Great interactions: How binding incorrect partners can teach us about protein recognition and function.
Protein-protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross-docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross-docking predictions using the area under the specificity-sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding-site predictions resulting from the cross-docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408-1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France.
Lydie
L
Vamparys
Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France.
Benoist
B
Laurent
Sorbonne Universités, UPMC Univ-Paris 6, CNRS UMR7238, Laboratoire De Biologie Computationnelle Et Quantitative, 15 Rue De L'Ecole De Médecine, Paris, 75006, France.|Institut Universitaire De France, Paris, 75005, France.
Alessandra
A
Carbone
Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France. sacquin@ibpc.fr.
Sophie
S
Sacquin-Mora
D019540:Area Under Curve; D001665:Binding Sites; D016000:Cluster Analysis; D019295:Computational Biology / Q000706:statistics & numerical data*; D030562:Databases, Protein; D015233:Models, Statistical; D062105:Molecular Docking Simulation; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data*; D011506:Proteins / Q000737:chemistry*; D012372:ROC Curve
D011506:Proteins
binding sites prediction; coarse grain models; docking; protein-protein interaction; protein-protein interfaces