Markov state models of biomolecular conformational dynamics

By John D. Chodera, Frank No'e. Current Opinion in Structural Biology, 2014

725 citations recorded by the source corpus.

It has recently become practical to construct Markov state models (MSMs) that reproduce the long-time statistical conformational dynamics of biomolecules using data from molecular dynamics simulations. MSMs can predict both stationary and kinetic quantities on long timescales (e.g. milliseconds) using a set of atomistic molecular dynamics simulations that are individually much shorter, thus addressing the well-known sampling problem in molecular dynamics simulation. In addition to providing predictive quantitative models, MSMs greatly facilitate both the extraction of insight into biomolecular mechanism (such as folding and functional dynamics) and quantitative comparison with single-molecule and ensemble kinetics experiments. A variety of methodological advances and software packages now bring the construction of these models closer to routine practice. Here, we review recent progress in this field, considering theoretical and methodological advances, new software tools, and recent applications of these approaches in several domains of biochemistry and biophysics, commenting on remaining challenges.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/901b0f0c21c18d2531c3e394cfb5d829304f1770

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Citations in the corpus

Variational Approach for Learning Markov Processes from Time Series Data (2017, background): “Examples include molecular dynamics (Chodera and Noé 2014; Prinz et al. 2011), wireless communications (Konrad et al. 2001; Ma et al. 2001) and fluid dynamics (Mezić 2013; Froyland et al. 2016).”
Scalable emulation of protein equilibrium ensembles with generative deep learning (2025, neutral): “Due to the vast computational costs, exhaustive MD sampling of biomolecules has only been achieved in few cases, either with special purpose supercomputers [27] or via large-scale distributed simulations integrated via statistical models [9, 28].”