STATISTICAL CLUSTERING-TECHNIQUES FOR THE ANALYSIS OF LONG MOLECULAR-DYNAMICS TRAJECTORIES - ANALYSIS OF 2.2-NS TRAJECTORIES OF YPGDV
STATISTICAL CLUSTERING-TECHNIQUES FOR THE ANALYSIS OF LONG MOLECULAR-DYNAMICS TRAJECTORIES - ANALYSIS OF 2.2-NS TRAJECTORIES OF YPGDV
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DOI：
10.1021/bi00053a005
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发表时间：
1993-01-19
期刊：
影响因子：
2.9
通讯作者：
BROOKS, CL
中科院分区：
文献类型：
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作者：
KARPEN, ME;TOBIAS, DJ;BROOKS, CL
导致新生蛋白质折叠事件的微观相互作用和机制通常是未知的。虽然如此短的时间尺度事件很难通过实验研究，但肽的分子动力学模拟可以为研究与蛋白质折叠起始相关的事件提供有用的模型。最近，对在溶液中形成稳定反转的五肽 Tyr-Pro-Gly-Asp-Val [Tobias, D.J., Mertz, J.E., & Brooks, C.L., III (1991) Biochemistry 30, 6054-6058] 进行了两次极长的分子动力学模拟（每次 2.2 ns）。托比亚斯等人。使用传统的轨迹分析方法检查了这个大型系统（大约 30 000 个构象）中的折叠事件。这个问题的剪切幅度促使我们开发一种基于自组织神经网络的自动化方法，以提取分子动力学轨迹的关键特征。神经网络用于执行构象聚类，从而降低系统的复杂性，同时最大限度地减少信息损失。使用二面角空间中的距离作为构象相似性的度量将构象分组在一起。所得簇代表“构象状态”，并且检查这些状态之间的转变以识别构象变化的机制。许多构象变化仅涉及单个二面角的旋转，但也发现了一致的角度变化。来自完整轨迹的 30 000 个样本中的大部分构象信息保留在相对较少的所得簇中，为分析大型分子模拟的扩展基础提供了强大的工具。
The microscopic interactions and mechanisms leading to nascent protein folding events are generally unknown. While such short time-scale events are difficult to study experimentally, molecular dynamics simulations of peptides can provide a useful model for studying events related to protein folding initiation. Recently, two extremely long molecular dynamics simulations (2.2 ns each) were carried out on the pentapeptide Tyr-Pro-Gly-Asp-Val [Tobias, D.J., Mertz, J.E., & Brooks, C.L., III (1991) Biochemistry 30, 6054-6058] that forms stable reverse turns in solution. Tobias et al. examined folding events in this large system (approximately 30 000 conformations) using traditional methods of trajectory analysis. The shear magnitude of this problem prompted us to develop an automated approach, based on self-organizing neural nets, to extract the key features of the molecular dynamics trajectory. The neural net is used to perform conformational clustering, which reduces the complexity of a system while minimizing the loss of information. The conformations were grouped together using distances in dihedral angle space as a measure of conformational similarity. The resulting clusters represent ''conformational states'', and transitions between these states were examined to identify mechanisms of conformational change. Many conformational changes involved the rotation of only a single dihedral angle, but concerted angle changes were also found. Most of the conformational information in the 30 000 samples from the full trajectories was retained in the relatively few resultant clusters, providing a powerful tool for analysis of an expanding base of large molecular simulations.