학술논문
Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes.
Document Type
Academic Journal
Author
Zarbafian S; Department of Mechanical Engineering, Boston University, Boston, Massachusetts, United States of America.; Moghadasi M; Division of Systems Engineering, Boston University, Boston, Massachusetts, United States of America.; Roshandelpoor A; Division of Systems Engineering, Boston University, Boston, Massachusetts, United States of America.; Nan F; Division of Systems Engineering, Boston University, Boston, Massachusetts, United States of America.; Li K; Division of Systems Engineering, Boston University, Boston, Massachusetts, United States of America.; Vakli P; Division of Systems Engineering, Boston University, Boston, Massachusetts, United States of America.; Department of Mechanical Engineering, Boston University, Boston, Massachusetts, United States of America.; Vajda S; Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America. vajda@bu.edu.; Kozakov D; Department of Applied Mathematics and Statistics and Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, United States of America. midas@laufercenter.org.; Paschalidis IC; Division of Systems Engineering, Boston University, Boston, Massachusetts, United States of America. yannisp@bu.edu.; Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America. yannisp@bu.edu.; Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts, United States of America. yannisp@bu.edu.; 8 Saint Mary's St., Boston, MA, 02215, United States of America. yannisp@bu.edu.
Source
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Subject
Language
English
Abstract
We propose a novel stochastic global optimization algorithm with applications to the refinement stage of protein docking prediction methods. Our approach can process conformations sampled from multiple clusters, each roughly corresponding to a different binding energy funnel. These clusters are obtained using a density-based clustering method. In each cluster, we identify a smooth "permissive" subspace which avoids high-energy barriers and then underestimate the binding energy function using general convex polynomials in this subspace. We use the underestimator to bias sampling towards its global minimum. Sampling and subspace underestimation are repeated several times and the conformations sampled at the last iteration form a refined ensemble. We report computational results on a comprehensive benchmark of 224 protein complexes, establishing that our refined ensemble significantly improves the quality of the conformations of the original set given to the algorithm. We also devise a method to enhance the ensemble from which near-native models are selected.