Sampling Enrichment toward Target Structures Using Hybrid Molecular Dynamics-Monte Carlo Simulations.
Sampling Enrichment toward Target Structures Using Hybrid Molecular Dynamics-Monte Carlo Simulations.
复制标题
使用混合分子动力学-蒙特卡罗模拟对目标结构进行采样富集
DOI：
10.1371/journal.pone.0156043
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Li Y
中科院分区：
文献类型：
--
作者：
Yang K;Różycki B;Cui F;Shi C;Chen W;Li Y
对靶状态的采样浓缩是提高采样效率(SE)的类似方法，在蛋白质结构的精细化和用于探索结构-功能关系的近天然结构集成的生成中都是至关重要的。我们发展了一种混合分子动力学(MD)-蒙特卡罗(MC)方法来丰富对目标结构的采样。在该方法中，基于模拟结构与目标结构的小角X射线散射(SAXS)强度分布的符合程度的MC结构-接受判断，通过对常规MD模拟进行扰动来获得较高的SE。我们发现，通过使排名靠前的模型在二级结构和三级结构中都更接近目标结构，混合模拟可以显著提高SE。具体地说，对于20个单残基多肽，当初始结构与目标结构的均方根偏差(RMSD)小于7时，在310K和370K下，混合MD-MC模拟的RMSD分别比并行MD模拟的RMSD更接近目标结构，分别为0.83？和1.73？同时，平均SE值也分别提高了13.2%和15.7%。与并行MD模拟相比，当在MD-MC模拟中逐渐可以检测到目标状态时，采样的丰富变得更加显著，并且使SE提高了200%。我们还在真实蛋白质系统中进行了混合MD-MC方法的测试，结果表明，5个真实蛋白质中有3个的SE得到了改善。总体而言，这项工作提出了一种有效的方法来利用解决方案SAXS来改进蛋白质结构预测和精化，以及生成用于功能注释的近自然结构。
Sampling enrichment toward a target state, an analogue of the improvement of sampling efficiency (SE), is critical in both the refinement of protein structures and the generation of near-native structure ensembles for the exploration of structure-function relationships. We developed a hybrid molecular dynamics (MD)-Monte Carlo (MC) approach to enrich the sampling toward the target structures. In this approach, the higher SE is achieved by perturbing the conventional MD simulations with a MC structure-acceptance judgment, which is based on the coincidence degree of small angle x-ray scattering (SAXS) intensity profiles between the simulation structures and the target structure. We found that the hybrid simulations could significantly improve SE by making the top-ranked models much closer to the target structures both in the secondary and tertiary structures. Specifically, for the 20 mono-residue peptides, when the initial structures had the root-mean-squared deviation (RMSD) from the target structure smaller than 7 Å, the hybrid MD-MC simulations afforded, on average, 0.83 Å and 1.73 Å in RMSD closer to the target than the parallel MD simulations at 310K and 370K, respectively. Meanwhile, the average SE values are also increased by 13.2% and 15.7%. The enrichment of sampling becomes more significant when the target states are gradually detectable in the MD-MC simulations in comparison with the parallel MD simulations, and provide >200% improvement in SE. We also performed a test of the hybrid MD-MC approach in the real protein system, the results showed that the SE for 3 out of 5 real proteins are improved. Overall, this work presents an efficient way of utilizing solution SAXS to improve protein structure prediction and refinement, as well as the generation of near native structures for function annotation.