Identifying protein-protein interface via a novel multi-scale local sequence and structural representation

Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface. In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average Irmsd value of 3.28Å and overall Fnat value of 63%, which improves upon Irmsd of 3.89Å and Fnat of 49% for ZRANK, and Irmsd of 3.99Å and Fnat of 46% for ClusPro. On CAPRI targets, our method achieves average Irmsd value of 3.45Å and overall Fnat value of 46%, which improves upon Irmsd of 4.18Å and Fnat of 40% for ZRANK, and Irmsd of 5.12Å and Fnat of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively. Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface.

[1]  Huan-Xiang Zhou,et al.  Interaction-site prediction for protein complexes: a critical assessment , 2007, Bioinform..

[2]  Zhao Li,et al.  Identification of Protein-Protein Interactions by Detecting Correlated Mutation at the Interface , 2015, J. Chem. Inf. Model..

[3]  R. Abagyan,et al.  Optimal docking area: A new method for predicting protein–protein interaction sites , 2004, Proteins.

[4]  R. Nussinov,et al.  Protein–protein interactions: Structurally conserved residues distinguish between binding sites and exposed protein surfaces , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Dusanka Janezic,et al.  ProBiS: a web server for detection of structurally similar protein binding sites , 2010, Nucleic Acids Res..

[6]  Lusheng Wang,et al.  Detecting Protein Conformational Changes in Interactions via Scaling Known Structures , 2013, J. Comput. Biol..

[7]  Cândida G. Silva,et al.  Enhancing Scoring Performance of Docking-Based Virtual Screening Through Machine Learning , 2016 .

[8]  M. Karplus,et al.  CHARMM: A program for macromolecular energy, minimization, and dynamics calculations , 1983 .

[9]  Pablo Chacón,et al.  FRODOCK 2.0: fast protein-protein docking server , 2016, Bioinform..

[10]  Ruth Nussinov,et al.  Automatic prediction of protein interactions with large scale motion , 2007, Proteins.

[11]  Jijun Tang,et al.  Identification of Protein–Protein Interactions via a Novel Matrix-Based Sequence Representation Model with Amino Acid Contact Information , 2016, International journal of molecular sciences.

[12]  Sam Ansari,et al.  Statistical analysis of predominantly transient protein–protein interfaces , 2005, Proteins.

[13]  R. Nussinov,et al.  Hydrogen bonds and salt bridges across protein-protein interfaces. , 1997, Protein engineering.

[14]  Fei Guo,et al.  Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier , 2017, Artif. Intell. Medicine.

[15]  L. Holm,et al.  The Pfam protein families database , 2005, Nucleic Acids Res..

[16]  O. Schueler‐Furman,et al.  Progress in protein–protein docking: Atomic resolution predictions in the CAPRI experiment using RosettaDock with an improved treatment of side‐chain flexibility , 2005, Proteins.

[17]  Doheon Lee,et al.  A feature-based approach to modeling protein–protein interaction hot spots , 2009, Nucleic acids research.

[18]  Jijun Tang,et al.  Predicting protein-protein interactions via multivariate mutual information of protein sequences , 2016, BMC Bioinformatics.

[19]  Lusheng Wang,et al.  Identifying protein-protein binding sites with a combined energy function. , 2014, Current protein & peptide science.

[20]  Xue-wen Chen,et al.  On Position-Specific Scoring Matrix for Protein Function Prediction , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[21]  Lusheng Wang,et al.  Probabilistic Models for Capturing More Physicochemical Properties on Protein-Protein Interface , 2014, J. Chem. Inf. Model..

[22]  Zhiping Weng,et al.  Protein–protein docking benchmark version 4.0 , 2010, Proteins.

[23]  Xiangrong Liu,et al.  An Empirical Study of Features Fusion Techniques for Protein-Protein Interaction Prediction , 2016 .

[24]  Alessandra Carbone,et al.  Protein–protein interaction specificity is captured by contact preferences and interface composition , 2017, Bioinform..

[25]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[26]  Sandor Vajda,et al.  CAPRI: A Critical Assessment of PRedicted Interactions , 2003, Proteins.

[27]  Sandor Vajda,et al.  ClusPro: an automated docking and discrimination method for the prediction of protein complexes , 2004, Bioinform..

[28]  E. Birney,et al.  Pfam: the protein families database , 2013, Nucleic Acids Res..

[29]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[30]  Song Liu,et al.  Protein binding site prediction using an empirical scoring function , 2006, Nucleic acids research.

