The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Maximum Allowed Solvent Accessibilites of Residues in Proteins
Open accessAbstract
The relative solvent accessibility (RSA) of a residue in a protein measures the extent of burial or exposure of that residue in the 3D structure. RSA is frequently used to describe a protein's biophysical or evolutionary properties. To calculate RSA, a residue's solvent accessibility (ASA) needs to be normalized by a suitable reference value for the given amino acid; several normalization scales have previously been proposed. However, these scales do not provide tight upper bounds on ASA values frequently observed in empirical crystal structures. Instead, they underestimate the largest allowed ASA values, by up to 20%. As a result, many empirical crystal structures contain residues that seem to have RSA values in excess of one. Here, we derive a new normalization scale that does provide a tight upper bound on observed ASA values. We pursue two complementary strategies, one based on extensive analysis of empirical structures and one based on systematic enumeration of biophysically allowed tripeptides. Both approaches yield congruent results that consistently exceed published values. We conclude that previously published ASA normalization values were too small, primarily because the conformations that maximize ASA had not been correctly identified. As an application of our results, we show that empirically derived hydrophobicity scales are sensitive to accurate RSA calculation, and we derive new hydrophobicity scales that show increased correlation with experimentally measured scales.
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Cites
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Cited by
Starr, T. N., Greaney, A. J., Hilton, S. K., Ellis, D., Crawford, K. H. D., Dingens, A. S., Navarro, M. J., Bowen, J. E., Tortorici, M. A., Walls, A. C., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.012 · Cited by 1,956
Ali, S., Hassan, M., Islam, A., & Ahmad, F. (2014) · Current Protein & Peptide Science · doi:10.2174/1389203715666140327114232 · Cited by 447
Multiplex assessment of protein variant abundance by massively parallel sequencing
Matreyek, K. A., Starita, L. M., Stephany, J. J., Martin, B., Chiasson, M. A., Gray, V. E., Kircher, M., Khechaduri, A., Dines, J. N., Hause, R. J., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0122-z · Cited by 436
Causes of evolutionary rate variation among protein sites
Echave, J., Spielman, S. J., & Wilke, C. O. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.18 · Cited by 328
Self-citationImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins
Abanades, B., Wong, W. K., Boyles, F., Georges, G., Bujotzek, A., & Deane, C. M. (2023) · Communications Biology · doi:10.1038/s42003-023-04927-7 · Cited by 298
Metadata
- Authors
- Tien, M. Z., Meyer, A. G., Sydykova, D. K., Spielman, S. J., & Wilke, C. O.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-11-21
- Volume
- 8
- Issue
- 11
- Pages
- e80635
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2097356092
- Cited by
- 511
- References
- 39
- FWCI
- 5.65
- Type
- journal article
- Language
- en
- Open access
- gold
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