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Cited by 71Advanced Physical and Chemical Molecular InteractionsProtein Structure and DynamicsMarkov Chains and Monte Carlo Methods

Dynamical reweighting: Improved estimates of dynamical properties from simulations at multiple temperatures

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Chodera, John D., Swope, William C., Noé, Frank, Prinz, Jan-Hendrik, Shirts, Michael R., Pande, Vijay S.·2011·The Journal of Chemical Physics
DOI 10.1063/1.3592152PMC PMC3143679
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Abstract

Dynamical averages based on functionals of dynamical trajectories, such as time-correlation functions, play an important role in determining kinetic or transport properties of matter. At temperatures of interest, the expectations of these quantities are often dominated by contributions from rare events, making the precise calculation of these quantities by molecular dynamics simulation difficult. Here, we present a reweighting method for combining simulations from multiple temperatures (or from simulated or parallel tempering simulations) to compute an optimal estimate of the dynamical properties at the temperature of interest without the need to invoke an approximate kinetic model (such as the Arrhenius law). Continuous and differentiable estimates of these expectations at any temperature in the sampled range can also be computed, along with an assessment of the associated statistical uncertainty. For rare events, aggregating data from multiple temperatures can produce an estimate with the desired precision at greatly reduced computational cost compared with simulations conducted at a single temperature. Here, we describe use of the method for the canonical (NVT) ensemble using four common models of dynamics (canonical distribution of Hamiltonian trajectories, Andersen thermostatting, Langevin, and overdamped Langevin or Brownian dynamics), but it can be applied to any thermodynamic ensemble provided the ratio of path probabilities at different temperatures can be computed. To illustrate the method, we compute a time-correlation function for solvated terminally-blocked alanine peptide across a range of temperatures using trajectories harvested using a modified parallel tempering protocol.

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  • A 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 [30]

    The Discussion says dynamical reweighting can use data from all temperatures in a tempering simulation, subject to the dynamics being amenable to reweighting. The Abstract says it can produce continuous, differentiable estimates of dynamical expectations across the sampled temperature range. Our copy does not establish that a resulting Markov state model (MSM) captures that continuous dependence; the paper describes estimates of dynamical properties rather than an MSM. (LLM, conf=0.86)

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Authors
Chodera, J. D., Swope, W. C., Noé, F., Prinz, J.-H., Shirts, M. R., & Pande, V. S.
Affiliations
  • QB3 (US)
  • IBM Research - Almaden (US)
  • Freie Universität Berlin (DE)
  • Heidelberg University (DE)
  • University of Virginia (US)
  • Stanford University (US)
ORCID
Venue
The Journal of Chemical Physics
Publisher
AIP Publishing
Published
2011-06-24
Volume
134
Issue
24
Pages
244107-244107
ISSN
  • 0021-9606 (ISSN-L)
  • 1089-7690
DOI
10.1063/1.3592152
PMC
PMC3143679
OpenAlex
W2090500441
Cited by
71
References
74
Citations by year
2026: 3 · 2025: 1 · 2024: 3 · 2023: 1 · 2022: 3 · 2021: 4 · 2020: 8 · 2019: 4 · 2018: 9 · 2017: 5 · 2016: 3 · 2015: 3 · 2014: 10 · 2013: 6 · 2012: 4
FWCI
5.09
Type
journal article
Language
en
Open access
Locations
  • The Journal of Chemical Physics (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Deutsche Forschungsgemeinschaft (825/2)
  • National Science Foundation (CHE-0535616, TG-MCB100015)
  • National Institutes of Health (GM34993, U54 GM072970)
Topics
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
Keywords
time correlation functions, rare events, parallel tempering, Langevin dynamics, canonical ensemble, statistical uncertainty, kinetic properties
MeSH terms
Alanine, Peptides, Temperature, Molecular Dynamics Simulation

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