[31]  H. Wolfson,et al.  FiberDock: Flexible induced‐fit backbone refinement in molecular docking , 2010, Proteins.

[32]  Ernst-Walter Knapp,et al.  ProPairs: A Data Set for Protein-Protein Docking , 2015, J. Chem. Inf. Model..

[33]  Xing Gao,et al.  An Improved Protein Structural Classes Prediction Method by Incorporating Both Sequence and Structure Information , 2015, IEEE Transactions on NanoBioscience.

[34]  Pritish Kumar Varadwaj,et al.  DeepInteract: Deep Neural Network Based Protein-Protein Interaction Prediction Tool , 2017 .

[35]  Pierre Tufféry,et al.  InterEvDock: a docking server to predict the structure of protein–protein interactions using evolutionary information , 2016, Nucleic Acids Res..

[36]  Zhiping Weng,et al.  ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers , 2014, Bioinform..

[37]  Dusanka Janezic,et al.  ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment , 2010, Bioinform..

[38]  Lusheng Wang,et al.  Protein-protein interface prediction based on hexagon structure similarity , 2016, Comput. Biol. Chem..

[39]  Mark N. Wass,et al.  Challenges for the prediction of macromolecular interactions. , 2011, Current opinion in structural biology.

[40]  Ruth Nussinov,et al.  PatchDock and SymmDock: servers for rigid and symmetric docking , 2005, Nucleic Acids Res..

[41]  David R. Westhead,et al.  Improved prediction of protein-protein binding sites using a support vector machines approach. , 2005, Bioinformatics.

[42]  Jijun Tang,et al.  Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information , 2017, Inf. Sci..

[43]  S. Henikoff,et al.  Amino acid substitution matrices from protein blocks. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[44]  Berk Hess,et al.  GROMACS 3.0: a package for molecular simulation and trajectory analysis , 2001 .

[45]  Samy O Meroueh,et al.  A Computational Investigation of Small-Molecule Engagement of Hot Spots at Protein-Protein Interaction Interfaces , 2017, J. Chem. Inf. Model..

[46]  M. Schroeder,et al.  Using protein binding site prediction to improve protein docking. , 2008, Gene.

[47]  Mieczyslaw Torchala,et al.  SwarmDock: a server for flexible protein-protein docking , 2013, Bioinform..

[48]  Yu-Dong Cai,et al.  Prediction of protein-peptide interaction with nearest neighbor algorithm , 1969 .

[49]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[50]  Ruth Nussinov,et al.  Geometry‐based flexible and symmetric protein docking , 2005, Proteins.

[51]  R. Raz,et al.  ProMate: a structure based prediction program to identify the location of protein-protein binding sites. , 2004, Journal of molecular biology.

[52]  Carles Pons,et al.  pyDockWEB: a web server for rigid-body protein-protein docking using electrostatics and desolvation scoring , 2013, Bioinform..

[53]  Huan-Xiang Zhou,et al.  meta-PPISP: a meta web server for protein-protein interaction site prediction , 2007, Bioinform..

[54]  Z. Weng,et al.  Integrating atom‐based and residue‐based scoring functions for protein–protein docking , 2011, Protein science : a publication of the Protein Society.

[55]  Lusheng Wang,et al.  Protein-protein binding site identification by enumerating the configurations , 2012, BMC Bioinformatics.

[56]  Xin Yan,et al.  Linear Regression Analysis: Theory and Computing , 2009 .

[57]  Ying Gao,et al.  DOCKGROUND protein-protein docking decoy set , 2008, Bioinform..

[58]  Miriam Eisenstein,et al.  Electrostatics in protein–protein docking , 2002, Protein science : a publication of the Protein Society.

[59]  Zhiping Weng,et al.  A combination of rescoring and refinement significantly improves protein docking performance , 2008, Proteins.

[60]  C. Dominguez,et al.  HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. , 2003, Journal of the American Chemical Society.

[61]  Daming Zhu,et al.  Structural neighboring property for identifying protein-protein binding sites , 2015, BMC Systems Biology.

[62]  Haim J. Wolfson,et al.  SnapDock—template-based docking by Geometric Hashing , 2017, Bioinform..

[63]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[64]  M. Madhusudhan,et al.  Computational modeling of protein assemblies. , 2017, Current opinion in structural biology.

[65]  Siguna Mueller,et al.  A covert authentication and security solution for GMOs , 2016, BMC Bioinformatics.