title: Statistical analysis and prediction of protein-protein interfaces.
abstract: Predicting protein-protein interfaces from a three-dimensional structure is a key task of computational structural proteomics. In contrast to geometrically distinct small molecule binding sites, protein-protein interface are notoriously difficult to predict. We generated a large nonredundant data set of 1494 true protein-protein interfaces using biological symmetry annotation where necessary. The data set was carefully analyzed and a Support Vector Machine was trained on a combination of a new robust evolutionary conservation signal with the local surface properties to predict protein-protein interfaces. Fivefold cross validation verifies the high sensitivity and selectivity of the model. As much as 97% of the predicted patches had an overlap with the true interface patch while only 22% of the surface residues were included in an average predicted patch. The model allowed the identification of potential new interfaces and the correction of mislabeled oligomeric states.
authors.affiliation: Molsoft LLC, San Diego, California, USA. bordner@ornl.gov
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Bordner
authors.forename: Ruben
authors.initials: R
authors.lastname: Abagyan
mesh_terms: D000818:Animals; D001665:Binding Sites; D001703:Biophysics / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D018360:Crystallography, X-Ray; D030562:Databases, Protein; D019281:Dimerization; D019143:Evolution, Molecular; D006801:Humans; D008958:Models, Molecular; D015233:Models, Statistical; D011485:Protein Binding; D011487:Protein Conformation; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D012372:ROC Curve; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein; D013499:Surface Properties
chemical_list: D011506:Proteins