MSMBuilder2: Modeling Conformational Dynamics on the Picosecond to Millisecond Scale
Open accessAbstract
Markov State Models provide a framework for understanding the fundamental states and rates in the conformational dynamics of biomolecules. We describe an improved protocol for constructing Markov State Models from molecular dynamics simulations. The new protocol includes advances in clustering, data preparation, and model estimation; these improvements lead to significant increases in model accuracy, as assessed by the ability to recapitulate equilibrium and kinetic properties of reference systems. A high-performance implementation of this protocol, provided in MSMBuilder2, is validated on dynamics ranging from picoseconds to milliseconds.
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EMMA and MSMBuilder facilitate the construction and validation of Markov state models from molecular simulation data in various trajectory formats.
“Both the EMMA [38] [https://simtk.org/home/emma] and MSMBuilder [25] [http://msmbuilder.org/] software packages facilitate the construction and validation of Markov state models from molecular simulation data in various trajectory formats.”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [25]
The Introduction identifies MSMBuilder as software implementing a protocol for constructing Markov state models from molecular dynamics simulations, and Results §4 describes validation analyses. Our copy does not establish that MSMBuilder accepts various trajectory formats; the citation to this paper is attached to MSMBuilder, not EMMA. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThe transition matrix is commonly estimated only over the largest connected subset of states and proceeds via an iterative maximum likelihood estimator that respects detailed balance.
“Commonly, the transition matrix is only estimated over the largest connected subset of states [24, 25] and proceeds via an iterative maximum likelihood estimator that respects detailed balance [26, 7].”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [25]
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- Authors
- Beauchamp, K. A., Bowman, G. R., Lane, T. J., Maibaum, L., Haque, I. S., & Pande, V. S.
- Affiliations
- Stanford University (US)
- Corresponding author
- Pande, Vijay S.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2011-09-16
- Volume
- 7
- Issue
- 10
- Pages
- 3412-3419
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- PMC
- PMC3224091
- OpenAlex
- W2075243501
- Cited by
- 372
- References
- 33
- Citations by year
- 2026: 6 · 2025: 19 · 2024: 10 · 2023: 21 · 2022: 13 · 2021: 22 · 2020: 17 · 2019: 30 · 2018: 38 · 2017: 35 · 2016: 46 · 2015: 41 · 2014: 36 · 2013: 24 · 2012: 13
- FWCI
- 9.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Chemical Theory and Computation (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- Adolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California Berkeley
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM062868)
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- Markov state models, conformational dynamics, molecular dynamics simulation analysis, clustering, model estimation, kinetic rates, equilibrium states, biomolecules
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