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Large Amplitude Elastic Motions in Proteins from a Single-Parameter, Atomic Analysis
Abstract
Normal mode analysis (NMA) is a leading method for studying long-time dynamics and elasticity of biomolecules. The method proceeds from complex semiempirical potentials characterizing the covalent and noncovalent interactions between atoms. It is widely accepted that such detailed potentials are essential to the success of NMA's. We show that a single-parameter potential is sufficient to reproduce the slow dynamics in good detail. Costly and inaccurate energy minimizations are eliminated, permitting direct analysis of crystal coordinates. The technique can be used for new applications, such as mapping of one crystal form to another by means of slow modes, and studying anomalous dynamics of large proteins and complexes.
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Putnam, C. D., Hammel, M., Hura, G. L., & Tainer, J. A. (2007) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583507004635 · Cited by 1,184
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Howard, R. J., Trudell, J. R., & Harris, R. A. (2014) · Pharmacological Reviews · doi:10.1124/pr.113.007468 · Cited by 56
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Metadata
- Authors
- Tirion, M. M.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 1996-08-26
- Volume
- 77
- Issue
- 9
- Pages
- 1905-1908
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2069777230
- Cited by
- 1,615
- References
- 0
- FWCI
- 2.85
- Type
- journal article
- Language
- en
- Open access
- closed
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