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Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules
Abstract
Many interesting dynamic properties of biological molecules cannot be simulated directly using molecular dynamics because of nanosecond time scale limitations. These systems are trapped in potential energy minima with high free energy barriers for large numbers of computational steps. The dynamic evolution of many molecular systems occurs through a series of rare events as the system moves from one potential energy basin to another. Therefore, we have proposed a robust bias potential function that can be used in an efficient accelerated molecular dynamics approach to simulate the transition of high energy barriers without any advance knowledge of the location of either the potential energy wells or saddle points. In this method, the potential energy landscape is altered by adding a bias potential to the true potential such that the escape rates from potential wells are enhanced, which accelerates and extends the time scale in molecular dynamics simulations. Our definition of the bias potential echoes the underlying shape of the potential energy landscape on the modified surface, thus allowing for the potential energy minima to be well defined, and hence properly sampled during the simulation. We have shown that our approach, which can be extended to biomolecules, samples the conformational space more efficiently than normal molecular dynamics simulations, and converges to the correct canonical distribution.
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Citations
Cited by
Dynamic personalities of proteins
Henzler-Wildman, K., & Kern, D. (2007) · Nature · doi:10.1038/nature06522 · Cited by 2,545
Using collective variables to drive molecular dynamics simulations
Fiorin, G., Klein, M. L., & Hénin, J. (2013) · Molecular Physics · doi:10.1080/00268976.2013.813594 · Cited by 1,053
Wei, G., Xi, W., Nussinov, R., & Ma, B. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00562 · Cited by 436
To milliseconds and beyond: challenges in the simulation of protein folding
Lane, T. J., Shukla, D., Beauchamp, K. A., & Pande, V. S. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2012.11.002 · Cited by 409
The future of molecular dynamics simulations in drug discovery
Borhani, D. W., & Shaw, D. E. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-011-9517-y · Cited by 313
“Several distinct, atomistic approaches aim to accelerate the still-insufficient sampling of protein conformational states, for example metadynamics [48], accelerated MD [49], and temperature accelerated MD [50].”
Background
Metadata
- Authors
- Hamelberg, D., Mongan, J., & McCammon, J. A.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2004-06-22
- Volume
- 120
- Issue
- 24
- Pages
- 11919-11929
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W1992899488
- Cited by
- 1,528
- References
- 0
- FWCI
- 5.25
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hamelberg, D., Mongan, J., & McCammon, J. A. (2004). Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules. The Journal of Chemical Physics. https://doi.org/10.1063/1.1755656
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.