Protein-protein interactions: scoring schemes and binding affinity.

Protein-protein interactions mediate several cellular functions, which can be understood from the information obtained using the three-dimensional structures of protein-protein complexes and binding affinity data. This review focuses on computational aspects of predicting the best native-like complex structure and binding affinities. The first part covers the prediction of protein-protein complex structures and the advantages of conformational searching and scoring functions in protein-protein docking. The second part is devoted to various aspects of protein-protein interaction thermodynamics, such as databases for binding affinities and other thermodynamic parameters, computational methods to predict the binding affinity using either the three-dimensional structures of complexes or amino acid sequences, and change in binding affinities of the complexes upon mutations. We provide the latest developments on protein-protein docking and binding affinity studies along with a list of available computational resources for understanding protein-protein interactions.

[1]  Frédéric Cazals,et al.  High‐resolution crystal structures leverage protein binding affinity predictions , 2016, Proteins.

[2]  Nurit Haspel,et al.  Accurate Prediction of Docked Protein Structure Similarity , 2015, J. Comput. Biol..

[3]  Martin Zacharias,et al.  iATTRACT: Simultaneous global and local interface optimization for protein–protein docking refinement , 2015, Proteins: Structure, Function, and Bioinformatics.

[4]  Holger Gohlke,et al.  DrugScorePPI Knowledge-Based Potentials Used as Scoring and Objective Function in Protein-Protein Docking , 2014, PloS one.

[5]  J. Janin.  A minimal model of protein–protein binding affinities , 2014, Protein science : a publication of the Protein Society.

[6]  F. Tian,et al.  Structure-based prediction of protein–protein binding affinity with consideration of allosteric effect , 2011, Amino Acids.

[7]  P. Kastritis,et al.  On the binding affinity of macromolecular interactions: daring to ask why proteins interact , 2013, Journal of The Royal Society Interface.

[8]  Chao Yang,et al.  Biomacromolecular quantitative structure–activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein–protein binding affinity , 2013, Journal of Computer-Aided Molecular Design.

[9]  Marc F. Lensink,et al.  Score_set: A CAPRI benchmark for scoring protein complexes , 2014, Proteins.

[10]  Raphael A. G. Chaleil,et al.  Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2. , 2015, Journal of molecular biology.

[11]  Edward W. Lowe,et al.  Computational Methods in Drug Discovery , 2014, Pharmacological Reviews.

[12]  Alexandre M J J Bonvin,et al.  Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes. , 2016, Protein engineering, design & selection : PEDS.

[13]  Pedro A Fernandes,et al.  A new scoring function for protein-protein docking that identifies native structures with unprecedented accuracy. , 2015, Physical chemistry chemical physics : PCCP.

[14]  Alexandre M J J Bonvin,et al.  Are scoring functions in protein-protein docking ready to predict interactomes? Clues from a novel binding affinity benchmark. , 2010, Journal of proteome research.

[15]  P. Bork,et al.  A method and server for predicting damaging missense mutations , 2010, Nature Methods.

[16]  Petras J. Kundrotas,et al.  Genome-Wide Structural Modeling of Protein-Protein Interactions , 2016, ISBRA.

[17]  M. Gromiha,et al.  Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants. , 2016, Methods in molecular biology.

[18]  C. Bajaj,et al.  Protein-Protein Docking with F2Dock 2.0 and GB-Rerank , 2013, PloS one.

[19]  Anna Vangone,et al.  Contacts-based prediction of binding affinity in protein–protein complexes , 2015, eLife.

[20]  Juan Fernández-Recio,et al.  SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models , 2012, Bioinform..

[21]  Julia M. Shifman,et al.  How Structure Defines Affinity in Protein-Protein Interactions , 2014, PloS one.

[22]  James R. Apgar,et al.  AB‐Bind: Antibody binding mutational database for computational affinity predictions , 2016, Protein science : a publication of the Protein Society.

[23]  Daniel F. A. R. Dourado,et al.  Modeling and fitting protein-protein complexes to predict change of binding energy , 2016, Scientific Reports.

[24]  Andrew B. Feldman,et al.  Minimalistic predictor of protein binding energy: contribution of solvation factor to protein binding. , 2015, Biophysical journal.

[25]  Jie Li,et al.  PDB-wide collection of binding data: current status of the PDBbind database , 2015, Bioinform..

[26]  Zhiping Weng,et al.  A benchmark testing ground for integrating homology modeling and protein docking , 2017, Proteins.

[27]  M. Michael Gromiha,et al.  Folding RaCe: a robust method for predicting changes in protein folding rates upon point mutations , 2015, Bioinform..

[28]  Mieczyslaw Torchala,et al.  The scoring of poses in protein-protein docking: current capabilities and future directions , 2013, BMC Bioinformatics.

[29]  David T. W. Jones,et al.  Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions , 2014, Nucleic acids research.

[30]  Chee Keong Kwoh,et al.  IFACEwat: the interfacial water-implemented re-ranking algorithm to improve the discrimination of near native structures for protein rigid docking , 2014, BMC Bioinformatics.

[31]  Shinn-Ying Ho,et al.  Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes , 2015, BMC Bioinformatics.

[32]  M. Michael Gromiha,et al.  Protein-protein binding affinity prediction from amino acid sequence , 2014, Bioinform..

