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To milliseconds and beyond: challenges in the simulation of protein folding
Open accessAbstract
Quantitatively accurate all-atom molecular dynamics (MD) simulations of protein folding have long been considered a holy grail of computational biology. Due to the large system sizes and long timescales involved, such a pursuit was for many years computationally intractable. Further, sufficiently accurate forcefields needed to be developed in order to realistically model folding. This decade, however, saw the first reports of folding simulations describing kinetics on the order of milliseconds, placing many proteins firmly within reach of these methods. Progress in sampling and forcefield accuracy, however, presents a new challenge: how to turn huge MD datasets into scientific understanding. Here, we review recent progress in MD simulation techniques and show how the vast datasets generated by such techniques present new challenges for analysis. We critically discuss the state of the art, including reaction coordinate and Markov state model (MSM) methods, and provide a perspective for the future.
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“the timescales relevant to biomolecular folding and function (often microseconds to seconds) are enormously long compared to the timesteps required for stable integration (generally femtoseconds) [1]”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [1]
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“However, numerous challenges have traditionally stood in the way of simulations reaching experimentally relevant timescales and gathering sufficient statistics to make definitive statements about protein folding mechanism [1].”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [1]
In the citing paper
“MSMs can predict kinetic quantities on long timescales (e.g. milliseconds) using a set of much shorter MD simulations.395–396”
From Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell (2016) · cites this paper as [396]
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Allen, M. P., & Tildesley, D. J. (2017) · Oxford University Press eBooks · doi:10.1093/oso/9780198803195.005.0004
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Metadata
- Authors
- Lane, T. J., Shukla, D., Beauchamp, K. A., & Pande, V. S.
- Affiliations
- Stanford University (US)
- Corresponding author
- Pande, Vijay S
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 23
- Issue
- 1
- Pages
- 58-65
- ISSN
- 0959-440X (ISSN-L)
- PMC
- PMC3673555
- OpenAlex
- W2039369142
- Cited by
- 409
- References
- 92
- Citations by year
- 2026: 13 · 2025: 21 · 2024: 11 · 2023: 22 · 2022: 21 · 2021: 23 · 2020: 26 · 2019: 26 · 2018: 38 · 2017: 70 · 2016: 38 · 2015: 44 · 2014: 36 · 2013: 20
- FWCI
- 2.92
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Current Opinion in Structural Biology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Simbios NIH Center for Biomedical Computation (GM072970)
- NIH (R01GM62828)
- NSF GRF
- Stanford Graduate Fellowship
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Keywords
- protein folding, all-atom molecular dynamics, folding kinetics, force fields, reaction coordinate, Markov state models, sampling efficiency
- MeSH terms
- Kinetics, Proteins, Protein Folding, Molecular Dynamics Simulation
About these checks
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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