51
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Meyer MJ, Beltrán JF, Liang S, Fragoza R, Rumack A, Liang J, Wei X, Yu H. Interactome INSIDER: a structural interactome browser for genomic studies. Nat Methods 2018; 15:107-114. [PMID: 29355848 PMCID: PMC6026581 DOI: 10.1038/nmeth.4540] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 10/22/2017] [Indexed: 02/07/2023]
Abstract
We present Interactome INSIDER, a tool to link genomic variant information with
structural protein-protein interactomes. Underlying this tool is the application of
machine learning to predict protein interaction interfaces for 185,957 protein
interactions with previously unresolved interfaces, in human and 7 model organisms,
including the entire experimentally determined human binary interactome. Predicted
interfaces exhibit similar functional properties as known interfaces, including enrichment
for disease mutations and recurrent cancer mutations. Through 2,164 de
novo mutagenesis experiments, we show that mutations of predicted and known
interface residues disrupt interactions at a similar rate, and much more frequently than
mutations outside of predicted interfaces. To spur functional genomic studies, Interactome
INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether
variants or disease mutations are enriched in known and predicted interaction interfaces
at various resolutions. Users may explore known population variants, disease mutations,
and somatic cancer mutations, or upload their own set of mutations for this purpose.
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Affiliation(s)
- Michael J Meyer
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA.,Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, USA
| | - Juan Felipe Beltrán
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Siqi Liang
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Robert Fragoza
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA.,Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
| | - Aaron Rumack
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Jin Liang
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Xiaomu Wei
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Department of Medicine, Weill Cornell College of Medicine, New York, New York, USA
| | - Haiyuan Yu
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
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52
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Qiu Z, Zhou B, Yuan J. Protein–protein interaction site predictions with minimum covariance determinant and Mahalanobis distance. J Theor Biol 2017; 433:57-63. [DOI: 10.1016/j.jtbi.2017.08.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 08/26/2017] [Accepted: 08/30/2017] [Indexed: 10/18/2022]
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53
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Taherzadeh G, Zhou Y, Liew AWC, Yang Y. Structure-based prediction of protein– peptide binding regions using Random Forest. Bioinformatics 2017; 34:477-484. [DOI: 10.1093/bioinformatics/btx614] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 09/25/2017] [Indexed: 11/12/2022] Open
Affiliation(s)
- Ghazaleh Taherzadeh
- School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia
| | - Yaoqi Zhou
- School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia
- Institute for Glycomics, Griffith University, Parklands Drive, Southport, QLD, Australia
| | - Alan Wee-Chung Liew
- School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia
| | - Yuedong Yang
- School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia
- Institute for Glycomics, Griffith University, Parklands Drive, Southport, QLD, Australia
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
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54
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Northey TC, Barešić A, Martin ACR. IntPred: a structure-based predictor of protein-protein interaction sites. Bioinformatics 2017; 34:223-229. [PMID: 28968673 PMCID: PMC5860208 DOI: 10.1093/bioinformatics/btx585] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 08/21/2017] [Accepted: 09/15/2017] [Indexed: 11/17/2022] Open
Abstract
Motivation Protein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein–protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods. Results On an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent. Availability and implementation IntPred is implemented in Perl and may be downloaded for local use or run via a web server at www.bioinf.org.uk/intpred/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Thomas C Northey
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
| | - Anja Barešić
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
| | - Andrew C R Martin
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
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55
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Biswas R, Ghosh S, Bagchi A. A structural perspective on the interactions of TRAF6 and Basigin during the onset of melanoma: A molecular dynamics simulation study. J Mol Recognit 2017; 30. [PMID: 28612997 DOI: 10.1002/jmr.2643] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 04/19/2017] [Accepted: 05/10/2017] [Indexed: 12/12/2022]
Abstract
Metastatic melanoma is the most fatal type of skin cancer. The roles of matrix metalloproteinases (MMPs) have well been established in the onset of melanoma. Basigin (BSG) belongs to the immunoglobulin superfamily and is critical for induction of extracellular MMPs during the onset of various cancers including melanoma. Tumor necrosis factor receptor-associated factor 6 (TRAF6) is an E3-ligase that interacts with BSG and mediates its membrane localization, which leads to MMP expression in melanoma cells. This makes TRAF6 a potential therapeutic target in melanoma. We here conducted protein-protein interaction studies on TRAF6 and BSG to get molecular level insights of the reactions. The structure of human BSG was constructed by protein threading. Molecular-docking method was applied to develop the TRAF6-BSG complex. The refined docked complex was further optimized by molecular dynamics simulations. Results from binding free energy, surface properties, and electrostatic interaction analysis indicate that Lys340 and Glu417 of TRAF6 play as the anchor residues in the protein interaction interface. The current study will be helpful in designing specific modulators of TRAF6 to control melanoma metastasis.
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Affiliation(s)
- Ria Biswas
- Department of Biochemistry and Biophysics, University of Kalyani, Kalyani, Nadia, India
| | - Semanti Ghosh
- Department of Biochemistry and Biophysics, University of Kalyani, Kalyani, Nadia, India
| | - Angshuman Bagchi
- Department of Biochemistry and Biophysics, University of Kalyani, Kalyani, Nadia, India
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56
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Structural analysis and insight into Zika virus NS5 mediated interferon inhibition. INFECTION GENETICS AND EVOLUTION 2017; 51:143-152. [PMID: 28365387 DOI: 10.1016/j.meegid.2017.03.027] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Revised: 03/03/2017] [Accepted: 03/25/2017] [Indexed: 11/20/2022]
Abstract
The Zika virus outbreak in 2015-2016 is the largest of its kind for which WHO declared a Public Health Emergency of International Concerns. No FDA approved drug is available for the treatment of the viral infection. The interaction of flavivirus NS5 protein with SIAH2 ubiquitin ligase has been previously known. NS5 of Zika virus has been implicated in the degradation of STAT2 protein, which activates interferon-stimulated antiviral activity. Based on our proposition that NS5 utilizes SIAH2-mediated proteasomal degradation of STAT2, an in-silico study was carried out to characterize the protein-protein interactions between NS5, SIAH2 and STAT2 proteins. The aim of our study was to identify the amino acid residues of NS5 involved in IFN antagonism as well as to find the association between NS5, SIAH2 and STAT2 to predict the interaction pattern of these proteins. Analysis proposed that NS5 recruits SIAH2 for the ubiquitination-dependent degradation of STAT2. NS5 residues involved in interaction with SIAH2 and/or STAT2 were found to be mostly conserved across related flaviviruses. These are novel findings regarding the Zika virus and require confirmation through experimental approaches.
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57
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Murakami Y, Tripathi LP, Prathipati P, Mizuguchi K. Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery. Curr Opin Struct Biol 2017; 44:134-142. [PMID: 28364585 DOI: 10.1016/j.sbi.2017.02.005] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2016] [Revised: 02/05/2017] [Accepted: 02/23/2017] [Indexed: 11/29/2022]
Abstract
Protein-protein interactions (PPIs) are vital to maintaining cellular homeostasis. Several PPI dysregulations have been implicated in the etiology of various diseases and hence PPIs have emerged as promising targets for drug discovery. Surface residues and hotspot residues at the interface of PPIs form the core regions, which play a key role in modulating cellular processes such as signal transduction and are used as starting points for drug design. In this review, we briefly discuss how PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research. Next, we describe the principles of in silico PPI prediction and survey the existing PPI and PPI site prediction servers that are useful for drug discovery. Finally, we discuss the potential of in silico PPI prediction in drug discovery.
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Affiliation(s)
- Yoichi Murakami
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan.
| | - Lokesh P Tripathi
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan.
| | - Philip Prathipati
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan
| | - Kenji Mizuguchi
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan.
