title: Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking.
abstract: Protein-protein docking protocols aim to predict the structures of protein-protein complexes based on the structure of individual partners. Docking protocols usually include several steps of sampling, clustering, refinement and re-scoring. The scoring step is one of the bottlenecks in the performance of many state-of-the-art protocols. The performance of scoring functions depends on the quality of the generated structures and its coupling to the sampling algorithm. A tool kit, GRADSCOPT (GRid Accelerated Directly SCoring OPTimizing), was designed to allow rapid development and optimization of different knowledge-based scoring potentials for specific objectives in protein-protein docking. Different atomistic and coarse-grained potentials can be created by a grid-accelerated directly scoring dependent Monte-Carlo annealing or by a linear regression optimization. We demonstrate that the scoring functions generated by our approach are similar to or even outperform state-of-the-art scoring functions for predicting near-native solutions. Of additional importance, we find that potentials specifically trained to identify the native bound complex perform rather poorly on identifying acceptable or medium quality (near-native) solutions. In contrast, atomistic long-range contact potentials can increase the average fraction of near-native poses by up to a factor 2.5 in the best scored 1% decoys (compared to existing scoring), emphasizing the need of specific docking potentials for different steps in the docking protocol.
authors.affiliation: Physik Department T38, Technische Universität München, James-Franck-Straße, Garching, Germany.
authors.forename: Alexander
authors.initials: A
authors.lastname: Sasse
authors.affiliation: Physik Department T38, Technische Universität München, James-Franck-Straße, Garching, Germany.
authors.forename: Sjoerd J
authors.initials: SJ
authors.lastname: de Vries
authors.affiliation: Physik Department T38, Technische Universität München, James-Franck-Straße, Garching, Germany.
authors.forename: Christina E M
authors.initials: CE
authors.lastname: Schindler
authors.affiliation: Physik Department T38, Technische Universität München, James-Franck-Straße, Garching, Germany.
authors.forename: Isaure Chauvot
authors.initials: IC
authors.lastname: de Beauchêne
authors.affiliation: Physik Department T38, Technische Universität München, James-Franck-Straße, Garching, Germany.
authors.forename: Martin
authors.identifier: http://orcid.org/0000-0001-5163-2663
authors.initials: M
authors.lastname: Zacharias
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D051188:Knowledge Bases; D016014:Linear Models; D008956:Models, Chemical; D008958:Models, Molecular; D062105:Molecular Docking Simulation / Q000379:methods*; D009010:Monte Carlo Method; D011485:Protein Binding; D011487:Protein Conformation; D025941:Protein Interaction Mapping / Q000379:methods*; D012984:Software