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一种时间和内存效率高的方法,用于快速计算具有二十面体对称性的复合物的正常模式。

A time and memory efficient recipe for fast normal mode computations of complexes with icosahedral symmetry.

机构信息

Department of Computer Science, Program of Bioinformatics and Computational Biology, Iowa State University, Ames, IA, 50011, USA.

出版信息

J Mol Graph Model. 2019 Mar;87:30-40. doi: 10.1016/j.jmgm.2018.10.024. Epub 2018 Nov 13.

Abstract

With the recent breakthroughs in experimental technologies, structure determination of extremely large assemblies, many with icosahedral symmetry, has been rapidly accelerating. Computational studies of their dynamics are important to deciphering their functions as well as to structural refinement but are challenged by their extremely large size, which ranges from hundreds of thousands to even millions of atoms. Group theory can be used to significantly speed up the normal mode computations of these symmetric complexes, but the derivation is often obscured by the complexity of group theory and consequently is not widely accessible. To address this problem, this work presents an easy recipe for normal mode computations of complexes with icosahedral symmetry. The recipe details how the Hessian matrix in symmetry coordinates can be constructed in a few easy steps of matrix multiplications, without going through the complexity of group theory. All the "ingredient" matrices required in the recipe are fully provided in the Supplemental Information for easy reproduction. The work is timely considering the expected large in-flux of many more icosahedral assemblies in the near future. The recipe uses a minimum amount of memory and solves the normal modes in a significantly reduced amount of time, making it feasible to perform normal mode computations of these assemblies on most computer systems.

摘要

随着实验技术的最新突破,具有二十面体对称性的极大组装体的结构测定正在迅速加速。对其动力学的计算研究对于破译它们的功能以及结构细化都很重要,但由于它们的尺寸非常大,范围从几十万到甚至上百万个原子,因此具有挑战性。群论可用于显著加快这些对称复合物的正则模态计算,但由于群论的复杂性,推导通常被掩盖,因此不易被广泛使用。为了解决这个问题,这项工作提出了一种计算具有二十面体对称性的复合物正则模态的简单方法。该方法详细说明了如何在矩阵乘法的几个简单步骤中构建对称坐标中的 Hessian 矩阵,而无需经历群论的复杂性。配方中所需的所有“成分”矩阵都在补充信息中完全提供,便于重现。考虑到未来不久将有更多的二十面体组装体涌入,这项工作非常及时。该配方使用最少的内存并在显著减少的时间内解决正则模态,使得在大多数计算机系统上对这些组装体进行正则模态计算成为可行。

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