What Markov State Models Can and Cannot Do: Correlation versus Path-Based Observables in Protein-Folding Models
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Abstract
Markov state models (MSMs) have been widely applied to study the kinetics and pathways of protein conformational dynamics based on statistical analysis of molecular dynamics (MD) simulations. These MSMs coarse-grain both configuration space and time in ways that limit what kinds of observables they can reproduce with high fidelity over different spatial and temporal resolutions. Despite their popularity, there is still limited understanding of which biophysical observables can be computed from these MSMs in a robust and unbiased manner, and which suffer from the space-time coarse-graining intrinsic in the MSM model. Most theoretical arguments and practical validity tests for MSMs rely on long-time equilibrium kinetics, such as the slowest relaxation time scales and experimentally observable time-correlation functions. Here, we perform an extensive assessment of the ability of well-validated protein folding MSMs to accurately reproduce path-based observable such as mean first-passage times (MFPTs) and transition path mechanisms compared to a direct trajectory analysis. We also assess a recently proposed class of history-augmented MSMs (haMSMs) that exploit additional information not accounted for in standard MSMs. We conclude with some practical guidance on the use of MSMs to study various problems in conformational dynamics of biomolecules. In brief, MSMs can accurately reproduce correlation functions slower than the lag time, but path-based observables can only be reliably reproduced if the lifetimes of states exceed the lag time, which is a much stricter requirement. Even in the presence of short-lived states, we find that haMSMs reproduce path-based observables more reliably.
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Conformational Dynamics in Proteins: Entangled Slow Fluctuations and Nonequilibrium Reaction Events
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Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics
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Self-citationAutomated Path Searching Reveals the Mechanism of Hydrolysis Enhancement by T4 Lysozyme Mutants
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Molecular Latent Space Simulators for Distributed and Multimolecular Trajectories
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- Authors
- Suárez, E., Wiewiora, R. P., Wehmeyer, C., Noé, F., Chodera, J. D., & Zuckerman, D. M.
- Affiliations
- Frederick National Laboratory for Cancer Research (US)
- Memorial Sloan Kettering Cancer Center (US)
- Freie Universität Berlin, 14195 Berlin, Germany
- Freie Universität Berlin (DE)
- Oregon Health & Science University (US)
- Corresponding author
- Chodera, John D.
- Zuckerman, Daniel M.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-04-27
- Volume
- 17
- Issue
- 5
- Pages
- 3119-3133
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- PMC
- PMC8127341
- OpenAlex
- W3128519410
- Cited by
- 65
- References
- 97
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 16 · 2023: 12 · 2022: 9 · 2021: 12
- FWCI
- 3.85
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Chemical Theory and Computation (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (HSN26120080001, P30CA008748)
- Division of Molecular and Cellular Biosciences (1119091)
- U.S. Department of Defense (W81XWH-17-1-0412)
- National Institutes of Health (R01GM115805, R01GM121505)
- H2020 European Research Council (ERC CoG 77223)
- 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 › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Keywords
- Markov state models, protein folding, conformational dynamics, mean first passage time, time correlation functions, time delay, state lifetimes
- MeSH terms
- Markov Chains, Models, Chemical, Stochastic Processes, Protein Folding, Molecular Dynamics Simulation
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