The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Coarse-Graining Methods for Computational Biology
Abstract
Connecting the molecular world to biology requires understanding how molecular-scale dynamics propagate upward in scale to define the function of biological structures. To address this challenge, multiscale approaches, including coarse-graining methods, become necessary. We discuss here the theoretical underpinnings and history of coarse-graining and summarize the state of the field, organizing key methodologies based on an emerging paradigm for multiscale theory and modeling of biomolecular systems. This framework involves an integrated, iterative approach to couple information from different scales. The primary steps, which coincide with key areas of method development, include developing first-pass coarse-grained models guided by experimental results, performing numerous large-scale coarse-grained simulations, identifying important interactions that drive emergent behaviors, and finally reconnecting to the molecular scale by performing all-atom molecular dynamics simulations guided by the coarse-grained results. The coarse-grained modeling can then be extended and refined, with the entire loop repeated iteratively if necessary.
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Laio, A., & Parrinello, M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.202427399 · Cited by 5,626
The MARTINI Force Field: Coarse Grained Model for Biomolecular Simulations
Marrink, S. J., Risselada, H. J., Yefimov, S., Tieleman, D. P., & de Vries, A. H. (2007) · The Journal of Physical Chemistry B · doi:10.1021/jp071097f · Cited by 5,562
Coarse Grained Model for Semiquantitative Lipid Simulations
Marrink, S. J., de Vries, A. H., & Mark, A. E. (2004) · The Journal of Physical Chemistry B · doi:10.1021/jp036508g · Cited by 2,257
T RANSITION P ATH S AMPLING : Throwing Ropes Over Rough Mountain Passes, in the Dark
Bolhuis, P. G., Chandler, D., Dellago, C., & Geissler, P. L. (2002) · Annual Review of Physical Chemistry · doi:10.1146/annurev.physchem.53.082301.113146 · Cited by 1,972
Anisotropy of Fluctuation Dynamics of Proteins with an Elastic Network Model
Atilgan, A. R., Durell, S. R., Jernigan, R. L., Demirel, M. C., Keskin, O., & Bahar, I. (2001) · Biophysical Journal · doi:10.1016/s0006-3495(01)76033-x · Cited by 1,637
Large Amplitude Elastic Motions in Proteins from a Single-Parameter, Atomic Analysis
Tirion, M. M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.1905 · Cited by 1,615
Miyazawa, S., & Jernigan, R. L. (1985) · Macromolecules · doi:10.1021/ma00145a039 · Cited by 1,536
Direct evaluation of thermal fluctuations in proteins using a single-parameter harmonic potential
Bahar, I., Atilgan, A. R., & Erman, B. (1997) · Folding and Design · doi:10.1016/s1359-0278(97)00024-2 · Cited by 1,375
Kästner, J. (2011) · WIREs Computational Molecular Science · doi:10.1002/wcms.66 · Cited by 1,283
A Multiscale Coarse-Graining Method for Biomolecular Systems
Izvekov, S., & Voth, G. A. (2005) · The Journal of Physical Chemistry B · doi:10.1021/jp044629q · Cited by 1,197
Self-citation
Cited by
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Sezgin, E., Levental, I., Mayor, S., & Eggeling, C. (2017) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2017.16 · Cited by 2,124
Coarse-Grained Protein Models and Their Applications
Kmiecik, S., Gront, D., Kolinski, M., Wieteska, L., Dawid, A. E., & Kolinski, A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00163 · Cited by 1,071
Perspective: Coarse-grained models for biomolecular systems
Noid, W. G. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4818908 · Cited by 893
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
CHARMM-GUI Martini Maker for Coarse-Grained Simulations with the Martini Force Field
Qi, Y., Ingólfsson, H. I., Cheng, X., Lee, J., Marrink, S. J., & Im, W. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00513 · Cited by 494
Metadata
- Authors
- Saunders, M. G., & Voth, G. A.
- Venue
- Annual Review of Biophysics
- Publisher
- Annual Reviews
- Published
- 2013-05-06
- Volume
- 42
- Issue
- 1
- Pages
- 73-93
- ISSN
- 1936-122X (ISSN-L)
- 1936-1238
- OpenAlex
- W2171695688
- Cited by
- 565
- References
- 64
- FWCI
- 3.40
- Type
- journal article
- Language
- en
- Open access
- closed
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