title: PIE-efficient filters and coarse grained potentials for unbound protein-protein docking.
abstract: Identifying correct binding modes in a large set of models is an important step in protein-protein docking. We identified protein docking filter based on overlap area that significantly reduces the number of candidate structures that require detailed examination. We also developed potentials based on residue contacts and overlap areas using a comprehensive learning set of 640 two-chain protein complexes with mathematical programming. Our potential showed substantially better recognition capacity compared to other publicly accessible protein docking potentials in discriminating between native and nonnative binding modes on a large test set of 84 complexes independent of our training set. We were able to rank a near-native model on the top in 43 cases and within top 10 in 51 cases. We also report an atomic potential that ranks a near-native model on the top in 46 cases and within top 10 in 58 cases. Our filter+potential is well suited for selecting a small set of models to be refined to atomic resolution.
authors.affiliation: Department of Computer Science, Cornell University, Ithaca, New York 14853, USA.
authors.forename: D V S
authors.initials: DV
authors.lastname: Ravikant
authors.forename: Ron
authors.initials: R
authors.lastname: Elber
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008954:Models, Biological; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012984:Software
chemical_list: D011506:Proteins