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58
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Integrating computational methods and experimental data for understanding the recognition mechanism and binding affinity of protein-protein complexes. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2017; 128:33-38. [PMID: 28069340 DOI: 10.1016/j.pbiomolbio.2017.01.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Revised: 01/04/2017] [Accepted: 01/05/2017] [Indexed: 01/09/2023]
Abstract
Protein-protein interactions perform several functions inside the cell. Understanding the recognition mechanism and binding affinity of protein-protein complexes is a challenging problem in experimental and computational biology. In this review, we focus on two aspects (i) understanding the recognition mechanism and (ii) predicting the binding affinity. The first part deals with computational techniques for identifying the binding site residues and the contribution of important interactions for understanding the recognition mechanism of protein-protein complexes in comparison with experimental observations. The second part is devoted to the methods developed for discriminating high and low affinity complexes, and predicting the binding affinity of protein-protein complexes using three-dimensional structural information and just from the amino acid sequence. The overall view enhances our understanding of the integration of experimental data and computational methods, recognition mechanism of protein-protein complexes and the binding affinity.
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59
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Ripoche H, Laine E, Ceres N, Carbone A. JET2 Viewer: a database of predicted multiple, possibly overlapping, protein-protein interaction sites for PDB structures. Nucleic Acids Res 2017; 45:D236-D242. [PMID: 27899675 PMCID: PMC5210541 DOI: 10.1093/nar/gkw1053] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2016] [Revised: 10/18/2016] [Accepted: 10/20/2016] [Indexed: 11/13/2022] Open
Abstract
The database JET2 Viewer, openly accessible at http://www.jet2viewer.upmc.fr/, reports putative protein binding sites for all three-dimensional (3D) structures available in the Protein Data Bank (PDB). This knowledge base was generated by applying the computational method JET2 at large-scale on more than 20 000 chains. JET2 strategy yields very precise predictions of interacting surfaces and unravels their evolutionary process and complexity. JET2 Viewer provides an online intelligent display, including interactive 3D visualization of the binding sites mapped onto PDB structures and suitable files recording JET2 analyses. Predictions were evaluated on more than 15 000 experimentally characterized protein interfaces. This is, to our knowledge, the largest evaluation of a protein binding site prediction method. The overall performance of JET2 on all interfaces are: Sen = 52.52, PPV = 51.24, Spe = 80.05, Acc = 75.89. The data can be used to foster new strategies for protein-protein interactions modulation and interaction surface redesign.
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Affiliation(s)
- Hugues Ripoche
- Sorbonne Universités, UPMC University Paris 06, CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005 Paris, France
| | - Elodie Laine
- Sorbonne Universités, UPMC University Paris 06, CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005 Paris, France
| | - Nicoletta Ceres
- CNRS UMR 5086/University Lyon I, Institut de Biologie et Chimie des Proteines, 69367 Lyon, France
| | - Alessandra Carbone
- Sorbonne Universités, UPMC University Paris 06, CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005 Paris, France .,Institut Universitaire de France, 75005 Paris, France
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60
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Faraggi E, Kloczkowski A. Accurate Prediction of One-Dimensional Protein Structure Features Using SPINE-X. Methods Mol Biol 2017; 1484:45-53. [PMID: 27787819 DOI: 10.1007/978-1-4939-6406-2_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Accurate prediction of protein secondary structure and other one-dimensional structure features is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. SPINE-X is a software package to predict secondary structure as well as accessible surface area and dihedral angles ϕ and ψ. For secondary structure SPINE-X achieves an accuracy of between 81 and 84 % depending on the dataset and choice of tests. The Pearson correlation coefficient for accessible surface area prediction is 0.75 and the mean absolute error from the ϕ and ψ dihedral angles are 20∘ and 33∘, respectively. The source code and a Linux executables for SPINE-X are available from Research and Information Systems at http://mamiris.com .
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Affiliation(s)
- Eshel Faraggi
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46032, USA
- Research and Information Systems, LLC, Indianapolis, IN, USA
| | - Andrzej Kloczkowski
- Battelle Center for Mathematical Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
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61
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Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes. Methods Mol Biol 2017; 1484:237-253. [PMID: 27787830 DOI: 10.1007/978-1-4939-6406-2_16] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Studying protein-protein interactions leads to a better understanding of the underlying principles of several biological pathways. Cost and labor-intensive experimental techniques suggest the need for computational methods to complement them. Several such state-of-the-art methods have been reported for analyzing diverse aspects such as predicting binding partners, interface residues, and binding affinity for protein-protein complexes with reliable performance. However, there are specific drawbacks for different methods that indicate the need for their improvement. This review highlights various available computational algorithms for analyzing diverse aspects of protein-protein interactions and endorses the necessity for developing new robust methods for gaining deep insights about protein-protein interactions.
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62
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Dai W, Wu A, Ma L, Li YX, Jiang T, Li YY. A novel index of protein-protein interface propensity improves interface residue recognition. BMC SYSTEMS BIOLOGY 2016; 10:112. [PMID: 28155660 PMCID: PMC5259823 DOI: 10.1186/s12918-016-0351-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Background Protein-protein interface holds important information of protein-protein interactions which play key roles in most biological processes. In the past few years, a lot of efforts have been made to improve interface residue recognition by characterizing protein-protein interfaces and extracting relevant features. However, most previous studies were carried out in a qualitative level, and there are also some inconsistencies between them. Results In the present work, to improve interface residue recognition, we built a novel quantitative residue protein-protein interface propensity index (QIPI) and gained a comprehensive picture of protein-protein interface through analyzing protein-protein interfaces on our comprehensive protein-protein interfaces dataset (Astral2.05-40-4506). Furthermore, in order to assess the effect of QIPI in improving the protein-protein interface prediction, we developed an interface residue recognition method SPR (Single domain based Patch Recognition) based on the QIPI. The evaluation results proved that our novel QIPI is able to improve the interface residue recognition. Conclusions Through a comprehensive quantitative analysis of protein-protein interface, we constructed a novel quantitative protein-protein interface propensity index (QIPI), which could be easily applied to improve the interface residue recognition and helpful in understanding the protein-protein interface. Availability QIPI and SPR are available to non-commercial users at our website: http://www.scbit.org/QIPI/. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0351-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wentao Dai
- Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 2012035, People's Republic of China.,Shanghai Industrial Technology Institute, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China
| | - Aiping Wu
- Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, 215123, China
| | - Liangxiao Ma
- Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 2012035, People's Republic of China
| | - Yi-Xue Li
- Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 2012035, People's Republic of China.,Shanghai Industrial Technology Institute, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China.,Shanghai Engineering Research Center of Pharmaceutical Translation, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China
| | - Taijiao Jiang
- Suzhou Institute of Systems Medicine, Suzhou, Jiangsu, 215123, China. .,Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
| | - Yuan-Yuan Li
- Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 2012035, People's Republic of China. .,Shanghai Industrial Technology Institute, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China. .,Shanghai Engineering Research Center of Pharmaceutical Translation, 1278 Keyuan Road, Shanghai, 201203, People's Republic of China.
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63
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Goldberg AB, Cho E, Miller CJ, Lou HJ, Turk BE. Identification of a Substrate-selective Exosite within the Metalloproteinase Anthrax Lethal Factor. J Biol Chem 2016; 292:814-825. [PMID: 27909054 DOI: 10.1074/jbc.m116.761734] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 11/23/2016] [Indexed: 01/02/2023] Open
Abstract
The metalloproteinase anthrax lethal factor (LF) is secreted by Bacillus anthracis to promote disease virulence through disruption of host signaling pathways. LF is a highly specific protease, exclusively cleaving mitogen-activated protein kinase kinases (MKKs) and rodent NLRP1B (NACHT leucine-rich repeat and pyrin domain-containing protein 1B). How LF achieves such restricted substrate specificity is not understood. Previous studies have suggested the existence of an exosite interaction between LF and MKKs that promotes cleavage efficiency and specificity. Through a combination of in silico prediction and site-directed mutagenesis, we have mapped an exosite to a non-catalytic region of LF. Mutations within this site selectively impair proteolysis of full-length MKKs yet have no impact on cleavage of short peptide substrates. Although this region appears important for cleaving all LF protein substrates, we found that mutation of specific residues within the exosite differentially affects MKK and NLRP1B cleavage in vitro and in cultured cells. One residue in particular, Trp-271, is essential for cleavage of MKK3, MKK4, and MKK6 but dispensable for targeting of MEK1, MEK2, and NLRP1B. Analysis of chimeric substrates suggests that this residue interacts with the MKK catalytic domain. We found that LF-W271A blocked ERK phosphorylation and growth in a melanoma cell line, suggesting that it may provide a highly selective inhibitor of MEK1/2 for use as a cancer therapeutic. These findings provide insight into how a bacterial toxin functions to specifically impair host signaling pathways and suggest a general strategy for mapping protease exosite interactions.
