Center for Theoretical Biological Physics, Rice University, Houston, TX 77030.
Department of Physics and Astronomy, Rice University, Houston, TX 77005.
Proc Natl Acad Sci U S A. 2019 May 7;116(19):9400-9409. doi: 10.1073/pnas.1900778116. Epub 2019 Apr 18.
Refining predicted protein structures with all-atom molecular dynamics simulations is one route to producing, entirely by computational means, structural models of proteins that rival in quality those that are determined by X-ray diffraction experiments. Slow rearrangements within the compact folded state, however, make routine refinement of predicted structures by unrestrained simulations infeasible. In this work, we draw inspiration from the fields of metallurgy and blacksmithing, where practitioners have worked out practical means of controlling equilibration by mechanically deforming their samples. We describe a two-step refinement procedure that involves identifying collective variables for mechanical deformations using a coarse-grained model and then sampling along these deformation modes in all-atom simulations. Identifying those low-frequency collective modes that change the contact map the most proves to be an effective strategy for choosing which deformations to use for sampling. The method is tested on 20 refinement targets from the CASP12 competition and is found to induce large structural rearrangements that drive the structures closer to the experimentally determined structures during relatively short all-atom simulations of 50 ns. By examining the accuracy of side-chain rotamer states in subensembles of structures that have varying degrees of similarity to the experimental structure, we identified the reorientation of aromatic side chains as a step that remains slow even when encouraging global mechanical deformations in the all-atom simulations. Reducing the side-chain rotamer isomerization barriers in the all-atom force field is found to further speed up refinement.
利用全原子分子动力学模拟来细化预测的蛋白质结构是一种完全通过计算手段产生蛋白质结构模型的方法，这些模型在质量上可与通过 X 射线衍射实验确定的结构模型相媲美。然而，在紧凑折叠状态下的缓慢重排使得通过无约束模拟对预测结构进行常规细化变得不可行。在这项工作中，我们从冶金和锻造领域汲取灵感，在这些领域，从业者已经找到了通过机械变形来控制平衡的实用方法。我们描述了一种两步细化程序，该程序涉及使用粗粒度模型识别机械变形的集体变量，然后在全原子模拟中沿着这些变形模式进行采样。证明识别那些改变接触图最多的低频集体模式是选择用于采样的变形的有效策略。该方法在 CASP12 竞赛的 20 个细化目标上进行了测试，结果表明，在相对较短的全原子模拟（50ns）中，该方法可以诱导大的结构重排，使结构更接近实验确定的结构。通过检查在与实验结构具有不同相似程度的结构子集中侧链旋转异构体状态的准确性，我们确定了芳香族侧链的重排是一个即使在全原子模拟中鼓励全局机械变形也仍然缓慢的步骤。发现降低全原子力场中侧链旋转异构体异构化势垒可以进一步加快细化过程。
