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通过动态优化的集体变量增强蛋白质构象转变的采样。

Enhanced Sampling of Protein Conformational Transitions via Dynamically Optimized Collective Variables.

机构信息

Department of Chemistry and Applied Bioscience , ETH Zürich, c/o USI Campus , Via Giuseppe Buffi 13 , Lugano , Ticino CH-6900 , Switzerland.

Institute of Computational Science , Universita della Svizzera Italiana (USI) , Via Giuseppe Buffi 13 , Lugano , Ticino CH-6900 , Switzerland.

出版信息

J Chem Theory Comput. 2019 Feb 12;15(2):1393-1398. doi: 10.1021/acs.jctc.8b00827. Epub 2019 Jan 3.

Abstract

Protein conformational transitions often involve many slow degrees of freedom. Their knowledge would give distinctive advantages because it provides chemical and mechanistic insight and accelerates the convergence of enhanced sampling techniques that rely on collective variables. In this study, we implemented a recently developed variational approach to conformational dynamics metadynamics to the conformational transition of the moderate size protein, L99A T4 Lysozyme. To find the slow modes of the system, we combined data coming from NMR experiments as well as from short MD simulations. A Metadynamics simulation based on these information reveals the presence of two intermediate states, at an affordable computational cost.

摘要

蛋白质构象转变通常涉及许多慢自由度。了解这些自由度将具有显著优势,因为它可以提供化学和机械洞察力,并加速依赖于集体变量的增强采样技术的收敛。在这项研究中,我们将最近开发的变分方法应用于构象动力学元动力学,以研究中等大小蛋白质 L99A T4 溶菌酶的构象转变。为了找到系统的慢模式,我们结合了来自 NMR 实验以及短 MD 模拟的数据。基于这些信息的元动力学模拟揭示了存在两个中间状态,而且计算成本可承受。

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