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Affiliation(s)
- Allison B Goldberg
- From the Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut 06520
| | - Eunice Cho
- From the Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut 06520
| | - Chad J Miller
- From the Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut 06520
| | - Hua Jane Lou
- From the Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut 06520
| | - Benjamin E Turk
- From the Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut 06520
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64
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Chen J, Xie ZR, Wu Y. Understand protein functions by comparing the similarity of local structural environments. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2016; 1865:142-152. [PMID: 27884635 DOI: 10.1016/j.bbapap.2016.11.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 11/03/2016] [Accepted: 11/17/2016] [Indexed: 12/20/2022]
Abstract
The three-dimensional structures of proteins play an essential role in regulating binding between proteins and their partners, offering a direct relationship between structures and functions of proteins. It is widely accepted that the function of a protein can be determined if its structure is similar to other proteins whose functions are known. However, it is also observed that proteins with similar global structures do not necessarily correspond to the same function, while proteins with very different folds can share similar functions. This indicates that function similarity is originated from the local structural information of proteins instead of their global shapes. We assume that proteins with similar local environments prefer binding to similar types of molecular targets. In order to testify this assumption, we designed a new structural indicator to define the similarity of local environment between residues in different proteins. This indicator was further used to calculate the probability that a given residue binds to a specific type of structural neighbors, including DNA, RNA, small molecules and proteins. After applying the method to a large-scale non-redundant database of proteins, we show that the positive signal of binding probability calculated from the local structural indicator is statistically meaningful. In summary, our studies suggested that the local environment of residues in a protein is a good indicator to recognize specific binding partners of the protein. The new method could be a potential addition to a suite of existing template-based approaches for protein function prediction.
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Affiliation(s)
- Jiawen Chen
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY 10461, United States
| | - Zhong-Ru Xie
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY 10461, United States
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY 10461, United States.
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65
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Laine E, Carbone A. Protein social behavior makes a stronger signal for partner identification than surface geometry. Proteins 2016; 85:137-154. [PMID: 27802579 PMCID: PMC5242317 DOI: 10.1002/prot.25206] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Revised: 10/10/2016] [Accepted: 10/20/2016] [Indexed: 01/26/2023]
Abstract
Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico‐chemical and geometrical properties determine who interact with whom remains far from fully understood. We show that characterizing how a protein behaves with many potential interactors in a complete cross‐docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S‐index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface‐based (ranking) score to discriminate partners from non‐interactors. We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking. Proteins 2016; 85:137–154. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Elodie Laine
- Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Paris, 75005, France
| | - Alessandra Carbone
- Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Paris, 75005, France.,Institut Universitaire de France, Paris, 75005, France
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66
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Bleymüller WM, Lämmermann N, Ebbes M, Maynard D, Geerds C, Niemann HH. MET-activating Residues in the B-repeat of the Listeria monocytogenes Invasion Protein InlB. J Biol Chem 2016; 291:25567-25577. [PMID: 27789707 DOI: 10.1074/jbc.m116.746685] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Revised: 10/26/2016] [Indexed: 12/20/2022] Open
Abstract
The facultative intracellular pathogen Listeria monocytogenes causes listeriosis, a rare but life-threatening disease. Host cell entry begins with activation of the human receptor tyrosine kinase MET through the bacterial invasion protein InlB, which contains an internalin domain, a B-repeat, and three GW domains. The internalin domain is known to bind MET, but no interaction partner is known for the B-repeat. Adding the B-repeat to the internalin domain potentiates MET activation and is required to stimulate Madin-Darby canine kidney (MDCK) cell scatter. Therefore, it has been hypothesized that the B-repeat may bind a co-receptor on host cells. To test this hypothesis, we mutated residues that might be important for binding an interaction partner. We identified two adjacent residues in strand β2 of the β-grasp fold whose mutation abrogated induction of MDCK cell scatter. Biophysical analysis indicated that these mutations do not alter protein structure. We then tested these mutants in human HT-29 cells that, in contrast to the MDCK cells, were responsive to the internalin domain alone. These assays revealed a dominant negative effect, reducing the activity of a construct of the internalin domain and mutated B-repeat below that of the individual internalin domain. Phosphorylation assays of MET and its downstream targets AKT and ERK confirmed the dominant negative effect. Attempts to identify a host cell receptor for the B-repeat were not successful. We conclude that there is limited support for a co-receptor hypothesis and instead suggest that the B-repeat contributes to MET activation through low affinity homodimerization.
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Affiliation(s)
- Willem M Bleymüller
- From the Department of Chemistry, Bielefeld University, 33615 Bielefeld, Germany
| | - Nina Lämmermann
- From the Department of Chemistry, Bielefeld University, 33615 Bielefeld, Germany
| | - Maria Ebbes
- From the Department of Chemistry, Bielefeld University, 33615 Bielefeld, Germany
| | - Daniel Maynard
- From the Department of Chemistry, Bielefeld University, 33615 Bielefeld, Germany
| | - Christina Geerds
- From the Department of Chemistry, Bielefeld University, 33615 Bielefeld, Germany
| | - Hartmut H Niemann
- From the Department of Chemistry, Bielefeld University, 33615 Bielefeld, Germany
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67
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Zhang D, Cui H, Korkin D, Wu Z. Incorporation of protein binding effects into likelihood ratio test for exome sequencing data. BMC Proc 2016; 10:275-281. [PMID: 27980649 PMCID: PMC5133515 DOI: 10.1186/s12919-016-0043-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Statistical association studies are an important tool in detecting novel disease genes. However, for sequencing data, association studies confront the challenge of low power because of relatively small data sample size and rare variants. Incorporating biological information that reflects disease mechanism is likely to strengthen the association evidence of disease genes, and thus increase the power of association studies. In this paper, we annotate non-synonymous single-nucleotide variants according to protein binding sites (BSs) by using a more accurate BS prediction method. We then incorporate this information into association study through a statistical framework of likelihood ratio test (LRT) based on weighted burden score of single-nucleotide variants (SNVs). The strategy is applied to Genetic Analysis Workshop 19 exome-sequencing data for detecting novel genes associated to hypotension. The SNV-weighting LRT idea is empirically verified by the simulated phenotypes (336 cases and 1607 controls), and the weights based on BS annotation are applied to the real phenotypes (394 cases and 1457 controls). Such strategy of weighting the prior information on protein functional sites is shown to be superior to the unweighted LRT and serves as a good complement to the existing association tests. Several putative genes are reported; some of them are functionally related to hypertension according to the previous evidence in the literature.
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Affiliation(s)
- Dongni Zhang
- Mathematics Department, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280 USA
| | - Hongzhu Cui
- Computer Science Department, Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280 USA
| | - Dmitry Korkin
- Computer Science Department, Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280 USA
| | - Zheyang Wu
- Mathematics Department, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280 USA
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68
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Taherzadeh G, Zhou Y, Liew AWC, Yang Y. Sequence-Based Prediction of Protein-Carbohydrate Binding Sites Using Support Vector Machines. J Chem Inf Model 2016; 56:2115-2122. [PMID: 27623166 DOI: 10.1021/acs.jcim.6b00320] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Carbohydrate-binding proteins play significant roles in many diseases including cancer. Here, we established a machine-learning-based method (called sequence-based prediction of residue-level interaction sites of carbohydrates, SPRINT-CBH) to predict carbohydrate-binding sites in proteins using support vector machines (SVMs). We found that integrating evolution-derived sequence profiles with additional information on sequence and predicted solvent accessible surface area leads to a reasonably accurate, robust, and predictive method, with area under receiver operating characteristic curve (AUC) of 0.78 and 0.77 and Matthew's correlation coefficient of 0.34 and 0.29, respectively for 10-fold cross validation and independent test without balancing binding and nonbinding residues. The quality of the method is further demonstrated by having statistically significantly more binding residues predicted for carbohydrate-binding proteins than presumptive nonbinding proteins in the human proteome, and by the bias of rare alleles toward predicted carbohydrate-binding sites for nonsynonymous mutations from the 1000 genome project. SPRINT-CBH is available as an online server at http://sparks-lab.org/server/SPRINT-CBH .
