Markov State Model (MSM) has become a popular approach to study the conformational dynamics of complex biological systems in recent years. Built upon a large number of short molecular dynamics simulation trajectories, MSM is able to predict the long time scale dynamics of complex systems. However, to achieve Markovianity, an MSM often contains hundreds or thousands of states (microstates), hindering human interpretation of the underlying system mechanism. One way to reduce the number of states is to lump kinetically similar states together and thus coarse-grain the microstates into macrostates. In this work, we introduce a probabilistic lumping algorithm, the Gibbs lumping algorithm, to assign a probability to any given kinetic lumping using the Bayesian inference. In our algorithm, the transitions among kinetically distinct macrostates are modeled by Poisson processes, which will well reflect the separation of time scales in the underlying free energy landscape of biomolecules. Furthermore, to facilitate the search for the optimal kinetic lumping (i.e., the lumped model with the highest probability), a Gibbs sampling algorithm is introduced. To demonstrate the power of our new method, we apply it to three systems: a 2D potential, alanine dipeptide, and a WW protein domain. In comparison with six other popular lumping algorithms, we show that our method can persistently produce the lumped macrostate model with the highest probability as well as the largest metastability. We anticipate that our Gibbs lumping algorithm holds great promise to be widely applied to investigate conformational changes in biological macromolecules.
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21 August 2018
Research Article|
August 09 2018
An efficient Bayesian kinetic lumping algorithm to identify metastable conformational states via Gibbs sampling
Special Collection:
Enhanced Sampling for Molecular Systems
Wei Wang
;
Wei Wang
1
HKUST-Shenzhen Research Institute
, Hi-Tech Park, Nanshan, Shenzhen 518057, China
2
Department of Chemistry, The Hong Kong University of Science and Technology
, Clear Water Bay, Kowloon, Hong Kong
3
Center of Systems Biology and Human Health, The Hong Kong University of Science and Technology
, Clear Water Bay, Kowloon, Hong Kong
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Tong Liang;
Tong Liang
4
Department of Statistics, The Chinese University of Hong Kong
, Shatin, New Territories, Hong Kong
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Fu Kit Sheong
;
Fu Kit Sheong
2
Department of Chemistry, The Hong Kong University of Science and Technology
, Clear Water Bay, Kowloon, Hong Kong
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Xiaodan Fan
;
Xiaodan Fan
a)
4
Department of Statistics, The Chinese University of Hong Kong
, Shatin, New Territories, Hong Kong
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Xuhui Huang
Xuhui Huang
a)
1
HKUST-Shenzhen Research Institute
, Hi-Tech Park, Nanshan, Shenzhen 518057, China
2
Department of Chemistry, The Hong Kong University of Science and Technology
, Clear Water Bay, Kowloon, Hong Kong
3
Center of Systems Biology and Human Health, The Hong Kong University of Science and Technology
, Clear Water Bay, Kowloon, Hong Kong
5
Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology
, Clear Water Bay, Kowloon, Hong Kong
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J. Chem. Phys. 149, 072337 (2018)
Article history
Received:
February 26 2018
Accepted:
July 25 2018
Citation
Wei Wang, Tong Liang, Fu Kit Sheong, Xiaodan Fan, Xuhui Huang; An efficient Bayesian kinetic lumping algorithm to identify metastable conformational states via Gibbs sampling. J. Chem. Phys. 21 August 2018; 149 (7): 072337. https://doi.org/10.1063/1.5027001
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