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Cited by 266

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

SYMMETRY, FORM, AND SHAPE: Guiding Principles for Robustness in Macromolecular Machines

Tama, Florence, Brooks, Charles L.·2006·Annual Review of Biophysics and Biomolecular Structure

Abstract

Computational studies of large macromolecular assemblages have come a long way during the past 10 years. With the explosion of computer power and parallel computing, timescales of molecular dynamics simulations have been extended far beyond the hundreds of picoseconds timescale. However, limitations remain for studies of large-scale conformational changes occurring on timescales beyond nanoseconds, especially for large macromolecules. In this review, we describe recent methods based on normal mode analysis that have enabled us to study dynamics on the microsecond timescale for large macromolecules using different levels of coarse graining, from atomically detailed models to those employing only low-resolution structural information. Emerging from such studies is a control principle for robustness in Nature's machines. We discuss this idea in the context of large-scale functional reorganization of the ribosome, virus particles, and the muscle protein myosin.

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Crossref · 10.1146/annurev.biophys.35.040405.102010

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Metadata

Authors
Tama, F., & Brooks, C. L.
Venue
Annual Review of Biophysics and Biomolecular Structure
Published
2006-06
Volume
35
Issue
1
Pages
115-133
ISSN
  • 1056-8700 (ISSN-L)
  • 1545-4266
OpenAlex
W2142872962
Cited by
266
References
4
FWCI
8.96
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: SYMMETRY, FORM, AND SHAPE: Guiding Principles for Robustness in Macromolecular Machines. https://app.cochat.ai/papers/10.1146/annurev.biophys.35.040405.102010
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Tama, F., & Brooks, C. L. (2006). SYMMETRY, FORM, AND SHAPE: Guiding Principles for Robustness in Macromolecular Machines. Annual Review of Biophysics and Biomolecular Structure. https://doi.org/10.1146/annurev.biophys.35.040405.102010

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.