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Affiliation(s)
- Ghazaleh Taherzadeh
- School of Information and Communication Technology and ‡Institute for Glycomics, Griffith University , Parklands Drive, Southport, Queensland 4215, Australia
| | - Yaoqi Zhou
- School of Information and Communication Technology and ‡Institute for Glycomics, Griffith University , Parklands Drive, Southport, Queensland 4215, Australia
| | - Alan Wee-Chung Liew
- School of Information and Communication Technology and ‡Institute for Glycomics, Griffith University , Parklands Drive, Southport, Queensland 4215, Australia
| | - Yuedong Yang
- School of Information and Communication Technology and ‡Institute for Glycomics, Griffith University , Parklands Drive, Southport, Queensland 4215, Australia
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69
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Guo F, Ding Y, Li SC, Shen C, Wang L. Protein–protein interface prediction based on hexagon structure similarity. Comput Biol Chem 2016; 63:83-88. [DOI: 10.1016/j.compbiolchem.2016.02.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Accepted: 02/01/2016] [Indexed: 01/17/2023]
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70
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Gao J, Zhang Q, Liu M, Zhu L, Wu D, Cao Z, Zhu R. bSiteFinder, an improved protein-binding sites prediction server based on structural alignment: more accurate and less time-consuming. J Cheminform 2016; 8:38. [PMID: 27403208 PMCID: PMC4939519 DOI: 10.1186/s13321-016-0149-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Accepted: 06/30/2016] [Indexed: 11/10/2022] Open
Abstract
MOTIVATION Protein-binding sites prediction lays a foundation for functional annotation of protein and structure-based drug design. As the number of available protein structures increases, structural alignment based algorithm becomes the dominant approach for protein-binding sites prediction. However, the present algorithms underutilize the ever increasing numbers of three-dimensional protein-ligand complex structures (bound protein), and it could be improved on the process of alignment, selection of templates and clustering of template. Herein, we built so far the largest database of bound templates with stringent quality control. And on this basis, bSiteFinder as a protein-binding sites prediction server was developed. RESULTS By introducing Homology Indexing, Chain Length Indexing, Stability of Complex and Optimized Multiple-Templates Clustering into our algorithm, the efficiency of our server has been significantly improved. Further, the accuracy was approximately 2-10 % higher than that of other algorithms for the test with either bound dataset or unbound dataset. For 210 bound dataset, bSiteFinder achieved high accuracies up to 94.8 % (MCC 0.95). For another 48 bound/unbound dataset, bSiteFinder achieved high accuracies up to 93.8 % for bound proteins (MCC 0.95) and 85.4 % for unbound proteins (MCC 0.72). Our bSiteFinder server is freely available at http://binfo.shmtu.edu.cn/bsitefinder/, and the source code is provided at the methods page. CONCLUSION An online bSiteFinder server is freely available at http://binfo.shmtu.edu.cn/bsitefinder/. Our work lays a foundation for functional annotation of protein and structure-based drug design. With ever increasing numbers of three-dimensional protein-ligand complex structures, our server should be more accurate and less time-consuming.Graphical Abstract bSiteFinder (http://binfo.shmtu.edu.cn/bsitefinder/) as a protein-binding sites prediction server was developed based on the largest database of bound templates so far with stringent quality control. By introducing Homology Indexing, Chain Length Indexing, Stability of Complex and Optimized Multiple-Templates Clustering into our algorithm, the efficiency of our server have been significantly improved. What's more, the accuracy was approximately 2-10 % higher than that of other algorithms for the test with either bound dataset or unbound dataset.
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Affiliation(s)
- Jun Gao
- Department of Bioinformatics, Tongji University, Shanghai, 200092 People's Republic of China ; School of Information Engineering, Shanghai Maritime University, Shanghai, 201306 People's Republic of China
| | - Qingchen Zhang
- Department of Bioinformatics, Tongji University, Shanghai, 200092 People's Republic of China
| | - Min Liu
- School of Information Engineering, Shanghai Maritime University, Shanghai, 201306 People's Republic of China
| | - Lixin Zhu
- Digestive Diseases and Nutrition Center, Department of Pediatrics, The State University of New York at Buffalo, Buffalo, NY 14260 USA ; Genomics, Environment, and Microbiome Community of Excellence, The State University of New York at Buffalo, Buffalo, NY 14203 USA ; Institute of Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032 People's Republic of China
| | - Dingfeng Wu
- Department of Bioinformatics, Tongji University, Shanghai, 200092 People's Republic of China
| | - Zhiwei Cao
- Department of Bioinformatics, Tongji University, Shanghai, 200092 People's Republic of China
| | - Ruixin Zhu
- Department of Bioinformatics, Tongji University, Shanghai, 200092 People's Republic of China
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71
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Sefid F, Rasooli I, Payandeh Z. Homology modeling of a Camelid antibody fragment against a conserved region of Acinetobacter baumannii biofilm associated protein (Bap). J Theor Biol 2016; 397:43-51. [DOI: 10.1016/j.jtbi.2016.02.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2015] [Revised: 12/26/2015] [Accepted: 02/10/2016] [Indexed: 10/22/2022]
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72
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Louros NN, Chrysina ED, Baltatzis GE, Patsouris ES, Hamodrakas SJ, Iconomidou VA. A common 'aggregation-prone' interface possibly participates in the self-assembly of human zona pellucida proteins. FEBS Lett 2016; 590:619-30. [PMID: 26879157 DOI: 10.1002/1873-3468.12099] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Revised: 02/01/2016] [Accepted: 02/05/2016] [Indexed: 02/03/2023]
Abstract
Human zona pellucida (ZP) is composed of four glycoproteins, namely ZP1, ZP2, ZP3 and ZP4. ZP proteins form heterodimers, which are incorporated into filaments through a common bipartite polymerizing component, designated as the ZP domain. The latter is composed of two individually folded subdomains, named ZP-N and ZP-C. Here, we have synthesized six 'aggregation-prone' peptides, corresponding to a common interface of human ZP2, ZP3 and ZP4. Experimental results utilizing electron microscopy, X-ray diffraction, ATR FT-IR spectroscopy and polarizing microscopy indicate that these peptides self-assemble forming fibrils with distinct amyloid-like features. Finally, by performing detailed modeling and docking, we attempt to shed some light in the self-assembly mechanism of human ZP proteins.
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Affiliation(s)
- Nikolaos N Louros
- Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Greece
| | - Evangelia D Chrysina
- Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece
| | | | | | - Stavros J Hamodrakas
- Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Greece
| | - Vassiliki A Iconomidou
- Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Greece
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73
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Taherzadeh G, Yang Y, Zhang T, Liew AW, Zhou Y. Sequence‐based prediction of protein–peptide binding sites using support vector machine. J Comput Chem 2016; 37:1223-9. [DOI: 10.1002/jcc.24314] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2015] [Accepted: 01/06/2016] [Indexed: 11/06/2022]
Affiliation(s)
- Ghazaleh Taherzadeh
- School of Information and Communication TechnologyGriffith UniversityParklands DriveSouthport Queensland4215 Australia
| | - Yuedong Yang
- School of Information and Communication TechnologyGriffith UniversityParklands DriveSouthport Queensland4215 Australia
- Institute for Glycomics, Griffith UniversityParklands DrSouthport Queensland4215 Australia
| | - Tuo Zhang
- Weill Cornell Medical College1300 York AvenueNew York, New York10065
| | - Alan Wee‐Chung Liew
- School of Information and Communication TechnologyGriffith UniversityParklands DriveSouthport Queensland4215 Australia
| | - Yaoqi Zhou
- School of Information and Communication TechnologyGriffith UniversityParklands DriveSouthport Queensland4215 Australia
- Institute for Glycomics, Griffith UniversityParklands DrSouthport Queensland4215 Australia
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74
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Esmaielbeiki R, Krawczyk K, Knapp B, Nebel JC, Deane CM. Progress and challenges in predicting protein interfaces. Brief Bioinform 2016; 17:117-31. [PMID: 25971595 PMCID: PMC4719070 DOI: 10.1093/bib/bbv027] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 03/18/2015] [Indexed: 12/31/2022] Open
Abstract
The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.
