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Clustering Algorithms to Analyze Molecular Dynamics Simulation Trajectories for Complex Chemical and Biological Systems
Jun-hui Peng1,2, Wei Wang1,2, Ye-qing Yu1,2, Han-lin Gu3, Xuhui Huang1,2,4,5
1.HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China;2.Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong;3.Department of Mathematics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong;4.Center of Systems Biology and Human Health, The Hong Kong University of Science and Technology, Kowloon, Hong Kong;5.State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
Abstract:
Molecular dynamics (MD) simulation has become a powerful tool to investigate the structurefunction relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets containing millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape (geometric clustering) and kinetics (kinetic clustering). In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, agglomerative algorithms (single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage), center-based algorithms (K-Means, K-Medoids, K-Centers, and APM), density-based algorithms (neighbor-based, DBSCAN, density-peaks, and Robust-DB), and spectral-based algorithms (PCCA and PCCA+). In particular, differences between geometric and kinetic clustering metrics will be discussed along with the performances of different clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algorithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets.
Key words:  Molecular dynamics simulation  Clustering algorithms  Markov state models  Protein dynamics
FundProject:This work was supported by Shenzhen Science and Technology Innovation Committee (JCYJ20170413173837121), the Hong Kong Research Grant Council (HKUST C6009-15G, 14203915, 16302214, 16304215, 16318816, and AoE/P-705/16), King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) (OSR-2016-CRG5-3007), Guangzhou Science Technology and Innovation Commission (201704030116), and Innovation and Technology Commission (ITCPD/17-9 and ITC-CNERC14SC01). X. Huang is the Padma Harilela Associate Professor of Science.
分析复杂化学及生物体系分子动力学模拟轨迹的聚类方法
彭俊辉1,2, 王薇1,2, 虞叶卿1,2, 谷翰林3, 黄旭辉1,2,4,5
1.香港科技大学深圳研究院, 深圳 518057;2.香港科技大学化学系, 九龙;3.香港科技大学数学系, 九龙;4.香港科技大学系统生物与人类健康中心, 九龙;5.香港科技大学分子神经科学国家重点实验室, 九龙
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DOI:10.1063/1674-0068/31/cjcp1806147
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