CATBOSS: Cluster Analysis of Trajectories Based on Segment Splitting.
CATBOSS: Cluster Analysis of Trajectories Based on Segment Splitting.
复制标题
DOI：
10.1021/acs.jcim.1c00598
复制
发表时间：
2021-10-25
影响因子：
5.6
通讯作者：
Lin YS
中科院分区：
文献类型：
--
作者：
Damjanovic J;Murphy JM;Lin YS
分子动力学（MD）模拟是一种非常有效的分子行为预测和分析工具。然而，这些模拟生成的大量数据可能难以以人类可读的方式处理和渲染。聚类分析是将数据划分为结构上不同的状态的常用方法。我们提出了一种方法，利用MD轨迹的时间信息，以更低的内存成本，使更准确的聚类的最先进的改进。迄今为止，MD模拟的聚类分析一般将模拟快照视为仅仅是独立数据点的集合，并试图根据结构相似性将它们分成不同的聚类。这种新的方法，基于段分裂的轨迹聚类分析（CATBOSS），应用基于密度峰值的聚类来分类通过变化检测学习的轨迹段。将该方法应用于合成玩具模型以及四个真实数据集-丙氨酸二肽和缬氨酸二肽以及两种快速折叠蛋白质的MD模拟轨迹-我们发现CATBOSS是鲁棒的和高性能的，产生自然外观的聚类边界，并大大提高聚类分辨率。由于将点分类为段通过将它们分组为接近状态均值来强调数据中的密度差距，因此应用于缬氨酸二肽系统的CATBOSS甚至能够解释故意从输入数据集中忽略的自由度。我们还展示了CATBOSS的潜在效用，区分亚稳态过渡段以及有前途的应用程序的情况下，很少或根本没有预先知道的内在坐标，使一个高度通用的分析工具。
Molecular dynamics (MD) simulations are an exceedingly and increasingly potent tool for molecular behavior prediction and analysis. However, the enormous wealth of data generated by these simulations can be difficult to process and render in a human-readable fashion. Cluster analysis is a commonly used way to partition data into structurally distinct states. We present a method that improves on the state of the art by taking advantage of the temporal information of MD trajectories to enable more accurate clustering at a lower memory cost. To date, cluster analysis of MD simulations has generally treated simulation snapshots as a mere collection of independent data points and attempted to separate them into different clusters based on structural similarity. This new method, cluster analysis of trajectories based on segment splitting (CATBOSS), applies density-peak-based clustering to classify trajectory segments learned by change detection. Applying the method to a synthetic toy model as well as four real-life data sets–trajectories of MD simulations of alanine dipeptide and valine dipeptide as well as two fast-folding proteins–we find CATBOSS to be robust and highly performant, yielding natural-looking cluster boundaries and greatly improving clustering resolution. As the classification of points into segments emphasizes density gaps in the data by grouping them close to the state means, CATBOSS applied to the valine dipeptide system is even able to account for a degree of freedom deliberately omitted from the input data set. We also demonstrate the potential utility of CATBOSS in distinguishing metastable states from transition segments as well as promising application to cases where there is little or no advance knowledge of intrinsic coordinates, making for a highly versatile analysis tool.
登录
查看更多内容
依靠泛素信号的分子基础：泛素二聚体的双尺度模拟研究。
影响因子：
4.3
作者：
Berg A;Kukharenko O;Scheffner M;Peter C
通讯作者：
Peter C
影响因子：
3
作者：
Jung J;Kobayashi C;Kasahara K;Tan C;Kuroda A;Minami K;Ishiduki S;Nishiki T;Inoue H;Ishikawa Y;Feig M;Sugita Y
通讯作者：
Sugita Y
影响因子：
62.1
作者：
Damjanovic J;Miao J;Huang H;Lin YS
通讯作者：
Lin YS
影响因子：
4.4
作者：
Altis, Alexandros;Nguyen, Phuong H.;Stock, Gerhard
通讯作者：
Stock, Gerhard
影响因子：
3.3
作者：
Jain, Abhinav;Stock, Gerhard
通讯作者：
Stock, Gerhard