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Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes.

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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Zarbafian S,Moghadasi M,Roshandelpoor A,Nan F,Li K,Vakli P,Vajda S,Kozakov D,Paschalidis IC

doi

10.1038/s41598-018-23982-3

subject

Has Abstract

pub_date

2018-04-12 00:00:00

pages

5896

issue

1

issn

2045-2322

pii

10.1038/s41598-018-23982-3

journal_volume

8

pub_type

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