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PyCGTOOL:从原子轨迹自动生成粗粒分子动力学模型。

PyCGTOOL: Automated Generation of Coarse-Grained Molecular Dynamics Models from Atomistic Trajectories.

机构信息

School of Chemistry, University of Southampton , Hampshire, SO17 1BJ, United Kingdom.

出版信息

J Chem Inf Model. 2017 Apr 24;57(4):650-656. doi: 10.1021/acs.jcim.7b00096. Epub 2017 Apr 4.

Abstract

Development of coarse-grained (CG) molecular dynamics models is often a laborious process which commonly relies upon approximations to similar models, rather than systematic parametrization. PyCGTOOL automates much of the construction of CG models via calculation of both equilibrium values and force constants of internal coordinates directly from atomistic molecular dynamics simulation trajectories. The derivation of bespoke parameters from atomistic simulations improves the quality of the CG model compared to the use of generic parameters derived from other molecules, while automation greatly reduces the time required. The ease of configuration of PyCGTOOL enables the rapid investigation of multiple atom-to-bead mappings and topologies. Although we present PyCGTOOL used in combination with the GROMACS molecular dynamics engine its use of standard trajectory input libraries means that it is in principle compatible with other software. The software is available from the URL https://github.com/jag1g13/pycgtool as the following doi: 10.5281/zenodo.259330 .

摘要

粗粒化(CG)分子动力学模型的开发通常是一个繁琐的过程,通常依赖于对类似模型的近似,而不是系统的参数化。PyCGTOOL 通过直接从原子分子动力学模拟轨迹计算内坐标的平衡值和力常数,实现了 CG 模型的大部分构建。与使用从其他分子推导的通用参数相比,从原子模拟中推导的专用参数可以提高 CG 模型的质量,而自动化则大大减少了所需的时间。PyCGTOOL 的配置简单,能够快速研究多个原子到珠子的映射和拓扑结构。虽然我们展示了 PyCGTOOL 与 GROMACS 分子动力学引擎结合使用,但它使用标准轨迹输入库意味着它原则上与其他软件兼容。该软件可从以下 URL 获取:https://github.com/jag1g13/pycgtool,其 doi 为:10.5281/zenodo.259330。

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