MasterMSM: A Package for Constructing Master Equation Models of Molecular Dynamics
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Abstract
Markov state models (MSMs) have become one of the most important techniques for understanding biomolecular transitions from classical molecular dynamics (MD) simulations. MSMs provide a systematized way of accessing the long time kinetics of the system of interest from the short-time scale microscopic transitions observed in simulation trajectories. At the same time, they provide a consistent description of the equilibrium and dynamical properties of the system of interest, and they are ideally suited for comparisons against experiment. A few software packages exist for building MSMs, which have been widely adopted. Here we introduce MasterMSM, a new Python package that uses the master equation formulation of MSMs and provides a number of new algorithms for building and analyzing these models. We demonstrate some of the most distinctive features of the package, including the estimation of rates, definition of core-sets for transition based assignment of states, the estimation of committors and fluxes, and the sensitivity analysis of the emerging networks. The package is available at https://github.com/daviddesancho/MasterMSM.
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Cites
Identification of slow molecular order parameters for Markov model construction
Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., & Noé, F. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4811489 · Cited by 990 · cited 1× · see citing works
“25 The main output from the trajectory module is a time series of discrete states, encapsulated in TimeSeries class objects.”
BackgroundMarkov state models of biomolecular conformational dynamics
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Coarse master equations for peptide folding dynamics
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Markov state models based on milestoning
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Folding kinetics and unfolded state dynamics of the GB1 hairpin from molecular simulation
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- Authors
- de Sancho, D., & Aguirre, A.
- Affiliations
- University of the Basque Country , , Paseo Manuel Lardizabal, 3 , ,
- Donostia International Physics Center (ES)
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-08-19
- Volume
- 59
- Issue
- 9
- Pages
- 3625-3629
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2969314109
- Cited by
- 4
- References
- 6
- Citations by year
- 2021: 2 · 2020: 1 · 2019: 1
- FWCI
- 0.33
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by-nc-nd)
- Funders
- Ministerio de Econom?a y Competitividad (CTQ2015-65320-R, RYC-2016-19590)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Markov state models, master equation, transition rate estimation, committor, flux analysis, sensitivity analysis, state assignment
- MeSH terms
- Algorithms, Kinetics, Markov Chains, Software, Molecular Dynamics Simulation
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