MSMBuilder: Statistical Models for Biomolecular Dynamics
Open accessPublished version of a preprint
Abstract
MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis. MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python application programming interface. MSMBuilder was developed with careful consideration for compatibility with the broader machine learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics, but is just as applicable to other computational or experimental time-series measurements.
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Cites
Markov state models of biomolecular conformational dynamics
Chodera, J. D., & Noé, F. (2014) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2014.04.002 · Cited by 776 · see citing works
Activation pathway of Src kinase reveals intermediate states as targets for drug design
Shukla, D., Meng, Y., Roux, B., & Pande, V. S. (2014) · Nature Communications · doi:10.1038/ncomms4397 · Cited by 333 · see citing works
Self-citationVariational approach to molecular kinetics
Nüske, F., Keller, B. G., Pérez-Hernández, G., Mey, A. S. J. S., & Noé, F. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct4009156 · Cited by 308 · see citing works
EMMA: A Software Package for Markov Model Building and Analysis
Senne, M., Trendelkamp-Schroer, B., Mey, A. S. J. S., Schütte, C., & Noé, F. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300274u · Cited by 148 · see citing works
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Huggins, D. J., Biggin, P. C., Dämgen, M. A., Essex, J. W., Harris, S. A., Henchman, R. H., Khalid, S., Kuzmanic, A., Laughton, C. A., Michel, J., et al. (2019) · WIREs Computational Molecular Science · doi:10.1002/wcms.1393 · Cited by 169 · see citing works
Verkhivker, G. M., Agajanian, S., Hu, G., & Tao, P. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00136 · Cited by 76 · see citing works
Towards simple kinetic models of functional dynamics for a kinase subfamily
Sultan, M. M., Kiss, G., & Pande, V. S. (2018) · Nature Chemistry · doi:10.1038/s41557-018-0077-9 · Cited by 68 · see citing works
Self-citationSuárez, E., Wiewiora, R. P., Wehmeyer, C., Noé, F., Chodera, J. D., & Zuckerman, D. M. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c01154 · Cited by 65 · see citing works
- Authors
- Harrigan, M. P., Sultan, M. M., Hernández, C. X., Husic, B. E., Eastman, P., Schwantes, C. R., Beauchamp, K. A., McGibbon, R. T., & Pande, V. S.
- Affiliations
- Stanford University (US)
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- McGibbon, Robert T.
- Pande, Vijay S.
- ORCID
- Venue
- Biophysical Journal
- Publisher
- Elsevier BV
- Published
- 2017-01
- Volume
- 112
- Issue
- 1
- Pages
- 10-15
- ISSN
- 0006-3495 (ISSN-L)
- PMC
- PMC5232355
- OpenAlex
- W2570547882
- Cited by
- 277
- References
- 4
- Citations by year
- 2026: 16 · 2025: 26 · 2024: 24 · 2023: 19 · 2022: 39 · 2021: 30 · 2020: 23 · 2019: 28 · 2018: 48 · 2017: 24
- FWCI
- 13.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Biophysical Journal (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (U19 AI109662, 2R01GM062868, P30CA008747)
- National Science Foundation (MCB-0954714)
- National Science Foundation Graduate Research Fellowship Program (DGE-114747)
- Sloan Kettering Institute
- Starr Foundation (I8-A8-058)
- 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, biomolecular dynamics, protein folding, conformational change, atomistic simulation, hidden Markov models, independent component analysis, time series analysis, high-dimensional time series, Python, scikit-learn compatibility, machine learning
- MeSH terms
- CSK Tyrosine-Protein Kinase, Markov Chains, Protein Conformation, Software, Models, Statistical, src-Family Kinases, Molecular Dynamics Simulation
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