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75
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Maheshwari S, Brylinski M. Template-based identification of protein–protein interfaces using eFindSitePPI. Methods 2016; 93:64-71. [DOI: 10.1016/j.ymeth.2015.07.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 07/12/2015] [Accepted: 07/29/2015] [Indexed: 11/26/2022] Open
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76
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Local Geometry and Evolutionary Conservation of Protein Surfaces Reveal the Multiple Recognition Patches in Protein-Protein Interactions. PLoS Comput Biol 2015; 11:e1004580. [PMID: 26690684 PMCID: PMC4686965 DOI: 10.1371/journal.pcbi.1004580] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2015] [Accepted: 10/04/2015] [Indexed: 11/19/2022] Open
Abstract
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.
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77
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Xue LC, Dobbs D, Bonvin AMJJ, Honavar V. Computational prediction of protein interfaces: A review of data driven methods. FEBS Lett 2015; 589:3516-26. [PMID: 26460190 PMCID: PMC4655202 DOI: 10.1016/j.febslet.2015.10.003] [Citation(s) in RCA: 101] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 10/01/2015] [Accepted: 10/02/2015] [Indexed: 01/06/2023]
Abstract
Reliably pinpointing which specific amino acid residues form the interface(s) between a protein and its binding partner(s) is critical for understanding the structural and physicochemical determinants of protein recognition and binding affinity, and has wide applications in modeling and validating protein interactions predicted by high-throughput methods, in engineering proteins, and in prioritizing drug targets. Here, we review the basic concepts, principles and recent advances in computational approaches to the analysis and prediction of protein-protein interfaces. We point out caveats for objectively evaluating interface predictors, and discuss various applications of data-driven interface predictors for improving energy model-driven protein-protein docking. Finally, we stress the importance of exploiting binding partner information in reliably predicting interfaces and highlight recent advances in this emerging direction.
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Affiliation(s)
- Li C Xue
- Faculty of Science - Chemistry, Bijvoet Center for Biomolecular Research, Utrecht Univ., Utrecht 3584 CH, The Netherlands.
| | - Drena Dobbs
- Department of Genetics, Development & Cell Biology, Iowa State Univ., Ames, IA 50011, USA; Bioinformatics & Computational Biology Program, Iowa State Univ., Ames, IA 50011, USA
| | - Alexandre M J J Bonvin
- Faculty of Science - Chemistry, Bijvoet Center for Biomolecular Research, Utrecht Univ., Utrecht 3584 CH, The Netherlands
| | - Vasant Honavar
- College of Information Sciences & Technology, Pennsylvania State Univ., University Park, PA 16802, USA; Genomics & Bioinformatics Program, Pennsylvania State Univ., University Park, PA 16802, USA; Neuroscience Program, Pennsylvania State Univ., University Park, PA 16802, USA; The Huck Institutes of the Life Sciences, Pennsylvania State Univ., University Park, PA 16802, USA; Center for Big Data Analytics & Discovery Informatics, Pennsylvania State Univ., University Park, PA 16802, USA; Institute for Cyberscience, Pennsylvania State Univ., University Park, PA 16802, USA
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78
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Sriwastava BK, Basu S, Maulik U. Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:1394-1404. [PMID: 26684462 DOI: 10.1109/tcbb.2015.2401018] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
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.
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79
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Guo F, Li SC, Wei Z, Zhu D, Shen C, Wang L. Structural neighboring property for identifying protein-protein binding sites. BMC SYSTEMS BIOLOGY 2015; 9 Suppl 5:S3. [PMID: 26356630 PMCID: PMC4565107 DOI: 10.1186/1752-0509-9-s5-s3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Background The protein-protein interaction plays a key role in the control of many biological functions, such as drug design and functional analysis. Determination of binding sites is widely applied in molecular biology research. Therefore, many efficient methods have been developed for identifying binding sites. In this paper, we calculate structural neighboring property through Voronoi diagram. Using 6,438 complexes, we study local biases of structural neighboring property on interface. Results We propose a novel statistical method to extract interacting residues, and interacting patches can be clustered as predicted interface residues. In addition, structural neighboring property can be adopted to construct a new energy function, for evaluating docking solutions. It includes new statistical property as well as existing energy items. Comparing to existing methods, our approach improves overall Fnat value by at least 3%. On Benchmark v4.0, our method has average Irmsd value of 3.31Å and overall Fnat value of 63%, which improves upon Irmsd of 3.89 Å and Fnat of 49% for ZRANK, and Irmsd of 3.99Å and Fnat of 46% for ClusPro. On the CAPRI targets, our method has average Irmsd value of 3.46 Å and overall Fnat value of 45%, which improves upon Irmsd of 4.18 Å and Fnat of 40% for ZRANK, and Irmsd of 5.12 Å and Fnat of 32% for ClusPro. Conclusions Experiments show that our method achieves better results than some state-of-the-art methods for identifying protein-protein binding sites, with the prediction quality improved in terms of CAPRI evaluation criteria.
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80
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Identification of Protein–Protein Interactions by Detecting Correlated Mutation at the Interface. J Chem Inf Model 2015; 55:2042-9. [DOI: 10.1021/acs.jcim.5b00320] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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81
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Yang L, Connaris H, Potter JA, Taylor GL. Structural characterization of the carbohydrate-binding module of NanA sialidase, a pneumococcal virulence factor. BMC STRUCTURAL BIOLOGY 2015; 15:15. [PMID: 26289431 PMCID: PMC4546082 DOI: 10.1186/s12900-015-0042-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Accepted: 08/11/2015] [Indexed: 11/10/2022]
Abstract
BACKGROUND Streptococcus pneumoniae Neuraminidase A (NanA) is a multi-domain protein anchored to the bacterial surface. Upstream of the catalytic domain of NanA is a domain that conforms to the sialic acid-recognising CBM40 family of the CAZY (carbohydrate-active enzymes) database. This domain has been identified to play a critical role in allowing the bacterium to promote adhesion and invasion of human brain microvascular endothelial cells, and hence may play a key role in promoting bacterial meningitis. In addition, the CBM40 domain has also been reported to activate host chemokines and neutrophil recruitment during infection. RESULTS Crystal structures of both apo- and holo- forms of the NanA CBM40 domain (residues 121 to 305), have been determined to 1.8 Å resolution. The domain shares the fold of other CBM40 domains that are associated with sialidases. When in complex with α2,3- or α2,6-sialyllactose, the domain is shown to interact only with the terminal sialic acid. Significantly, a deep acidic pocket adjacent to the sialic acid-binding site is identified, which is occupied by a lysine from a symmetry-related molecule in the crystal. This pocket is adjacent to a region that is predicted to be involved in protein-protein interactions. CONCLUSIONS The structural data provide the details of linkage-independent sialyllactose binding by NanA CBM40 and reveal striking surface features that may hold the key to recognition of binding partners on the host cell surface. The structure also suggests that small molecules or sialic acid analogues could be developed to fill the acidic pocket and hence provide a new therapeutic avenue against meningitis caused by S. pneumoniae.
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Affiliation(s)
- Lei Yang
- Biomedical Sciences Research Complex, University of St Andrews, St Andrews, Fife, KY16 9ST, UK.
| | - Helen Connaris
- Biomedical Sciences Research Complex, University of St Andrews, St Andrews, Fife, KY16 9ST, UK.
| | - Jane A Potter
- Biomedical Sciences Research Complex, University of St Andrews, St Andrews, Fife, KY16 9ST, UK.
| | - Garry L Taylor
- Biomedical Sciences Research Complex, University of St Andrews, St Andrews, Fife, KY16 9ST, UK.
