分子动力学
计算机科学
折叠(DSP实现)
过程(计算)
比例(比率)
工作流程
Atom(片上系统)
统计物理学
路径(计算)
算法
人工智能
化学
物理
计算化学
工程类
量子力学
数据库
电气工程
嵌入式系统
操作系统
程序设计语言
作者
Pengfei Xu,Xiaohong Mou,Qiuhan Guo,Ting Fu,Hong Ren,Guiyan Wang,Yan Li,Guohui Li
标识
DOI:10.1063/1674-0068/cjcp2110218
摘要
The coarse grained (CG) model implements the molecular dynamics simulation by simplifying atom properties and interaction between them. Despite losing certain detailed information, the CG model is still the first-thought option to study the large molecule in long time scale with less computing resource. The deep learning model mainly mimics the human studying process to handle the network input as the image to achieve a good classification and regression result. In this work, the TorchMD, a MD framework combining the CG model and deep learning model, is applied to study the protein folding process. In 3D collective variable (CV) space, the modified find density peaks algorithm is applied to cluster the conformations from the TorchMD CG simulation. The center conformation in different states is searched. And the boundary conformations between clusters are assigned. The string algorithm is applied to study the path between two states, which are compared with the end conformations from all atoms simulations. The result shows that the main phenomenon of protein folding with TorchMD CG model is the same as the all-atom simulations, but with a less simulating time scale. The workflow in this work provides another option to study the protein folding and other relative processes with the deep learning CG model.
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