title: Prediction of protein interaction sites from sequence profile and residue neighbor list.
abstract: Protein-protein interaction sites are predicted from a neural network with sequence profiles of neighboring residues and solvent exposure as input. The network was trained on 615 pairs of nonhomologous complex-forming proteins. Tested on a different set of 129 pairs of nonhomologous complex-forming proteins, 70% of the 11,004 predicted interface residues are actually located in the interfaces. These 7732 correctly predicted residues account for 65% of the 11,805 residues making up the 129 interfaces. The main strength of the network predictor lies in the fact that neighbor lists and solvent exposure are relatively insensitive to structural changes accompanying complex formation. As such, it performs equally well with bound or unbound structures of the proteins. For a set of 35 test proteins, when the input was calculated from the bound and unbound structures, the correct fractions of the predicted interface residues were 69 and 70%, respectively.
authors.affiliation: Department of Physics, Drexel University, Philadelphia, Pennsylvania 19104, USA. hxzhou@einstein.drexel.edu
authors.forename: H X
authors.initials: HX
authors.lastname: Zhou
authors.forename: Y
authors.initials: Y
authors.lastname: Shan
mesh_terms: D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D008958:Models, Molecular; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
chemical_list: D011506:Proteins