[33]  Shengyou Huang,et al.  Determination of an effective scoring function for RNA–RNA interactions with a physics-based double-iterative method , 2018, Nucleic acids research.

[34]  Isaure Chauvot de Beauchêne,et al.  A web interface for easy flexible protein-protein docking with ATTRACT. , 2015, Biophysical journal.

[35]  Ruth Nussinov,et al.  An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles. , 2014, Progress in biophysics and molecular biology.

[36]  Yang Zhang,et al.  Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles , 2015, PLoS Comput. Biol..

[37]  Marianne Rooman,et al.  BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations , 2013, Nucleic Acids Res..

[38]  Daniel F. A. R. Dourado,et al.  A multiscale approach to predicting affinity changes in protein–protein interfaces , 2014, Proteins.

[39]  Hege S. Beard,et al.  Applying Physics-Based Scoring to Calculate Free Energies of Binding for Single Amino Acid Mutations in Protein-Protein Complexes , 2013, PloS one.

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

[41]  M Michael Gromiha,et al.  Analysis of protein-protein interaction networks based on binding affinity. , 2016, Current protein & peptide science.

[42]  Emil Alexov,et al.  Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method , 2015, PLoS Comput. Biol..

[43]  Ilya A Vakser,et al.  Protein-protein docking: from interaction to interactome. , 2014, Biophysical journal.

[44]  Zhiping Weng,et al.  Prediction of protein–protein binding free energies , 2012, Protein science : a publication of the Protein Society.

[45]  R. Sowdhamini,et al.  Structural Interface Parameters Are Discriminatory in Recognising Near-Native Poses of Protein-Protein Interactions , 2014, PloS one.

[46]  G C P van Zundert,et al.  The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes. , 2016, Journal of molecular biology.

[47]  Iain H. Moal,et al.  Protein-protein binding affinity prediction on a diverse set of structures , 2011, Bioinform..

[48]  Jing Zhao,et al.  The integration of weighted gene association networks based on information entropy , 2017, PloS one.

[49]  Mingming Jia,et al.  COSMIC: exploring the world's knowledge of somatic mutations in human cancer , 2014, Nucleic Acids Res..

[50]  A. Olson,et al.  Proteome-wide covalent ligand discovery in native biological systems , 2016, Nature.

[51]  Yuri Matsuzaki,et al.  MEGADOCK 4.0: an ultra–high-performance protein–protein docking software for heterogeneous supercomputers , 2014, Bioinform..

[52]  Douglas E. V. Pires,et al.  mCSM-AB: a web server for predicting antibody–antigen affinity changes upon mutation with graph-based signatures , 2016, Nucleic Acids Res..

[53]  M. Michael Gromiha,et al.  PINT: Protein–protein Interactions Thermodynamic Database , 2005, Nucleic Acids Res..

[54]  E. Alexov,et al.  SAAMBE: Webserver to Predict the Charge of Binding Free Energy Caused by Amino Acids Mutations , 2016, International journal of molecular sciences.

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

[56]  Igor Jurisica,et al.  In silico prediction of physical protein interactions and characterization of interactome orphans , 2014, Nature Methods.

[57]  P. Ng,et al.  SIFT missense predictions for genomes , 2015, Nature Protocols.

[58]  Yuri Matsuzaki,et al.  Protein-protein Interaction Network Prediction by Using Rigid-Body Docking Tools: Application to Bacterial Chemotaxis , 2013, Protein and peptide letters.

[59]  Philip M. Kim,et al.  Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation , 2014, PloS one.

[60]  G. Basu,et al.  Protein–protein association rates captured in a single geometric parameter , 2015, Proteins.

[61]  Daisuke Kihara,et al.  Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: A CASP‐CAPRI experiment , 2016, Proteins.

[62]  Pamela F. Jones,et al.  VORFFIP-Driven Dock: V-D2OCK, a Fast and Accurate Protein Docking Strategy , 2015, PloS one.

[63]  Juan Fernández-Recio,et al.  CCharPPI web server: computational characterization of protein-protein interactions from structure , 2015, Bioinform..

[64]  Ricardo Villamarín-Salomón,et al.  ClinVar: public archive of interpretations of clinically relevant variants , 2015, Nucleic Acids Res..

[65]  Anna R. Panchenko,et al.  MutaBind estimates and interprets the effects of sequence variants on protein–protein interactions , 2016, Nucleic Acids Res..

[66]  Douglas E. V. Pires,et al.  mCSM: predicting the effects of mutations in proteins using graph-based signatures , 2013, Bioinform..

[67]  Limsoon Wong,et al.  Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions , 2014, Biology Direct.

[68]  M. Gromiha,et al.  Discrimination of driver and passenger mutations in epidermal growth factor receptor in cancer. , 2015, Mutation research.

[69]  M Michael Gromiha,et al.  Feature selection and classification of protein–protein complexes based on their binding affinities using machine learning approaches , 2014, Proteins.

[70]  Xuemei Pu,et al.  Prediction of protein–protein binding affinity using diverse protein–protein interface features , 2014 .

[71]  Kurt S. Thorn,et al.  ASEdb: a database of alanine mutations and their effects on the free energy of binding in protein interactions , 2001, Bioinform..