title: Predicted protein-protein interaction sites from local sequence information.
abstract: Protein-protein interactions are facilitated by a myriad of residue-residue contacts on the interacting proteins. Identifying the site of interaction in the protein is a key for deciphering its functional mechanisms, and is crucial for drug development. Many studies indicate that the compositions of contacting residues are unique. Here, we describe a neural network that identifies protein-protein interfaces from sequence. For the most strongly predicted sites (in 34 of 333 proteins), 94% of the predictions were confirmed experimentally. When 70% of our predictions were right, we correctly predicted at least one interaction site in 20% of the complexes (66/333). These results indicate that the prediction of some interaction sites from sequence alone is possible. Incorporating evolutionary and predicted structural information may improve our method. However, even at this early stage, our tool might already assist wet-lab biology.
authors.affiliation: CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA. ofran@cubic.bioc.columbia.edu
authors.forename: Yanay
authors.initials: Y
authors.lastname: Ofran
authors.forename: Burkhard
authors.initials: B
authors.lastname: Rost
mesh_terms: D001665:Binding Sites; D002138:Calibration; D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011485:Protein Binding; D011487:Protein Conformation