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Effective protein conformational sampling based on predicted torsion angles
Author(s) -
Yang Yuedong,
Zhou Yaoqi
Publication year - 2016
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.24285
Subject(s) - bottleneck , torsion (gastropod) , protein structure prediction , sampling (signal processing) , dihedral angle , algorithm , function (biology) , statistical physics , protein structure , range (aeronautics) , computer science , biological system , mathematics , physics , biology , materials science , molecule , nuclear magnetic resonance , zoology , hydrogen bond , filter (signal processing) , quantum mechanics , evolutionary biology , composite material , computer vision , embedded system
Protein structure prediction is a long‐standing problem in molecular biology. Due to lack of an accurate energy function, it is often difficult to know whether the sampling algorithm or the energy function is the most important factor for failure of locating near‐native conformations of proteins. This article examines the size dependence of sampling effectiveness by using a perfect “energy function”: the root‐mean‐squared distance from the target native structure. Using protein targets up to 460 residues from critical assessment of structure prediction techniques (CASP11, 2014), we show that the accuracy of near native structures sampled is relatively independent of protein sizes but strongly depends on the errors of predicted torsion angles. Even with 40% out‐of‐range angle prediction, 2 Å or less near‐native conformation can be sampled. The result supports that the poor energy function is one of the bottlenecks of structure prediction and predicted torsion angles are useful for overcoming the bottleneck by restricting the sampling space in the absence of a perfect energy function. © 2015 Wiley Periodicals, Inc.