Department of Computer Sciences and Genome Center, University of California, Davis , Davis, California 95616, United States.
Department of Structural Biology, Stanford University , Stanford, California 94305, United States.
J Chem Theory Comput. 2017 Mar 14;13(3):1424-1438. doi: 10.1021/acs.jctc.6b01136. Epub 2017 Feb 24.
Understanding the dynamics of biomolecules is the key to understanding their biological activities. Computational methods ranging from all-atom molecular dynamics simulations to coarse-grained normal-mode analyses based on simplified elastic networks provide a general framework to studying these dynamics. Despite recent successes in studying very large systems with up to a 100,000,000 atoms, those methods are currently limited to studying small- to medium-sized molecular systems due to computational limitations. One solution to circumvent these limitations is to reduce the size of the system under study. In this paper, we argue that coarse-graining, the standard approach to such size reduction, must define a hierarchy of models of decreasing sizes that are consistent with each other, i.e., that each model contains the information of the dynamics of its predecessor. We propose a new method, Decimate, for generating such a hierarchy within the context of elastic networks for normal-mode analysis. This method is based on the concept of the renormalization group developed in statistical physics. We highlight the details of its implementation, with a special focus on its scalability to large systems of up to millions of atoms. We illustrate its application on two large systems, the capsid of a virus and the ribosome translation complex. We show that highly decimated representations of those systems, containing down to 1% of their original number of atoms, still capture qualitatively and quantitatively their dynamics. Decimate is available as an OpenSource resource.
理解生物分子的动力学是理解其生物活性的关键。从全原子分子动力学模拟到基于简化弹性网络的粗粒正则模态分析等计算方法，为研究这些动力学提供了一个通用框架。尽管最近在研究多达 1 亿个原子的非常大的系统方面取得了成功，但由于计算限制，这些方法目前仅限于研究中小分子系统。解决这些限制的一种方法是减小研究系统的大小。在本文中，我们认为，粗粒化是这种尺寸减小的标准方法，必须定义一个相互一致的、尺寸减小的模型层次结构，即每个模型都包含其前一个模型的动力学信息。我们提出了一种新的方法 Decimate，用于在正则模态分析的弹性网络背景下生成这种层次结构。该方法基于统计物理学中发展的重整化群概念。我们强调了其实现的细节，特别关注其对多达数百万个原子的大型系统的可扩展性。我们在两个大型系统——病毒衣壳和核糖体翻译复合物上展示了其应用。我们表明，那些系统的高度简化表示形式，包含其原始原子数目的 1%，仍然能够定性和定量地捕捉它们的动力学。Decimate 作为开源资源提供。
