowders are not inert but can form interactions with other molecules. PMID:26583736

  13. Analysis of phosphorylation-dependent protein-protein interactions of histone h3.

    PubMed

    Klingberg, Rebecca; Jost, Jan Oliver; Schümann, Michael; Gelato, Kathy Ann; Fischle, Wolfgang; Krause, Eberhard; Schwarzer, Dirk

    2015-01-16

    Multiple posttranslational modifications (PTMs) of histone proteins including site-specific phosphorylation of serine and threonine residues govern the accessibility of chromatin. According to the histone code theory, PTMs recruit regulatory proteins or block their access to chromatin. Here, we report a general strategy for simultaneous analysis of both of these effects based on a SILAC MS scheme. We applied this approach for studying the biochemical role of phosphorylated S10 of histone H3. Differential pull-down experiments with H3-tails synthesized from l- and d-amino acids uncovered that histone acetyltransferase 1 (HAT1) and retinoblastoma-binding protein 7 (RBBP7) are part of the protein network, which interacts with the unmodified H3-tail. An additional H3-derived bait containing the nonhydrolyzable phospho-serine mimic phosphonomethylen-alanine (Pma) at S10 recruited several isoforms of the 14-3-3 family and blocked the recruitment of HAT1 and RBBP7 to the unmodified H3-tail. Our observations provide new insights into the many functions of H3S10 phosphorylation. In addition, the outlined methodology is generally applicable for studying specific binding partners of unmodified histone tails. PMID:25330109

  14. Fuzzy regions in an intrinsically disordered protein impair protein-protein interactions.

    PubMed

    Gruet, Antoine; Dosnon, Marion; Blocquel, David; Brunel, Joanna; Gerlier, Denis; Das, Rahul K; Bonetti, Daniela; Gianni, Stefano; Fuxreiter, Monika; Longhi, Sonia; Bignon, Christophe

    2016-02-01

    Despite the partial disorder-to-order transition that intrinsically disordered proteins often undergo upon binding to their partners, a considerable amount of residual disorder may be retained in the bound form, resulting in a fuzzy complex. Fuzzy regions flanking molecular recognition elements may enable partner fishing through non-specific, transient contacts, thereby facilitating binding, but may also disfavor binding through various mechanisms. So far, few computational or experimental studies have addressed the effect of fuzzy appendages on partner recognition by intrinsically disordered proteins. In order to shed light onto this issue, we used the interaction between the intrinsically disordered C-terminal domain of the measles virus (MeV) nucleoprotein (NTAIL ) and the X domain (XD) of the viral phosphoprotein as model system. After binding to XD, the N-terminal region of NTAIL remains conspicuously disordered, with α-helical folding taking place only within a short molecular recognition element. To study the effect of the N-terminal fuzzy region on NTAIL /XD binding, we generated N-terminal truncation variants of NTAIL , and assessed their binding abilities towards XD. The results revealed that binding increases with shortening of the N-terminal fuzzy region, with this also being observed with hsp70 (another MeV NTAIL binding partner), and for the homologous NTAIL /XD pairs from the Nipah and Hendra viruses. Finally, similar results were obtained when the MeV NTAIL fuzzy region was replaced with a highly dissimilar artificial disordered sequence, supporting a sequence-independent inhibitory effect of the fuzzy region. PMID:26684000

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

    PubMed Central

    Nissen, Klaus B.; Haugaard-Kedström, Linda M.; Wilbek, Theis S.; Nielsen, Line S.; Åberg, Emma; Kristensen, Anders S.; Bach, Anders; Jemth, Per; Strømgaard, Kristian

    2015-01-01

    PDZ domains in general, and those of PSD-95 in particular, are emerging as promising drug targets for diseases such as ischemic stroke. We have previously shown that dimeric ligands that simultaneously target PDZ1 and PDZ2 of PSD-95 are highly potent inhibitors of PSD-95. However, PSD-95 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 experiments using stopped-flow spectrometry showed that the increase in affinity is caused by a decrease in the dissociation rate of the trimeric ligand as compared to the dimeric ligands, likely reflecting the lower probability of simultaneous dissociation of all three PDZ ligands. Thus, we have provided novel inhibitors of the MAGUK proteins with exceptionally high affinity, which can be used to further elucidate the therapeutic potential of these proteins. PMID:25658767

  16. Discovery of direct inhibitors of Keap1-Nrf2 protein-protein interaction as potential therapeutic and preventive agents.

    PubMed

    Abed, Dhulfiqar Ali; Goldstein, Melanie; Albanyan, Haifa; Jin, Huijuan; Hu, Longqin

    2015-07-01

    The Keap1-Nrf2-ARE pathway is an important antioxidant defense mechanism that protects cells from oxidative stress and the Keap1-Nrf2 protein-protein interaction (PPI) has become an important drug target to upregulate the expression of ARE-controlled cytoprotective oxidative stress response enzymes in the development of therapeutic and preventive agents for a number of diseases and conditions. However, most known Nrf2 activators/ARE inducers are indirect inhibitors of Keap1-Nrf2 PPI and they are electrophilic species that act by modifying the sulfhydryl groups of Keap1׳s cysteine residues. The electrophilicity of these indirect inhibitors may cause "off-target" side effects by reacting with cysteine residues of other important cellular proteins. Efforts have recently been focused on the development of direct inhibitors of Keap1-Nrf2 PPI. This article reviews these recent research efforts including the development of high throughput screening assays, the discovery of peptide and small molecule direct inhibitors, and the biophysical characterization of the binding of these inhibitors to the target Keap1 Kelch domain protein. These non-covalent direct inhibitors of Keap1-Nrf2 PPI could potentially be developed into effective therapeutic or preventive agents for a variety of diseases and conditions. PMID:26579458

  17. Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

    PubMed

    Liu, Guang-Hui; Shen, Hong-Bin; Yu, Dong-Jun

    2016-04-01

    Accurately predicting protein-protein interaction sites (PPIs) is currently a hot topic because it has been demonstrated to be very useful for understanding disease mechanisms and designing drugs. Machine-learning-based computational approaches have been broadly utilized