Jun-hui Peng, Wei Wang, Ye-qing Yu, Han-lin Gu, Xuhui Huang. Clustering Algorithms to Analyze Molecular Dynamics Simulation Trajectories for Complex Chemical and Biological Systems†[J]. Chinese Journal of Chemical Physics , 2018, 31(4): 404-420. doi: 10.1063/1674-0068/31/cjcp1806147
Citation: Jun-hui Peng, Wei Wang, Ye-qing Yu, Han-lin Gu, Xuhui Huang. Clustering Algorithms to Analyze Molecular Dynamics Simulation Trajectories for Complex Chemical and Biological Systems[J]. Chinese Journal of Chemical Physics , 2018, 31(4): 404-420. doi: 10.1063/1674-0068/31/cjcp1806147

Clustering Algorithms to Analyze Molecular Dynamics Simulation Trajectories for Complex Chemical and Biological Systems

doi: 10.1063/1674-0068/31/cjcp1806147
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  • Author Bio:

    Xuhui Huang is the Padma Harilela Associate Professor of Science at Department of Chemistry of the Hong Kong University of Scientific and Technology. He earned his B.S. degree (class of 9703) from University of Science and Technology of China (USTC) in 2001. He obtained his Ph.D. degree from Columbia University in 2006 with Prof. Bruce Beme. He did his postdoc research at Stanford University with Profs. Michael Levitt (Nobel Laureate in Chemistry) and Vijay Pande. He joined HKUST as an assistant professor in 2010, and received an early promotion to the tenured Associated Professor on January 2015. His research is focused on statistical mechanics and biophysical chemistry. He has received a series of awards including the American Chemical Society (ACS) OpenEye Outstanding Junior Faculty Award (2014), Hong Kong Research Grant Council Early Career Award (2013), and ACS CCG Excellence Award (2006). In 2017, he was elected as a founding member of Young Academy of Sciences of Hong Kong

  • Corresponding author: Xuhui Huang, E-mail:xuhuihuang@ust.hk
  • Received Date: 2018-06-20
  • Accepted Date: 2018-07-13
  • Publish Date: 2018-08-27
  • 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.


  • Part of the special issue for celebration of "the 60th Anniversary of University of Science and Technology of China and the 30th Anniversary of Chinese Journal of Chemical Physics". All the authors proudly obtained their Bachelor's degrees from USTC: J. Peng (Class of 0808), W. Wang (Class of 1003), Y. Yu (Class of 1203), H. Gu (Class of 1300), and X. Huang (Class of 9703)
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