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Application of Markov State Models to Simulate Long Timescale Dynamics of Biological Macromolecules

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Protein Conformational Dynamics

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 805))

Abstract

Conformational changes of proteins are an*Author contributed equally with all other contributors. essential part of many biological processes such as: protein folding, ligand binding, signal transduction, allostery, and enzymatic catalysis. Molecular dynamics (MD) simulations can describe the dynamics of molecules at atomic detail, therefore providing a much higher temporal and spatial resolution than most experimental techniques. Although MD simulations have been widely applied to study protein dynamics, the timescales accessible by conventional MD methods are usually limited to timescales that are orders of magnitude shorter than the conformational changes relevant for most biological functions. During the past decades great effort has been devoted to the development of theoretical methods that may enhance the conformational sampling. In recent years, it has been shown that the statistical mechanics framework provided by discrete-state and -time Markov State Models (MSMs) can predict long timescale dynamics from a pool of short MD simulations. In this chapter we provide the readers an account of the basic theory and selected applications of MSMs. We will first introduce the general concepts behind MSMs, and then describe the existing procedures for the construction of MSMs. This will be followed by the discussions of the challenges of constructing and validating MSMs, Finally, we will employ two biologically-relevant systems, the RNA polymerase and the LAO-protein, to illustrate the application of Markov State Models to elucidate the molecular mechanisms of complex conformational changes at biologically relevant timescales.

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Acknowledgements

XH acknowledges support from the National Basic Research Program of China (973 Program 2013CB834703), National Science Foundation of China: 21273188, and Hong Kong Research Grants Council GRF 661011 and HKUST2/CRF/10. DAS acknowledges support from the PEW Charitable Trusts as postdoctoral fellow in the Biomedical Sciences. FKS acknowledges support from Hong Kong PhD Fellowship Scheme (2012/13).

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    Authors and Affiliations

    1. Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

      Lin-Tai Da, Fu Kit Sheong, Daniel-Adriano Silva & Xuhui Huang

    2. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA

      Lin-Tai Da

    3. Department of Biochemistry, University of Washington, Seattle, WA, 98105, USA

      Daniel-Adriano Silva

    4. Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

      Xuhui Huang

    5. Center of Systems Biology and Human Health, School of Science and Institute for Advance Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

      Xuhui Huang

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    1. Lin-Tai Da
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    Correspondence to Xuhui Huang .

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    Editors and Affiliations

    1. Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, People's Republic of China

      Ke-li Han

    2. The Scripps Research Institute, La Jolla, California, USA

      Xin Zhang

    3. Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, People's Republic of China

      Ming-jun Yang

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    Da, LT., Sheong, F.K., Silva, DA., Huang, X. (2014). Application of Markov State Models to Simulate Long Timescale Dynamics of Biological Macromolecules. In: Han, Kl., Zhang, X., Yang, Mj. (eds) Protein Conformational Dynamics. Advances in Experimental Medicine and Biology, vol 805. Springer, Cham. https://doi.org/10.1007/978-3-319-02970-2_2

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