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哈密顿重加权法在 GROMOS 力场中优化蛋白质主链二面角参数。

Hamiltonian Reweighing To Refine Protein Backbone Dihedral Angle Parameters in the GROMOS Force Field.

机构信息

Institute for Molecular Modeling and Simulation , University of Natural Resources and Life Sciences , Muthgasse 18 , 1190 Vienna , Austria.

出版信息

J Chem Inf Model. 2020 Jan 27;60(1):279-288. doi: 10.1021/acs.jcim.9b01034. Epub 2020 Jan 9.

Abstract

Molecular dynamics simulations of proteins depend critically on the underlying force field, which may be parameterized against experimental data or high-quality quantum calculations. Here, we develop search algorithms based on Monte Carlo and steepest descent calculations to optimize the backbone dihedral angle parameters from a single reference simulation. We apply these tools to improve the agreement between simulations of single, capped amino acids and experimentally determined values and secondary structure propensities of these molecules. The parameters are further refined based on simulations of a set of seven proteins and finally validated in simulations on a large set of 52 protein structures. Improvements in the dihedral angle distributions are observed, and structural propensities of the proteins are reproduced very well. Overall, the GROMOS 54A8_bb parameter set forms an improvement to previous parameter sets, both for small molecules and for protein simulations.

摘要

蛋白质的分子动力学模拟严重依赖于基础力场,力场可以根据实验数据或高质量的量子计算进行参数化。在这里,我们开发了基于蒙特卡罗和最陡下降计算的搜索算法,以优化来自单个参考模拟的主链二面角参数。我们将这些工具应用于改进单个加帽氨基酸模拟与实验确定值之间的一致性,以及这些分子的二级结构倾向。这些参数进一步基于一组七种蛋白质的模拟进行优化,最后在对 52 个蛋白质结构的大型模拟集进行验证。观察到二面角分布的改善,并且蛋白质的结构倾向得到很好的再现。总体而言,GROMOS 54A8_bb 参数集是对以前参数集的改进,无论是对小分子还是蛋白质模拟都是如此。

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