title: Computational prediction of protein interfaces: A review of data driven methods.
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.
authors.affiliation: Faculty of Science - Chemistry, Bijvoet Center for Biomolecular Research, Utrecht Univ., Utrecht 3584 CH, The Netherlands. Electronic address: L.Xue@uu.nl.
authors.forename: Li C
authors.initials: LC
authors.lastname: Xue
authors.affiliation: Department of Genetics, Development & Cell Biology, Iowa State Univ., Ames, IA 50011, USA; Bioinformatics & Computational Biology Program, Iowa State Univ., Ames, IA 50011, USA.
authors.forename: Drena
authors.initials: D
authors.lastname: Dobbs
authors.affiliation: Faculty of Science - Chemistry, Bijvoet Center for Biomolecular Research, Utrecht Univ., Utrecht 3584 CH, The Netherlands.
authors.forename: Alexandre M J J
authors.initials: AM
authors.lastname: Bonvin
authors.affiliation: 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.
authors.forename: Vasant
authors.initials: V
authors.lastname: Honavar
mesh_terms: D019295:Computational Biology / Q000379:methods*; D062105:Molecular Docking Simulation; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D013379:Substrate Specificity
chemical_list: D011506:Proteins
keywords: Cross validation on instance level; Cross validation on protein level; Docking; Evaluation caveats; Machine learning; Partner-specific interface prediction; Protein–protein interaction