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Improving the Continuum Dielectric Approach to Calculating p Kas of Ionizable Groups in Proteins
Abstract
The pH-dependent characteristics of a protein are determined by the p K a s of its titratable residues. Continuum electrostatic approaches provide a fast means of calculating the p K a s of the titratable residues in proteins immersed in aqueous ionic solution. Here, we show how optimization of the parameters used in continuum electrostatic calculations can lead to significant improvements in the accuracy of p K a predictions. Dependence on the protein dielectric constant is studied, and two classes of ionizable sites are identified: (1) a large number of mostly solvent exposed residues for which the best p K a is calculated if the protein dielectric constant is set close to that of the aqueous solvent; and (2) a small number of mostly buried residues for which the best p K a is calculated with a lower site-specific protein dielectric constant. These two classes of ionizable sites can be distinguished using a criterion based on desolvation energy. A priori determination of the optimum protein dielectric constant for the second class is not easy, but satisfactory results are obtained if it is assigned in the range 10−20. The accuracy of the calculated p K a s is influenced by the assignment of proton positions and we suggest modifications to the OPLS parameter set that eliminate the need to construct hypothetical, and therefore error prone, proton positions during the calculation procedure. For the test set of proteins studied, the final recommended protocol enables the tautomers of histidine residues to be assigned automatically, the p K a s of residues in the “high dielectric constant” class to be calculated to within 0.7 p K a units, and the p K a s of residues in the “low dielectric constant” class to be calculated to within 1.6 p K a units.
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Cited by
Very fast empirical prediction and rationalization of protein pKa values
Li, H., Robertson, A. D., & Jensen, J. H. (2005) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20660 · Cited by 2,054
Anandakrishnan, R., Aguilar, B., & Onufriev, A. V. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks375 · Cited by 1,872
H++: a server for estimating pKas and adding missing hydrogens to macromolecules
Gordon, J. C., Myers, J. B., Folta, T., Shoja, V., Heath, L. S., & Onufriev, A. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki464 · Cited by 1,726
Fast empirical pKa prediction by Ewald summation
Krieger, E., Nielsen, J. E., Spronk, C. A. E. M., & Vriend, G. (2006) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2006.02.009 · Cited by 473
Swails, J. M., York, D. M., & Roitberg, A. E. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct401042b · Cited by 268
Protein p K a Prediction with Machine Learning
Cai, Z., Luo, F., Wang, Y., Li, E., & Huang, Y. (2021) · ACS Omega · doi:10.1021/acsomega.1c05440 · Cited by 45
Polarization and polarizability assessed by protein amide acidity
Hernández, G., Anderson, J. S., & LeMaster, D. M. (2009) · Biochemistry · doi:10.1021/bi900526z · Cited by 31
Gallo, G., Brameyer, S., Kuppermann, S., Schneider, S., Kielkowski, P., & Jung, K. (2026) · Journal of Bacteriology · doi:10.1128/jb.00542-25 · Cited by 4
Metadata
- Authors
- Demchuk, E., & Wade, R. C.
- Venue
- The Journal of Physical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 1996-01-01
- Volume
- 100
- Issue
- 43
- Pages
- 17373-17387
- ISSN
- 0022-3654 (ISSN-L)
- 1541-5740
- OpenAlex
- W1995260626
- Cited by
- 246
- References
- 0
- FWCI
- 3.96
- Type
- journal article
- Language
- en
- Open access
- closed
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