The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index.
Macromolecular Modeling with Rosetta
Abstract
Advances over the past few years have begun to enable prediction and design of macromolecular structures at near-atomic accuracy. Progress has stemmed from the development of reasonably accurate and efficiently computed all-atom potential functions as well as effective conformational sampling strategies appropriate for searching a highly rugged energy landscape, both driven by feedback from structure prediction and design tests. A unified energetic and kinematic framework in the Rosetta program allows a wide range of molecular modeling problems, from fibril structure prediction to RNA folding to the design of new protein interfaces, to be readily investigated and highlights areas for improvement. The methodology enables the creation of novel molecules with useful functions and holds promise for accelerating experimental structural inference. Emerging connections to crystallographic phasing, NMR modeling, and lower-resolution approaches are described and critically assessed.
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Cites
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Predicting protein structures with a multiplayer online game
Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., Beenen, M., Leaver-Fay, A., Baker, D., Popović, Z., & players, F. (2010) · Nature · doi:10.1038/nature09304 · Cited by 1,368
Self-citationBiological and Chemical Approaches to Diseases of Proteostasis Deficiency
Powers, E. T., Morimoto, R. I., Dillin, A., Kelly, J. W., & Balch, W. E. (2009) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.052308.114844 · Cited by 1,149
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Metadata
- Authors
- Das, R., & Baker, D.
- Venue
- Annual Review of Biochemistry
- Publisher
- Annual Reviews
- Published
- 2008-06-01
- Volume
- 77
- Issue
- 1
- Pages
- 363-382
- ISSN
- 0066-4154 (ISSN-L)
- 1545-4509
- OpenAlex
- W2146134187
- Cited by
- 989
- References
- 87
- FWCI
- 3.38
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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