Everything you wanted to know about Markov State Models but were afraid to ask

By Vijay S. Pande, Kyle A. Beauchamp, Gregory R. Bowman. Methods, 2010

663 citations recorded by the source corpus.

Simulating protein folding has been a challenging problem for decades due to the long timescales involved (compared with what is possible to simulate) and the challenges of gaining insight from the complex nature of the resulting simulation data. Markov State Models (MSMs) present a means to tackle both of these challenges, yielding simulations on experimentally relevant timescales, statistical significance, and coarse grained representations that are readily humanly understandable. Here, we review this method with the intended audience of non-experts, in order to introduce the method to a broader audience. We review the motivations, methods, and caveats of MSMs, as well as some recent highlights of applications of the method. We conclude by discussing how this approach is part of a paradigm shift in how one uses simulations, away from anecdotal single-trajectory approaches to a more comprehensive statistical approach.

Subfields: Computer Networks and Communications, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/4afa2c93c386bc87c4fa4339709e2b788a6ed32a

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Citations in the corpus

Markov state models of biomolecular conformational dynamics (2014, background): “[6,7], the present review focuses on theoretical advances and applications that have been published since these earlier reviews were written.”
Alchemical free energy methods for drug discovery: Progress and challenges (2011, background): “While the metastable states can be identified by many short parallel simulations [88], recent adaptive schemes allow these to be constructed very efficiently on a computer cluster [89,90].”
Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview (2018, background): “Currently, the major difficulties of this technique are related to the trajectory partition into discrete states, the choice of the lagtime and sufficient sampling to guarantee statistical significance (Pande et al., 2010).”
Meeting Review: Diffuse X-Ray Scattering to Model Protein Motions (2014, neutral): “…1992; Clarage et al., 1992, 1995; Doucet and Benoit, 1987; Glover et al., 1991; Héry et al., 1998; Kolatkar et al., 1994; Meinhold et al., 2007; Meinhold and Smith, 2005a, 2005b, 2007; Mizuguchi et al., 1994; Moore, 2009; Phillips et al., 1980; Riccardi et al., 2010; Wall et al., 1997a, 1997b).”
GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit (2013, methodology): “The emergence of standardized packages for molecular modeling such as CHARMM, GROMOS, Amber, NAMD, and GROMACS has been important since they have helped commoditize simulation research, making the techniques available to life science application researchers who are not specialists in simulation…”
Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network (2022, methodology): “Specifically, we launched 10 parallel simulations from the apo structure, then constructed a Markov state model (MSM) 32,33 of the conformational ensemble and prioritized structures for the next round of simulations using a ranking function that balances exploitation (i.e. prioritizing states with…”