Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics.

By John D. Chodera, N. Singhal, Vijay S. Pande, Ken A. Dill, William Swope. Journal of Chemical Physics, 2007

597 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/64e66318f7d408e1192b93d15bba021447925522

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Citations in the corpus

Machine learning for molecular simulation (2019, background): “If E performs a one-hot-encoding that indicates which “state” the system is in, then the pair (E,P) is called Markov state model (MSM [76, 77, 78, 79, 74, 80]), and P is a matrix of conditional probabilities to be in a state j at time t+ τ given that the system was in a state i at time t.”
Identification of slow molecular order parameters for Markov model construction. (2013, background): “The resulting models are often called transition networks [74, 62, 32], Diffusion maps [18, 76], Master equation models [87, 14], Markov models [64] or Markov state models [86, 16] (MSM), where “Markovianity” means that the kinetics are modeled by a memoryless jump process between states.”
Kinetic distance and kinetic maps from molecular dynamics simulation. (2015, background): “Due to increasing availability of high-performance computing resources for mass production of MD data, there is an increasing interest in systematic and automatic construction of models of the metastable dynamics, such as Markov state models (MSMs) [34, 14, 10, 8, 24, 6] and diffusion maps [11, 26].”
The protein folding problem. (2008, background): “To go beyond classical assumptions, there has been an extensive and growing effort to use master-equation approaches (13,26,31,36,60, 61,77,110,161,172,176,201,202,208,211,212,231,232) to explore underlying assumptions about reaction coordinates, pathways, transition states, and RLSs.”
Markov models of molecular kinetics: generation and validation. (2011, methodology): “This approach was based on the idea that the discretized dynamics must be approximately Markovian if the system remained in each partition sufficiently long to approximately lose memory [39].”
Multiensemble Markov models of molecular thermodynamics and kinetics (2016, unknown): “x∈X∩Si aðxÞ= Z aðxÞμi ðxÞdx [27]”
Constructing Markov State Models to elucidate the functional conformational changes of complex biomolecules (2018, methodology): “In addition, Chodera et al.(120) invented a Monte Carlo-simulated annealing (MCSA) algorithm to optimize the PCCA results based on metastability ( P iMii, where M is the TPM of the macrostate model).”