基于TorchMD的粗粒化分子动力模拟研究

Coarse-Grained Molecular Dynamics Study based on TorchMD

粗粒化模型通过简化原子性质以及原子间的相互作用实现生物大分子长时间尺度的分子动力学模拟.深度学习通过模拟人类的认知过程实现海量数据的准确分类和回归过程.本论文将这两种技术进行融合,利用基于深度学习的粗粒化分子动力学模拟技术研究分子在不同状态之间的变化过程,并提出基于TorchMD的分子动力学模拟的分析框架.在本工作中,MFDP聚类算法被用于在三维的CV变量空间中进行聚类,并确定分子的若干主要状态,在完成聚类的同时,给出各类中的代表分子构象,并给出类之间的分子构象.这为后续利用String算法分析分子在不同状态间的转换路径打下基础.通过String算法,迭代搜索得到分子在不同状态之间的变化路径以及对应的势能变化曲线.通过与已有文献的结果进行对比,验证了基于TorchMD的粗粒化分子动力学模拟的理论框架可以在相对较短的时间尺度里研究分子的变化过程.

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.