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首页> 外文期刊>Journal of molecular graphics & modelling >Selection rules on initial structures in parallel cascade selection molecular dynamics affect conformational sampling efficiency
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Selection rules on initial structures in parallel cascade selection molecular dynamics affect conformational sampling efficiency

机译:并行级联选择中初始结构的选择规则分子动力学影响构象采样效率

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摘要

Parallel cascade selection molecular dynamics (PaCS-MD) is a conformational sampling method for generating transition pathways from a given reactant to a product. In PaCS-MD, initial structures relevant to conformational transitions of proteins are selected and resampled by short-time MD simulations. As a general reaction coordinate, a root-mean-square deviation measured from the product (RMSD) is employed to rank the resampled configurations. Quantitatively, it initial structures are randomly selected from among the top X % of highly ranked configurations and resampled again. In PaCS-MD, the selection of initial structures and their conformational resampling are repeated as a cycle to promote the essential conformational transitions. Therefore, rules for selecting the initial structures might affect the conformational sampling efficiency. In the present study, to address the conformational sampling efficiency depending on the selection rule, the open-closed transition of di-ubiquitin was reproduced by PaCS-MD based on the resampling from the top X = 0.1, 1.0, 2.0, 5.0, 10.0, 25.0, 30.0, 40.0, and 50.0% of highly ranked configurations. Judging from broadness of sampled conformational area and required cycles, we conclude that the resampling from the top similar to 2.0% of highly ranked configurations might be the most efficient for generating a set of transition pathways in PaCS-MD. (C) 2018 Elsevier Inc. All rights reserved.
机译:平行级联选择分子动力学(PACS-MD)是一种构象采样方法,用于从给定反应物到产物的转变途径。在PACS-MD中,通过短时间MD模拟选择和重采采样与构象转变相关的初始结构。作为一般反应坐标,采用从产品(RMSD)测量的根平均方偏差来对重采样的配置进行排名。定量地,IT初始结构是从高级配置的顶部X%中随机选择并再次重新采样。在PACS-MD中,重复选择初始结构及其构象重采样作为促进基本构象转变的循环。因此,用于选择初始结构的规则可能会影响构象采样效率。在本研究中,根据选择规则来解决构象采样效率,基于从顶部x = 0.1,1.0,5.0,1.0,10.0,10.0,10.0,10.0,10.0,10.0,10.0,10.0,10.0 ,25.0,30.0,40.0和50.0%的高度排名配置。从采样的构象区域和所需周期的宽广来说,我们得出结论,从上面的重新采样类似于高度排名的配置的2.0%可能是在PACS-MD中产生一组过渡途径的最有效。 (c)2018年Elsevier Inc.保留所有权利。

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