Accurate, conformation-dependent predictions of solvent effects on protein ionization constants - PubMed (original) (raw)
Accurate, conformation-dependent predictions of solvent effects on protein ionization constants
P Barth et al. Proc Natl Acad Sci U S A. 2007.
Abstract
Predicting how aqueous solvent modulates the conformational transitions and influences the pKa values that regulate the biological functions of biomolecules remains an unsolved challenge. To address this problem, we developed FDPB_MF, a rotamer repacking method that exhaustively samples side chain conformational space and rigorously calculates multibody protein-solvent interactions. FDPB_MF predicts the effects on pKa values of various solvent exposures, large ionic strength variations, strong energetic couplings, structural reorganizations and sequence mutations. The method achieves high accuracy, with root mean square deviations within 0.3 pH unit of the experimental values measured for turkey ovomucoid third domain, hen lysozyme, Bacillus circulans xylanase, and human and Escherichia coli thioredoxins. FDPB_MF provides a faithful, quantitative assessment of electrostatic interactions in biological macromolecules.
Conflict of interest statement
The authors declare no conflict of interest.
Figures
Fig. 1.
Accuracy of the convergence of the FDPB_MF compared with the solution obtained by exhaustively sampling all of the discrete states of the system. The two different predicted titration curves of Asp 27 correspond to the sum of the probability of the deprotonated rotamers of Asp-27 as a function of the pH. The “exact titration” curve (black) was obtained by enumerating all of the discrete states of the system and is equal to the sum at a given pH of the Boltzmann weights of the discrete states occupied by the deprotonated rotamers. The FDPB_MF curve (gray) is derived by relaxing a rotameric probability distribution with the FDPB_MF method. It corresponds to the probability that minimizes at a given pH the electrostatic free energy of the protein after optimization by the SCMF algorithm.
Fig. 2.
Synopsis of the pKa predictions performed by FDPB_MF and comparison with other methods: PROPKA (18), MCCE (10), and EGAD (9). rsmds and maximal errors to the experimental values are in pH units. Each line and _r_2 value corresponds to the best linear regression fit to the data and the correlation coefficient, respectively.
Fig. 3.
Good agreement between observed and predicted conformational changes induced by the protonation of Glu 172 in B. circulans xylanase. Figures were generated by using Pymol (
). As discussed in ref. , the crystal structure solved at an apparent pH of 4.0 is likely to be consistent with Glu 172 being protonated. Protons on the x-ray structures were added with the program Reduce (32). At the bottom, distance changes are provided in angstroms. The first and second numbers represent distances between protons and heteroatoms and distances between heteroatoms, respectively.
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