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dc.contributor.authorPeng, Jun-hui
dc.contributor.authorWang, Wei
dc.contributor.authorYu, Ye-qing
dc.contributor.authorGu, Han-lin
dc.contributor.authorHuang, Xuhui
dc.date.accessioned2018-11-11T09:04:00Z
dc.date.available2018-11-11T09:04:00Z
dc.date.issued2018-09-25
dc.identifier.citationPeng J, Wang W, Yu Y, Gu H, Huang X (2018) Clustering algorithms to analyze molecular dynamics simulation trajectories for complex chemical and biological systems. Chinese Journal of Chemical Physics 31: 404–420. Available: http://dx.doi.org/10.1063/1674-0068/31/cjcp1806147.
dc.identifier.issn1674-0068
dc.identifier.issn2327-2244
dc.identifier.doi10.1063/1674-0068/31/cjcp1806147
dc.identifier.urihttp://hdl.handle.net/10754/629809
dc.description.abstractMolecular dynamics (MD) simulation has become a powerful tool to investigate the structure-function 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.
dc.description.sponsorshipThis 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.
dc.publisherAIP Publishing
dc.subjectMolecular dynamics
dc.subjectBiological macromolecules
dc.subjectProbability theory
dc.subjectProteins
dc.subjectComputational models
dc.titleClustering algorithms to analyze molecular dynamics simulation trajectories for complex chemical and biological systems
dc.typeArticle
dc.identifier.journalChinese Journal of Chemical Physics
dc.contributor.institutionDepartment of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
dc.contributor.institutionHKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
dc.contributor.institutionDepartment of Mathematics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
dc.contributor.institutionState Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
dc.contributor.institutionCenter of Systems Biology and Human Health, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
kaust.grant.numberOSR-2016-CRG5-3007
dc.date.published-online2018-09-25
dc.date.published-print2018-08


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