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Proton Binding to Proteins: p K a Calculations with Explicit and Implicit Solvent Models
Abstract
Ionizable residues play important roles in protein structure and activity, and proton binding is a valuable reporter of electrostatic interactions in these systems. We use molecular dynamics free energy simulations (MDFE) to compute proton pKa shifts, relative to a model compound in solution, for three aspartate side chains in two proteins. Simulations with explicit solvent and with an implicit, dielectric continuum solvent are reported. The implicit solvent simulations use the generalized Born (GB) model, which provides an approximate, analytical solution to Poisson's equation. With explicit solvent, the direction of the pKa shifts is correct in all three cases with one force field (AMBER) and in two out of three cases with another (CHARMM). For two aspartates, the dielectric response to ionization is found to be linear, even though the separate protein and solvent responses can be nonlinear. For thioredoxin Asp26, nonlinearity arises from the presence of two substates that correspond to the two possible orientations of the protonated carboxylate. For this side chain, which is partly buried and has a large pKa upshift, very long simulations are needed to correctly sample several slow degrees of freedom that reorganize in response to the ionization. Thus, nearby Lys57 rotates to form a salt bridge and becomes buried, while three waters intercalate along the opposite edge of Asp26. Such strong and anisotropic reorganization is very difficult to predict with Poisson-Boltzmann methods that only consider electrostatic interactions and employ a single protein structure. In contrast, MDFE with a GB dielectric continuum solvent, used for the first time for pKa calculations, can describe protein reorganization accurately and gives encouraging agreement with experiment and with the explicit solvent simulations.
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Cited by
CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
Hwang, W., Austin, S. L., Blondel, A., Boittier, E. D., Boresch, S., Buck, M., Buckner, J., Caflisch, A., Chang, H.-T., Cheng, X., et al. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04100 · Cited by 136
Modeling covalent-modifier drugs.
Awoonor-Williams, E., Walsh, A. G., & Rowley, C. N. (2017) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2017.05.009 · Cited by 90
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
Metadata
- Authors
- Simonson, T., Carlsson, J., & Case, D. A.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2004-03-12
- Volume
- 126
- Issue
- 13
- Pages
- 4167-4180
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W2010597259
- Cited by
- 284
- References
- 0
- FWCI
- 6.89
- Type
- journal article
- Language
- en
- Open access
- closed
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