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Refining protein structures using enhanced sampling techniques with restraints derived from an ensemble‐based model
Author(s) -
Ma Tianqi,
Zang Tianwu,
Wang Qinghua,
Ma Jianpeng
Publication year - 2018
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.3486
Subject(s) - casp , computer science , correctness , algorithm , gaussian , computational chemistry , protein structure prediction , protein structure , chemistry , biochemistry
This paper reports a method for high‐accuracy protein structural refinement, which is a direct extension of the method in our recent publication (Zang, J Chem Phys 2018; 149:072319). It combines a parallel continuous simulated tempering (PCST) method with a temperature‐dependent restraint and a blind model selection scheme. In this work, a single‐reference‐based restraint in previous work was changed to an ensemble‐based model (EBM), in which the non‐bonded Lennard–Jones term for each contacting atomic pair in previous restraining potential was replaced by a multi‐Gaussian function whose parameters are derived from an ensemble of structures such as the ones from various CASP participating groups. The purpose of EBM is to take advantage of partial “correctness” distributed among members of the structural ensemble. Totally 18 targets were refined from the refinement category of CASP10, CASP11 and CASP12. In Top‐1 group, 11 out of 18 targets had better models (greater GDT_TS scores) than the CASPR participants. In Top‐5 group, nine out of 18 were better. Our results show that PCST‐EBM method can considerably improve the low‐accuracy structures.