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利用多尺度细胞相关性计算蛋白质的熵。

Entropy of Proteins Using Multiscale Cell Correlation.

机构信息

Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.

MRC Human Genetics Unit, Institute of Genetics & Molecular Medicine, The University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, United Kingdom.

出版信息

J Chem Inf Model. 2020 Nov 23;60(11):5540-5551. doi: 10.1021/acs.jcim.0c00611. Epub 2020 Sep 29.

Abstract

A new multiscale method is presented to calculate the entropy of proteins from molecular dynamics simulations. Termed Multiscale Cell Correlation (MCC), the method decomposes the protein into sets of rigid-body units based on their covalent-bond connectivity at three levels of hierarchy: molecule, residue, and united atom. It evaluates the vibrational and topographical entropy from forces, torques, and dihedrals at each level, taking into account correlations between sets of constituent units that together make up a larger unit at the coarser length scale. MCC gives entropies in close agreement with normal-mode analysis and smaller than those using quasiharmonic analysis as well as providing much faster convergence. Moreover, MCC provides an insightful decomposition of entropy at each length scale and for each type of amino acid according to their solvent exposure and whether they are terminal residues. While the residue entropy depends weakly on solvent exposure, there is greater variation in entropy components for larger, more polar amino acids, which have increased conformational entropy but reduced vibrational entropy with greater solvent exposure.

摘要

提出了一种新的多尺度方法来计算蛋白质的分子动力学模拟的熵。该方法称为多尺度细胞相关性(MCC),它根据蛋白质的共价键连接性,将蛋白质分解为三组刚体单元,分为分子、残基和联合原子三个层次。它从各个层次的力、扭矩和二面角评估振动和地形熵,同时考虑组成更大单元的各个单元之间的相关性。MCC 给出的熵与正常模式分析非常吻合,而且比使用准谐分析的结果小,同时收敛速度更快。此外,MCC 还提供了根据溶剂暴露程度和是否为末端残基,按每种氨基酸类型在各个长度尺度上的熵的深入分解。尽管残基熵对溶剂暴露的依赖性较弱,但对于较大的、极性更强的氨基酸,熵分量的变化更大,这些氨基酸的构象熵增加,但振动熵随溶剂暴露而降低。

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