The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
The Stability Effects of Protein Mutations Appear to be Universally Distributed
Abstract
How the thermodynamic stability effects of protein mutations (DeltaDeltaG) are distributed is a fundamental property related to the architecture, tolerance to mutations (mutational robustness), and evolutionary history of proteins. The stability effects of mutations also dictate the rate and dynamics of protein evolution, with deleterious mutations being the main inhibitory factor. Using the FoldX algorithm that attempts to computationally predict DeltaDeltaG effects of mutations, we deduced the overall distributions of stability effects for all possible mutations in 21 different globular, single domain proteins. We found that these distributions are strikingly similar despite a range of sizes and folds, and largely follow a bi-Gaussian function: The surface residues exhibit a narrow distribution with a mildly destabilizing mean DeltaDeltaG ( approximately 0.6 kcal/mol), whereas the core residues exhibit a wider distribution with a stronger destabilizing mean ( approximately 1.4 kcal/mol). Since smaller proteins have a higher fraction of surface residues, the relative weight of these single distributions correlates with size. We also found that proteins evolved in the laboratory follow an essentially identical distribution, whereas de novo designed folds show markedly less destabilizing distributions (i.e. they seem more robust to the effects of mutations). This bi-Gaussian model provides an analytical description of the predicted distributions of mutational stability effects. It comprises a novel tool for analyzing proteins and protein models, for simulating the effect of mutations under evolutionary processes, and a quantitative description of mutational robustness.
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Cites
The FoldX web server: an online force field
Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., & Serrano, L. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki387 · Cited by 2,759
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Metadata
- Authors
- Tokuriki, N., Stricher, F., Schymkowitz, J., Serrano, L., & Tawfik, D. S.
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2007-06
- Volume
- 369
- Issue
- 5
- Pages
- 1318-1332
- ISSN
- 0022-2836 (ISSN-L)
- OpenAlex
- W2115503115
- Cited by
- 414
- References
- 4
- FWCI
- 5.03
- Type
- journal article
- Language
- en
- Open access
- closed
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