Inherent structure versus geometric metric for state space discretization.
Inherent structure versus geometric metric for state space discretization.
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DOI：
10.1002/jcc.24315
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发表时间：
2016-05-30
影响因子：
3
通讯作者：
Huo S
中科院分区：
文献类型：
--
作者：
Liu H;Li M;Fan J;Huo S
固有结构（IS）和基于几何的聚类方法通常用于分析分子动力学轨迹。通过将采样构象最小化到势能/有效能量表面上的局部极小值来获得IS。最小化到同一能量盆中的构象属于一个簇。我们调查的影响，这两种方法的应用程序的轨迹分解对我们的理解的热力学和动力学的丙氨酸四肽。我们发现，在微观聚类水平上，IS方法和基于均方根偏差（RMSD）的聚类方法给出了完全不同的结果。根据能源景观的局部特征，具有接近RMSDS的构象可以最小化为不同的最小值，而具有大RMSDS的构象可以最小化为同一盆地。然而，基于从微团簇构建的过渡矩阵计算的弛豫时间尺度是相似的。微观集群水平的差异导致不同的宏观集群。虽然通过这两种聚类方法建立的动态模型验证近似马尔可夫，IS方法似乎给出了一个有意义的状态空间离散化在宏观集群水平。
Inherent structure (IS) and geometry-based clustering methods are commonly used for analyzing molecular dynamics trajectories. ISs are obtained by minimizing the sampled conformations into local minima on potential/effective energy surface. The conformations that are minimized into the same energy basin belong to one cluster. We investigate the influence of the applications of these two methods of trajectory decomposition on our understanding of the thermodynamics and kinetics of alanine tetrapeptide. We find that at the micro cluster level, the IS approach and root-mean-square deviation (RMSD) based clustering method give totally different results. Depending on the local features of energy landscape, the conformations with close RMSDs can be minimized into different minima, while the conformations with large RMSDs could be minimized into the same basin. However, the relaxation timescales calculated based on the transition matrices built from the micro clusters are similar. The discrepancy at the micro cluster level leads to different macro clusters. Although the dynamic models established through both clustering methods are validated approximately Markovian, the IS approach seems to give a meaningful state space discretization at the macro cluster level.