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82
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Hwang H, Petrey D, Honig B. A hybrid method for protein-protein interface prediction. Protein Sci 2015; 25:159-65. [PMID: 26178156 DOI: 10.1002/pro.2744] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2015] [Revised: 07/02/2015] [Accepted: 07/06/2015] [Indexed: 12/31/2022]
Abstract
The growing structural coverage of proteomes is making structural comparison a powerful tool for function annotation. Such template-based approaches are based on the observation that structural similarity is often sufficient to infer similar function. However, it seems clear that, in addition to structural similarity, the specific characteristics of a given protein should also be taken into account in predicting function. Here we describe PredUs 2.0, a method to predict regions on a protein surface likely to bind other proteins, that is, interfacial residues. PredUs 2.0 is based on the PredUs method that is entirely template-based and uses known binding sites in structurally similar proteins to predict interfacial residues. PredUs 2.0 uses a Bayesian approach to combine the template-based scoring of PredUs with a score that reflects the propensities of individual amino acids to be in interfaces. PredUs 2.0 includes a novel protein size dependent metric to determine the number of residues that should be reported as interfacial. PredUs 2.0 significantly outperforms PredUs as well as other published interface prediction methods.
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Affiliation(s)
- Howook Hwang
- Department of Systems Biology, Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics, Howard Hughes Medical Institute, Columbia University, 1130 St. Nicholas Ave., Room 815, New York, NY, 10032
| | - Donald Petrey
- Department of Systems Biology, Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics, Howard Hughes Medical Institute, Columbia University, 1130 St. Nicholas Ave., Room 815, New York, NY, 10032
| | - Barry Honig
- Department of Systems Biology, Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics, Howard Hughes Medical Institute, Columbia University, 1130 St. Nicholas Ave., Room 815, New York, NY, 10032
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83
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Afzal M, Khurshid S, Khalid R, Paracha RZ, Khan IH, Akhtar MW. Fusion of selected regions of mycobacterial antigens for enhancing sensitivity in serodiagnosis of tuberculosis. J Microbiol Methods 2015; 115:104-11. [PMID: 26068786 DOI: 10.1016/j.mimet.2015.06.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Revised: 06/04/2015] [Accepted: 06/06/2015] [Indexed: 11/27/2022]
Abstract
Serodiagnosis of tuberculosis requires detection of antibodies against multiple antigens of Mycobacterium tuberculosis, because antibody profiles differ among the patients. Using fusion proteins with epitopes from two or more antigens would facilitate in the detection of multiple antibodies. Fusion constructs tn1FbpC1-tnPstS1 and tn2FbpC1-tnPstS1 were produced by linking truncated regions of variable lengths from FbpC1 to the N-terminus of the truncated PstS1. Similarly a truncated fragment of HSP was linked to the N-terminus of a truncated fragment from FbpC1 to produce tnHSP-tn1FbpC1. ELISA analysis of the plasma samples of TB patients against tn2FbpC1-tnPstS1 showed 72.2% sensitivity which is nearly the same as the expected combined value for the two individual antigens. However, the sensitivity of tn1FbpC1-tnPstS1 was lowered to 60%. tnHSP-tn1FbpC1 showed 67.7% sensitivity which is slightly less than the expected combined value for the two individual antigens, but still significantly higher than that of each of the individual antigen. Data for secondary structure analysis by CD spectrometry was in reasonable agreement with the X-ray crystallographic data of the native proteins and the predicted structure of the fusion proteins. Comparative molecular modeling suggests that the epitopes of the constituent proteins are better exposed in tn2FbpC1-tnPstS1 as compared to those in tn1FbpC1-tnPstS1. Therefore, removal of the N-terminal non-epitopic region of FbpC1 from 34-96 amino acids seems to have unmasked at least some of the epitopes, resulting in greater sensitivity. The high level of sensitivity of tn2FbpC1-tnPstS1 and tnHSP-tn1FbpC1, not reported before, shows that these fusion proteins have great potential for use in serodiagnosis of tuberculosis.
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Affiliation(s)
- Madeeha Afzal
- School of Biological Sciences, University of the Punjab, Lahore 54590, Pakistan.
| | - Sana Khurshid
- School of Biological Sciences, University of the Punjab, Lahore 54590, Pakistan.
| | - Ruqyya Khalid
- School of Biological Sciences, University of the Punjab, Lahore 54590, Pakistan.
| | - Rehan Zafar Paracha
- Atta-Ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad 44000, Pakistan.
| | - Imran H Khan
- Department of Pathology and Laboratory Medicine, University of California, Davis 95616, USA.
| | - M Waheed Akhtar
- School of Biological Sciences, University of the Punjab, Lahore 54590, Pakistan.
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Shytaj IL, Savarino A. Cell-mediated anti-Gag immunity in pharmacologically induced functional cure of simian AIDS: a 'bottleneck effect'? J Med Primatol 2015; 44:227-40. [PMID: 26058990 DOI: 10.1111/jmp.12176] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/11/2015] [Indexed: 12/14/2022]
Abstract
BACKGROUND Administration of antiretroviral therapy and two experimental drugs, auranofin and buthionine sulfoximine (BSO), was previously shown to be followed by drug-free control of chronic SIVmac251 infection, decreased immune activation and increased cell-mediated anti-Gag responses. METHODS Phylogeny was analysed with Phylogeny.fr. Entropy was calculated with the specific tool of the HIV Sequence Database. The capsid Gag structure was computed using SPDBV. The bottleneck effect was simulated through an appropriate online tool. RESULTS The region of Gag predominantly targeted during control of SIVmac251 infection is highly conserved in primate lentiviruses and plays an important role in capsid architecture. Computer-aided simulations support the view that the preferential development of immune responses against this region is derived from a 'bottleneck effect' after restriction, by auranofin and BSO, of the activated lymphocyte pool. CONCLUSIONS Restriction of immune activation through auranofin/BSO may result in stochastic selection of cell clones targeting conserved epitopes leading to a functional cure-like condition.
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85
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Maheshwari S, Brylinski M. Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform 2015; 16:1025-34. [PMID: 25797794 DOI: 10.1093/bib/bbv009] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Indexed: 01/20/2023] Open
Abstract
It has been more than a decade since the completion of the Human Genome Project that provided us with a complete list of human proteins. The next obvious task is to figure out how various parts interact with each other. On that account, we review 10 methods for protein interface prediction, which are freely available as web servers. In addition, we comparatively evaluate their performance on a common data set comprising different quality target structures. We find that using experimental structures and high-quality homology models, structure-based methods outperform those using only protein sequences, with global template-based approaches providing the best performance. For moderate-quality models, sequence-based methods often perform better than those structure-based techniques that rely on fine atomic details. We note that post-processing protocols implemented in several methods quantitatively improve the results only for experimental structures, suggesting that these procedures should be tuned up for computer-generated models. Finally, we anticipate that advanced meta-prediction protocols are likely to enhance interface residue prediction. Notwithstanding further improvements, easily accessible web servers already provide the scientific community with convenient resources for the identification of protein-protein interaction sites.
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86
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Kumar V, Damodharan S, Pandaranayaka EP, Madathiparambil MG, Tennyson J. Molecular modeling andin-silicoengineering ofCardamom mosaic viruscoat protein for the presentation of immunogenic epitopes ofLeptospiraLipL32. J Biomol Struct Dyn 2015; 34:42-56. [DOI: 10.1080/07391102.2015.1009491] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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87
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Aumentado-Armstrong TT, Istrate B, Murgita RA. Algorithmic approaches to protein-protein interaction site prediction. Algorithms Mol Biol 2015; 10:7. [PMID: 25713596 PMCID: PMC4338852 DOI: 10.1186/s13015-015-0033-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2014] [Accepted: 01/07/2015] [Indexed: 12/19/2022] Open
Abstract
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.
