title: Refining near-native protein-protein docking decoys by local resampling and energy minimization.
abstract: How to refine a near-native structure to make it closer to its native conformation is an unsolved problem in protein-structure and protein-protein complex-structure prediction. In this article, we first test several scoring functions for selecting locally resampled near-native protein-protein docking conformations and then propose a computationally efficient protocol for structure refinement via local resampling and energy minimization. The proposed method employs a statistical energy function based on a Distance-scaled Ideal-gas REference state (DFIRE) as an initial filter and an empirical energy function EMPIRE (EMpirical Protein-InteRaction Energy) for optimization and re-ranking. Significant improvement of final top-1 ranked structures over initial near-native structures is observed in the ZDOCK 2.3 decoy set for Benchmark 1.0 (74% whose global rmsd reduced by 0.5 A or more and only 7% increased by 0.5 A or more). Less significant improvement is observed for Benchmark 2.0 (38% versus 33%). Possible reasons are discussed.
authors.affiliation: Indiana University School of Informatics, Indiana University-Purdue University, Indianapolis, 46202, USA.
authors.forename: Shide
authors.initials: S
authors.lastname: Liang
authors.forename: Guangce
authors.initials: G
authors.lastname: Wang
authors.forename: Yaoqi
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D001665:Binding Sites; D019295:Computational Biology; D030562:Databases, Protein; D008958:Models, Molecular; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D013816:Thermodynamics
chemical_list: D011506:Proteins