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Improving low-accuracy protein structures using enhanced sampling techniques

机译：使用增强的采样技术改善低精度蛋白质结构

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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.
机译：在本文中，我们报告了使用增强的采样和盲选择技术进行高精度蛋白质结构改进的结果。通过组合平行连续的模拟回火（PCST）方法，由Zang等人开发。 [J.化学。物理。 141,044113（2014）]和基于结构的模型（SBM）为约束，我们精致23个目标（从Casp12的Casp10和5的细化类别中获得18个）。我们还设计了一种新颖的模型选择方法，以盲目地从非常长的仿真轨迹中选择高质量模型。 PCST-SBM与盲选择方法的结合使用产生的最终模型优于初始模型。对于Top-1组，23个目标中的7个具有更好的模型（更大的全球距离测试总分数），而不是结构预测参与者的关键评估。对于前5个组，23分中的10个更好。我们的结果证明了蛋白质结构预测和改进中增强抽样的关键位置，证明了电流造成电流场的可实现低精度结构的相当大提高。通过AIP发布发布。

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