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88
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Maheshwari S, Brylinski M. Prediction of protein-protein interaction sites from weakly homologous template structures using meta-threading and machine learning. J Mol Recognit 2015; 28:35-48. [DOI: 10.1002/jmr.2410] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Revised: 06/19/2014] [Accepted: 06/27/2014] [Indexed: 11/11/2022]
Affiliation(s)
- Surabhi Maheshwari
- Department of Biological Sciences; Louisiana State University; Baton Rouge LA 70803 USA
| | - Michal Brylinski
- Department of Biological Sciences; Louisiana State University; Baton Rouge LA 70803 USA
- Center for Computation & Technology; Louisiana State University; Baton Rouge LA 70803 USA
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89
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Xie ZR, Chen J, Zhao Y, Wu Y. Decomposing the space of protein quaternary structures with the interface fragment pair library. BMC Bioinformatics 2015; 16:14. [PMID: 25592649 PMCID: PMC4384354 DOI: 10.1186/s12859-014-0437-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Accepted: 12/18/2014] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The physical interactions between proteins constitute the basis of protein quaternary structures. They dominate many biological processes in living cells. Deciphering the structural features of interacting proteins is essential to understand their cellular functions. Similar to the space of protein tertiary structures in which discrete patterns are clearly observed on fold or sub-fold motif levels, it has been found that the space of protein quaternary structures is highly degenerate due to the packing of compact secondary structure elements at interfaces. Therefore, it is necessary to further decompose the protein quaternary structural space into a more local representation. RESULTS Here we constructed an interface fragment pair library from the current structure database of protein complexes. After structural-based clustering, we found that more than 90% of these interface fragment pairs can be represented by a limited number of highly abundant motifs. These motifs were further used to guide complex assembly. A large-scale benchmark test shows that the native-like binding is highly likely in the structural ensemble of modeled protein complexes that were built through the library. CONCLUSIONS Our study therefore presents supportive evidences that the space of protein quaternary structures can be represented by the combination of a small set of secondary-structure-based packing at binding interfaces. Finally, after future improvements such as adding sequence profiles, we expect this new library will be useful to predict structures of unknown protein-protein interactions.
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Affiliation(s)
- Zhong-Ru Xie
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
| | - Jiawen Chen
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
| | - Yilin Zhao
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
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90
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Forli S, Olson AJ. Computational challenges of structure-based approaches applied to HIV. Curr Top Microbiol Immunol 2015; 389:31-51. [PMID: 25711462 DOI: 10.1007/82_2015_432] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Here, we review some of the opportunities and challenges that we face in computational modeling of HIV therapeutic targets and structural biology, both in terms of methodology development and structure-based drug design (SBDD). Computational methods have provided fundamental support to HIV research since the initial structural studies, helping to unravel details of HIV biology. Computational models have proved to be a powerful tool to analyze and understand the impact of mutations and to overcome their structural and functional influence in drug resistance. With the availability of structural data, in silico experiments have been instrumental in exploiting and improving interactions between drugs and viral targets, such as HIV protease, reverse transcriptase, and integrase. Issues such as viral target dynamics and mutational variability, as well as the role of water and estimates of binding free energy in characterizing ligand interactions, are areas of active computational research. Ever-increasing computational resources and theoretical and algorithmic advances have played a significant role in progress to date, and we envision a continually expanding role for computational methods in our understanding of HIV biology and SBDD in the future.
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Affiliation(s)
- Stefano Forli
- MGL, Department of Integrative Structural and Computational Biology and HIV Interaction and Viral Evolution Center, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla, CA, 92037, USA
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91
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Dong Z, Wang K, Dang TKL, Gültas M, Welter M, Wierschin T, Stanke M, Waack S. CRF-based models of protein surfaces improve protein-protein interaction site predictions. BMC Bioinformatics 2014; 15:277. [PMID: 25124108 PMCID: PMC4150965 DOI: 10.1186/1471-2105-15-277] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2013] [Accepted: 08/01/2014] [Indexed: 11/13/2022] Open
Abstract
Background The identification of protein-protein interaction sites is a computationally challenging task and important for understanding the biology of protein complexes. There is a rich literature in this field. A broad class of approaches assign to each candidate residue a real-valued score that measures how likely it is that the residue belongs to the interface. The prediction is obtained by thresholding this score. Some probabilistic models classify the residues on the basis of the posterior probabilities. In this paper, we introduce pairwise conditional random fields (pCRFs) in which edges are not restricted to the backbone as in the case of linear-chain CRFs utilized by Li et al. (2007). In fact, any 3D-neighborhood relation can be modeled. On grounds of a generalized Viterbi inference algorithm and a piecewise training process for pCRFs, we demonstrate how to utilize pCRFs to enhance a given residue-wise score-based protein-protein interface predictor on the surface of the protein under study. The features of the pCRF are solely based on the interface predictions scores of the predictor the performance of which shall be improved. Results We performed three sets of experiments with synthetic scores assigned to the surface residues of proteins taken from the data set PlaneDimers compiled by Zellner et al. (2011), from the list published by Keskin et al. (2004) and from the very recent data set due to Cukuroglu et al. (2014). That way we demonstrated that our pCRF-based enhancer is effective given the interface residue score distribution and the non-interface residue score are unimodal. Moreover, the pCRF-based enhancer is also successfully applicable, if the distributions are only unimodal over a certain sub-domain. The improvement is then restricted to that domain. Thus we were able to improve the prediction of the PresCont server devised by Zellner et al. (2011) on PlaneDimers. Conclusions Our results strongly suggest that pCRFs form a methodological framework to improve residue-wise score-based protein-protein interface predictors given the scores are appropriately distributed. A prototypical implementation of our method is accessible at http://ppicrf.informatik.uni-goettingen.de/index.html.
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Affiliation(s)
| | | | | | | | | | | | | | - Stephan Waack
- Institute of Computer Science, University of Göttingen, Goldschmidtstr, 7, 37077 Göttingen, Germany.
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92
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Esmaielbeiki R, Nebel JC. Scoring docking conformations using predicted protein interfaces. BMC Bioinformatics 2014; 15:171. [PMID: 24906633 PMCID: PMC4057934 DOI: 10.1186/1471-2105-15-171] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2012] [Accepted: 05/29/2014] [Indexed: 12/22/2022] Open
Abstract
Background Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). Results First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. Conclusion Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations.
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Affiliation(s)
- Reyhaneh Esmaielbeiki
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.
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93
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Paracha RZ, Ali A, Ahmad J, Hussain R, Niazi U, Muhammad SA. Structural evaluation of BTK and PKCδ mediated phosphorylation of MAL at positions Tyr86 and Tyr106. Comput Biol Chem 2014; 51:22-35. [PMID: 24840642 DOI: 10.1016/j.compbiolchem.2014.04.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Revised: 04/03/2014] [Accepted: 04/07/2014] [Indexed: 01/02/2023]
Abstract
A number of diseases including sepsis, rheumatoid arthritis, diabetes, cardiovascular diseases and hyperinflammatory immune disorders have been associated with Toll like receptor (TLR) 2 and TLR4. Endogenous adaptor protein known as MyD88 adapter-like protein (MAL) bind exclusively to the cytosolic portions of TLR2 and TLR4 to initiate downstream signalling. Brutons tyrosine kinase (BTK) and protein kinase C delta (PKCδ) have been implicated to phosphorylate MAL and activate it to initiate downstream signalling. BTK has been associated with phosphorylation at positions Tyr86 and Tyr106, necessary for the activation of MAL but definite residual target of PKCδ in MAL is still to be explored. To produce a better understanding of the functional domains involved in the formation of MAL-kinase complexes, computer-aided studies were used to characterize the protein-protein interactions (PPIs) of phosphorylated BTK and PKCδ with MAL. Docking and physicochemical studies indicated that BTK was involved in close contact with Tyr86 and Tyr106 of MAL whereas PKCδ may phosphorylate Tyr106 only. Moreover, the electrostatics charge distribution of binding interfaces of BTK and PKCδ were distinct but compatible with respective regions of MAL. Our results implicate that position of Tyr86 is specifically phosphorylated by BTK whereas Tyr106 can be phosphorylated by competitive action of both BTK and PKCδ. Additionally, the residues of MAL which are necessary for interaction with TLR2, TLR4, MyD88 and SOCS-1 also play their roles in maintaining interaction with kinases and can be targeted in future to reduce TLR2 and TLR4 induced pathological responses.
