Optimal use of data in parallel tempering simulations for the construction of discrete-state Markov models of biomolecular dynamics
Open accessAbstract
Parallel tempering (PT) molecular dynamics simulations have been extensively investigated as a means of efficient sampling of the configurations of biomolecular systems. Recent work has demonstrated how the short physical trajectories generated in PT simulations of biomolecules can be used to construct the Markov models describing biomolecular dynamics at each simulated temperature. While this approach describes the temperature-dependent kinetics, it does not make optimal use of all available PT data, instead estimating the rates at a given temperature using only data from that temperature. This can be problematic, as some relevant transitions or states may not be sufficiently sampled at the temperature of interest, but might be readily sampled at nearby temperatures. Further, the comparison of temperature-dependent properties can suffer from the false assumption that data collected from different temperatures are uncorrelated. We propose here a strategy in which, by a simple modification of the PT protocol, the harvested trajectories can be reweighted, permitting data from all temperatures to contribute to the estimated kinetic model. The method reduces the statistical uncertainty in the kinetic model relative to the single temperature approach and provides estimates of transition probabilities even for transitions not observed at the temperature of interest. Further, the method allows the kinetics to be estimated at temperatures other than those at which simulations were run. We illustrate this method by applying it to the generation of a Markov model of the conformational dynamics of the solvated terminally blocked alanine peptide.
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The 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 [24]
The section on estimating transition probabilities from discrete trajectories describes a maximum-likelihood transition-matrix estimate, and the reversible-transition-matrices section imposes detailed balance. Neither section describes an iterative maximum-likelihood estimator. The cited paper also does not establish the claim about estimating only over the largest connected subset of states. (LLM, conf=0.84)
Experimental — accuracy not yet measuredA dynamical reweighting method permits use of data from all temperatures and allows the resulting MSM to capture continuous dependence on temperature.
“This approach was subsequently improved upon [24] by the introduction of a dynamical reweighting method [30] that permits use of data from all temperatures, which also allows the resulting MSM to capture the continuous dependence on temperature.”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [24]
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- Authors
- Prinz, J.-H., Chodera, J. D., Pande, V. S., Swope, W. C., Smith, J. C., & Noé, F.
- Affiliations
- Heidelberg University (DE)
- QB3 (US)
- Stanford University (US)
- IBM Research - Almaden (US)
- Oak Ridge National Laboratory (US)
- Freie Universität Berlin (DE)
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2011-06-24
- Volume
- 134
- Issue
- 24
- Pages
- 244108-244108
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- PMC
- PMC3139503
- OpenAlex
- W1993299912
- Cited by
- 52
- References
- 47
- Citations by year
- 2026: 1 · 2024: 4 · 2023: 1 · 2021: 2 · 2020: 5 · 2019: 2 · 2018: 5 · 2017: 5 · 2016: 1 · 2015: 6 · 2014: 10 · 2013: 7 · 2012: 2
- FWCI
- 2.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Journal of Chemical Physics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (725/2)
- U.S. Department of Energy (ERKJE84/ERKPE84)
- National Science Foundation (CHE-0535616)
- National Institutes of Health (GM34993, RO1 GM062868)
- 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 › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Keywords
- parallel tempering, Markov state models, transition rate estimation, temperature-dependent kinetics, conformational dynamics, alanine peptides
- MeSH terms
- Alanine, Kinetics, Markov Chains, Peptides, Temperature, Molecular Dynamics Simulation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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