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Charges in the hydrophobic interior of proteins
Open accessAbstract
Charges are inherently incompatible with hydrophobic environments. Presumably for this reason, ionizable residues are usually excluded from the hydrophobic interior of proteins and are found instead at the surface, where they can interact with bulk water. Paradoxically, ionizable groups buried in the hydrophobic interior of proteins play essential roles, especially in biological energy transduction. To examine the unusual properties of internal ionizable groups we measured the p K a of glutamic acid residues at 25 internal positions in a stable form of staphylococcal nuclease. Two of 25 Glu residues titrated with normal p K a near 4.5; the other 23 titrated with elevated p K a values ranging from 5.2–9.4, with an average value of 7.7. Trp fluorescence and far-UV circular dichroism were used to monitor the effects of internal charges on conformation. These data demonstrate that although charges buried in proteins are indeed destabilizing, charged side chains can be buried readily in the hydrophobic core of stable proteins without the need for specialized structural adaptations to stabilize them, and without inducing any major conformational reorganization. The apparent dielectric effect experienced by the internal charges is considerably higher than the low dielectric constants of hydrophobic matter used to represent the protein interior in electrostatic continuum models of proteins. The high thermodynamic stability required for proteins to withstand the presence of buried charges suggests a pathway for the evolution of enzymes, and it underscores the need to mind thermodynamic stability in any strategy for engineering novel or altered enzymatic active sites in proteins.
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Cited by
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Isom, D. G., Castañeda, C. A., Cannon, B. R., & García-Moreno E., B. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1010750108 · Cited by 466
Self-citationStructure of the CLC-1 chloride channel from Homo sapiens
Park, E., & MacKinnon, R. (2018) · eLife · doi:10.7554/elife.36629 · Cited by 109
Protein p K a Prediction by Tree-Based Machine Learning
Chen, A. Y., Lee, J., Damjanovic, A., & Brooks, B. R. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01257 · Cited by 37
Driving forces of translocation through bacterial translocon SecYEG.
Knyazev, D. G., Kuttner, R., Zimmermann, M., Sobakinskaya, E., & Pohl, P. (2018) · The Journal of Membrane Biology · doi:10.1007/s00232-017-0012-9 · Cited by 33
Beecroft, S. J., Ayala, M., McGillivray, G., Nanda, V., Agolini, E., Novelli, A., Digilio, M. C., Dotta, A., Carrozzo, R., Clayton, J., et al. (2021) · Human Mutation · doi:10.1002/humu.24179 · Cited by 25
Metadata
- Authors
- Isom, D. G., Castañeda, C. A., Cannon, B. R., Velu, P. D., & García-Moreno E., B.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2010-08-26
- Volume
- 107
- Issue
- 37
- Pages
- 16096-16100
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2094575480
- Cited by
- 234
- References
- 0
- FWCI
- 6.14
- Type
- journal article
- Language
- en
- Open access
- green
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