Computational prediction of protein interfaces: A review of data driven methods

• Published in: FEBS letters 2015-11, Vol.589 (23), p.3516-3526
• Main Authors: Xue, Li C., Dobbs, Drena, Bonvin, Alexandre M.J.J., Honavar, Vasant
• Format: Article
• Language: English
• Subjects: Computational Biology - methods; Cross validation on instance level; Cross validation on protein level; Docking; Evaluation caveats; Machine learning; Molecular Docking Simulation; Partner-specific interface prediction; Protein Binding; Proteins - chemistry; Proteins - metabolism; Protein–protein interaction; Substrate Specificity
• ISSN: 0014-5793; 1873-3468
• DOI: 10.1016/j.febslet.2015.10.003

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.