The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
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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In the citing paper
“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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Citations
Cites
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Khaled, M., Johannknecht, L., & Palomino‐Hernández, O. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.05.686750
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“…– for instance, in the climate sciences with the development of coupled earth system models (Golaz et al., 2019) and global cloud-resolving models (Satoh et al., 2019), and in protein folding research with the development of simulation models of all-atom molecular dynamics (Lane et al., 2013).”
Rocha-Santos, A., Grillo, I. B., Urquiza‐Carvalho, G. A., & Rocha, G. B. (2026) · ACS Omega · doi:10.1021/acsomega.5c05968
Chand, A. (2026) · Journal of Chemical Sciences · doi:10.1007/s12039-026-02481-3
Asmuruf, F. A., Supeno, S., Yabansabra, Y. R., Salim, I., & Simaremare, E. S. (2026) · Research Square · doi:10.21203/rs.3.rs-9151448/v1
Ojha, A. A., Votapka, L. W., Dutta, S., Noland, A. F., Hanson, S. M., & Amaro, R. E. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00659 · Cited by 3
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“Molecular dynamics (MD) can resolve atomic-level detail through physics-based simulation but is computationally expensive, with exhaustive conformational sampling generally prohibitive at the scale required for design screening [21, 22].”
Scalable emulation of protein equilibrium ensembles with generative deep learning
Lewis, S., Hempel, T., Jiménez Luna, J., Gastegger, M., Xie, Y., Foong, A. Y. K., García Satorras, V., Abdin, O., Veeling, B. S., Zaporozhets, I., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.05.626885 · Cited by 349
“It consists of 68 systems with multiple chains and uses the amber ff99sb forcefield [26].”
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
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