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Affiliation(s)
- Rehan Zafar Paracha
- Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Amjad Ali
- Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
| | - Jamil Ahmad
- Research Center for Modeling and Simulation (RCMS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
| | - Riaz Hussain
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad 44000, Pakistan
| | - Umar Niazi
- IBERS, Aberystwyth University, Edward Llwyd Building, Penglais Campus, Aberystwyth, Ceredigion, Wales SY23 3FG, UK
| | - Syed Aun Muhammad
- Department of Pharmacy, COMSATS Institute of Information Technology Abbottabad, 22060, Pakistan
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94
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Bendell CJ, Liu S, Aumentado-Armstrong T, Istrate B, Cernek PT, Khan S, Picioreanu S, Zhao M, Murgita RA. Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor. BMC Bioinformatics 2014; 15:82. [PMID: 24661439 PMCID: PMC4021185 DOI: 10.1186/1471-2105-15-82] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2013] [Accepted: 02/14/2014] [Indexed: 11/14/2022] Open
Abstract
Background Transient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods’ restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement. Results The presence of unknown interaction sites as a result of limited knowledge about protein interactions in the testing set dramatically reduces prediction accuracy. Greater accuracy in labelling the data by enforcing higher interface site rates per domain resulted in an average 44% improvement across multiple machine learning algorithms. A set of 10 biologically unrelated proteins that were consistently predicted on with high accuracy emerged through our analysis. We identify seven features with the most predictive power over multiple datasets and machine learning algorithms. Through our analysis, we created a new predictor, RAD-T, that outperforms existing non-structurally specializing machine learning protein interface predictors, with an average 59% increase in MCC score on a dataset with a high number of interactions. Conclusion Current methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites. Changes to predictors’ training sets will be integral to the future progress of interface prediction by machine learning methods. We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Robert A Murgita
- Department of Microbiology and Immunology, McGill, Montreal, CA.
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95
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Wang DD, Wang R, Yan H. Fast prediction of protein–protein interaction sites based on Extreme Learning Machines. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2012.12.062] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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96
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Martin J. Benchmarking protein-protein interface predictions: Why you should care about protein size. Proteins 2014; 82:1444-52. [DOI: 10.1002/prot.24512] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2013] [Revised: 12/12/2013] [Accepted: 12/26/2013] [Indexed: 11/10/2022]
Affiliation(s)
- Juliette Martin
- Bases Moléculaires et Structurales des Systèmes Infectieux; CNRS, UMR 5086; Université Lyon 1; IBCP, 7 passage du Vercors F-69367 France
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97
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Xue LC, Jordan RA, EL-Manzalawy Y, Dobbs D, Honavar V. DockRank: ranking docked conformations using partner-specific sequence homology-based protein interface prediction. Proteins 2014; 82:250-67. [PMID: 23873600 PMCID: PMC4417613 DOI: 10.1002/prot.24370] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2012] [Revised: 06/27/2013] [Accepted: 07/09/2013] [Indexed: 12/11/2022]
Abstract
Selecting near-native conformations from the immense number of conformations generated by docking programs remains a major challenge in molecular docking. We introduce DockRank, a novel approach to scoring docked conformations based on the degree to which the interface residues of the docked conformation match a set of predicted interface residues. DockRank uses interface residues predicted by partner-specific sequence homology-based protein-protein interface predictor (PS-HomPPI), which predicts the interface residues of a query protein with a specific interaction partner. We compared the performance of DockRank with several state-of-the-art docking scoring functions using Success Rate (the percentage of cases that have at least one near-native conformation among the top m conformations) and Hit Rate (the percentage of near-native conformations that are included among the top m conformations). In cases where it is possible to obtain partner-specific (PS) interface predictions from PS-HomPPI, DockRank consistently outperforms both (i) ZRank and IRAD, two state-of-the-art energy-based scoring functions (improving Success Rate by up to 4-fold); and (ii) Variants of DockRank that use predicted interface residues obtained from several protein interface predictors that do not take into account the binding partner in making interface predictions (improving success rate by up to 39-fold). The latter result underscores the importance of using partner-specific interface residues in scoring docked conformations. We show that DockRank, when used to re-rank the conformations returned by ClusPro, improves upon the original ClusPro rankings in terms of both Success Rate and Hit Rate. DockRank is available as a server at http://einstein.cs.iastate.edu/DockRank/.
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Affiliation(s)
- Li C. Xue
- Bioinformatics and Computational Biology program, Iowa State University, Ames, Iowa
| | - Rafael A. Jordan
- Department of Computer Science, Iowa State University, Ames, Iowa
- Department of Systems and Computer Engineering, Pontificia Universidad Javeriana, Cali, Colombia
| | - Yasser EL-Manzalawy
- Department of Computer Science, Iowa State University, Ames, Iowa
- Department of Systems and Computer Engineering, Al-Azhar University, Cairo, Egypt
| | - Drena Dobbs
- Bioinformatics and Computational Biology program, Iowa State University, Ames, Iowa
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa
| | - Vasant Honavar
- Bioinformatics and Computational Biology program, Iowa State University, Ames, Iowa
- Department of Computer Science, Iowa State University, Ames, Iowa
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98
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de Moraes FR, Neshich IAP, Mazoni I, Yano IH, Pereira JGC, Salim JA, Jardine JG, Neshich G. Improving predictions of protein-protein interfaces by combining amino acid-specific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages. PLoS One 2014; 9:e87107. [PMID: 24489849 PMCID: PMC3904977 DOI: 10.1371/journal.pone.0087107] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2012] [Accepted: 12/22/2013] [Indexed: 11/18/2022] Open
Abstract
Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now integrated into the BlueStar STING suite of programs. Consequently, the prediction of protein-protein interfaces for all proteins available in the PDB is possible through STING_interfaces module, accessible at the following website: (http://www.cbi.cnptia.embrapa.br/SMS/predictions/index.html).
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Affiliation(s)
- Fábio R. de Moraes
- Biology Institute, University of Campinas, Campinas, São Paulo, Brazil
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
| | - Izabella A. P. Neshich
- Biology Institute, University of Campinas, Campinas, São Paulo, Brazil
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
| | - Ivan Mazoni
- Biology Institute, University of Campinas, Campinas, São Paulo, Brazil
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
| | - Inácio H. Yano
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
| | - José G. C. Pereira
- Biology Institute, University of Campinas, Campinas, São Paulo, Brazil
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
| | - José A. Salim
- School of Electrical and Computer Engineering, University of Campinas, Campinas, São Paulo, Brazil
| | - José G. Jardine
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
| | - Goran Neshich
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
- * E-mail:
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Template-based structure modeling of protein-protein interactions. Curr Opin Struct Biol 2013; 24:10-23. [PMID: 24721449 DOI: 10.1016/j.sbi.2013.11.005] [Citation(s) in RCA: 116] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2013] [Revised: 10/29/2013] [Accepted: 11/21/2013] [Indexed: 01/21/2023]
Abstract
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 protein-protein complex structure library and the lack of methods for distant homologous template identification and full-length complex structure refinement.
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Brylinski M. Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling. Proteome Sci 2013; 11:47. [PMID: 24321360 PMCID: PMC3866606 DOI: 10.1186/1477-5956-11-47] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2013] [Accepted: 12/03/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A growing body of evidence shows that gene products encoded by short open reading frames play key roles in numerous cellular processes. Yet, they are generally overlooked in genome assembly, escaping annotation because small protein-coding genes are difficult to predict computationally. Consequently, there are still a considerable number of small proteins whose functions are yet to be characterized. RESULTS To address this issue, we apply a collection of structural bioinformatics algorithms to infer molecular function of putative small proteins from the mouse proteome. Specifically, we construct 1,743 confident structure models of small proteins, which reveal a significant structural diversity with a noticeably high helical content. A subsequent structure-based function annotation of small protein models exposes 178,745 putative protein-protein interactions with the remaining gene products in the mouse proteome, 1,100 potential binding sites for small organic molecules and 987 metal-binding signatures. CONCLUSIONS These results strongly indicate that many small proteins adopt three-dimensional structures and are fully functional, playing important roles in transcriptional regulation, cell signaling and metabolism. Data collected through this work is freely available to the academic community at http://www.brylinski.org/content/databases to support future studies oriented on elucidating the functions of hypothetical small proteins.
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Affiliation(s)
- Michal Brylinski
- Department of Biological Sciences, Louisiana State University, 70803 Baton Rouge, LA, USA.
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