Molecular 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.
|Original language||English (US)|
|Number of pages||17|
|Journal||Chinese Journal of Chemical Physics|
|State||Published - Sep 25 2018|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2016-CRG5-3007
Acknowledgements: 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.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.