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Improving low-accuracy protein structures using enhanced sampling techniques
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DOI：10.1063/1.5027243.png)
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摘要
En 中文
In this paper, we report results of using enhanced sampling and blind selection techniques for high-accuracy protein structural refinement. By combining a parallel continuous simulated tempering (PCST) method, previously developed by Zang et al. [J. Chem. Phys. 141, 044113 (2014)], and the structure based model (SBM) as restraints, we refined 23 targets (18 from the refinement category of the CASP10 and 5 from that of CASP12). We also designed a novel model selection method to blindly select high-quality models from very long simulation trajectories. The combined use of PCST-SBM with the blind selection method yielded final models that are better than initial models. For Top-1 group, 7 out of 23 targets had better models (greater global distance test total scores) than the critical assessment of structure prediction participants. For Top-5 group, 10 out of 23 were better. Our results justify the crucial position of enhanced sampling in protein structure prediction and refinement and demonstrate that a considerable improvement of low-accuracy structures is achievable with current force fields. Published by AIP Publishing.
Keyword:
FREE-ENERGY LANDSCAPE
STRUCTURE PREDICTION
SECONDARY STRUCTURE
DYNAMICS
REFINEMENT
SIMULATION
ENSEMBLE
DISTRIBUTIONS
DEFINITION
ALGORITHMS