arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2301.04492physics.chem-phcs.LGphysics.comp-ph
收藏

大规模分子动力学模拟数据的快速构象聚类

Fast conformational clustering of extensive molecular dynamics simulation data

University of Konstanz(康斯坦茨大学);Max Planck Institute for Polymer Research(马克斯·普朗克聚合物研究所)

Simon Hunkler, Kay Diederichs, Oleksandra Kukharenko, Christine Peter

AI总结:

本文提出一种结合 cc_analysis、encodermap 与 HDBSCAN 的无监督工作流程,通过迭代聚类和可调 RMSD 标准实现长分子动力学轨迹的快速构象聚类,并在四个蛋白体系上验证了其能力与性能。

摘要:

我们提出了一种无监督数据处理工作流程,专门用于对长分子动力学模拟轨迹进行快速构象聚类。在该方法中,我们将两种降维算法(cc_analysis 和 encodermap)与基于密度的空间聚类算法(HDBSCAN)相结合。所提出的方案受益于这三种算法的优势,同时避免了各单独方法的大部分缺点。此处 cc_analysis 算法首次被应用于分子模拟数据。Encodermap 通过提供一种高效的方式来处理大量数据并将其分配到聚类,从而对 cc_analysis 形成补充。该流程的主要目标是在保持所发现聚类具有清晰构象身份的同时,最大化给定轨迹中被分配帧的数量。在实践中,我们通过使用迭代聚类方法和最终聚类分配中可调节的基于均方根偏差的标准来实现这一目标。这使得能够发现不同密度以及不同程度结构身份的聚类。借助四个测试体系,我们展示了该聚类工作流程的能力和性能:Trp-cage 蛋白的野生型和耐热突变体(TC5b 和 TC10b)、NTL9 和 Protein B。这些体系中的每一个都对该方案提出了各自的挑战,总体上很好地概述了使用所提出方法时的优势以及可能出现的潜在困难。

英文摘要:

We present an unsupervised data processing workflow that is specifically designed to obtain a fast conformational clustering of long molecular dynamics simulation trajectories. In this approach we combine two dimensionality reduction algorithms (cc\_analysis and encodermap) with a density-based spatial clustering algorithm (HDBSCAN). The proposed scheme benefits from the strengths of the three algorithms while avoiding most of the drawbacks of the individual methods. Here the cc\_analysis algorithm is for the first time applied to molecular simulation data. Encodermap complements cc\_analysis by providing an efficient way to process and assign large amounts of data to clusters. The main goal of the procedure is to maximize the number of assigned frames of a given trajectory, while keeping a clear conformational identity of the clusters that are found. In practice we achieve this by using an iterative clustering approach and a tunable root-mean-square-deviation-based criterion in the final cluster assignment. This allows to find clusters of different densities as well as different degrees of structural identity. With the help of four test systems we illustrate the capability and performance of this clustering workflow: wild-type and thermostable mutant of the Trp-cage protein (TC5b and TC10b), NTL9 and Protein B. Each of these systems poses individual challenges to the scheme, which in total give a nice overview of the advantages, as well as potential difficulties that can arise when using the proposed method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