Building Markov state models with solvent dynamics
Open accessAbstract
BACKGROUND: Markov state models have been widely used to study conformational changes of biological macromolecules. These models are built from short timescale simulations and then propagated to extract long timescale dynamics. However, the solvent information in molecular simulations are often ignored in current methods, because of the large number of solvent molecules in a system and the indistinguishability of solvent molecules upon their exchange. METHODS: We present a solvent signature that compactly summarizes the solvent distribution in the high-dimensional data, and then define a distance metric between different configurations using this signature. We next incorporate the solvent information into the construction of Markov state models and present a fast geometric clustering algorithm which combines both the solute-based and solvent-based distances. RESULTS: We have tested our method on several different molecular dynamical systems, including alanine dipeptide, carbon nanotube, and benzene rings. With the new solvent-based signatures, we are able to identify different solvent distributions near the solute. Furthermore, when the solute has a concave shape, we can also capture the water number inside the solute structure. Finally we have compared the performances of different Markov state models. The experiment results show that our approach improves the existing methods both in the computational running time and the metastability. CONCLUSIONS: In this paper we have initiated an study to build Markov state models for molecular dynamical systems with solvent degrees of freedom. The methods we described should also be broadly applicable to a wide range of biomolecular simulation analyses.
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Solvent degrees of freedom that play a critical role in defining kinetically distinct states are notoriously difficult to deal with.
“Solvent degrees of freedom that play a critical role in defining kinetically distinct states are also notoriously difficult to deal with [16].”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [16]
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- Authors
- Gu, C., Chang, H.-W., Maibaum, L., Pande, V. S., Carlsson, G. E., & Guibas, L. J.
- Affiliations
- Stanford University (US)
- University of Washington (US)
- Corresponding author
- Guibas, Leonidas J
- Venue
- BMC Bioinformatics
- Published
- 2013-01
- Volume
- 14
- Issue
- S2
- Pages
- S8-S8
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC3549830
- OpenAlex
- W2142660277
- Cited by
- 21
- References
- 35
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 2 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 2 · 2017: 3 · 2016: 4 · 2015: 1 · 2014: 2 · 2013: 1
- FWCI
- 1.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (0905823, 0900700, 0914833, 1011228)
- Office of Naval Research (FA9550, N00014)
- 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, solvent dynamics, solvent distribution, conformational dynamics, geometric clustering, metastability, alanine dipeptide, carbon nanotubes, benzene ring, biomolecular simulation
- MeSH terms
- Algorithms, Computer Simulation, Markov Chains, Models, Molecular, Solutions, Solvents
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