Constructing Markov State Models to elucidate the functional conformational changes of complex biomolecules
KAUST Grant NumberOSR-2016-CRG5-3007
Permanent link to this recordhttp://hdl.handle.net/10754/626074
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AbstractThe function of complex biomolecular machines relies heavily on their conformational changes. Investigating these functional conformational changes is therefore essential for understanding the corresponding biological processes and promoting bioengineering applications and rational drug design. Constructing Markov State Models (MSMs) based on large-scale molecular dynamics simulations has emerged as a powerful approach to model functional conformational changes of the biomolecular system with sufficient resolution in both time and space. However, the rapid development of theory and algorithms for constructing MSMs has made it difficult for nonexperts to understand and apply the MSM framework, necessitating a comprehensive guidance toward its theory and practical usage. In this study, we introduce the MSM theory of conformational dynamics based on the projection operator scheme. We further propose a general protocol of constructing MSM to investigate functional conformational changes, which integrates the state-of-the-art techniques for building and optimizing initial pathways, performing adaptive sampling and constructing MSMs. We anticipate this protocol to be widely applied and useful in guiding nonexperts to study the functional conformational changes of large biomolecular systems via the MSM framework. We also discuss the current limitations of MSMs and some alternative methods to alleviate them.
CitationWang W, Cao S, Zhu L, Huang X (2017) Constructing Markov State Models to elucidate the functional conformational changes of complex biomolecules. Wiley Interdisciplinary Reviews: Computational Molecular Science: e1343. Available: http://dx.doi.org/10.1002/wcms.1343.
SponsorsThe authors would like to thank Dr. Fu Kit Sheong for fruitful discussions. This work was supported by the Hong Kong Research Grant Council (grant numbers HKUST C6009-15G, 16305817, 16302214, 16304215, 16318816, AoE/P-705/16, M-HKUST601/13, F-HKUST605/15, and T13–607/12R), King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) (OSR-2016-CRG5-3007), and Innovation and Technology Commission (ITCPD/17-9 and ITC-CNERC14SC01). W.W. acknowledges support from the Hong Kong Ph.D. Fellowship Scheme 2014/15 (PF13-14699). X.H. is the Padma Harilela Associate Professor of Science.