Application of the Gaussian dielectric boundary in Zap to the prediction of protein pKa values

Abstract

The results of two rounds of blind pK(a) predictions for ionizable residues in staphylococcal nuclease using OpenEye's legacy protein pK(a) prediction program based on the Zap Poisson-Boltzmann solver were submitted to the 2009 prediction challenge organized by the Protein pK(a) Cooperative and first round predictions were discussed at the corresponding June 2009 Telluride conference. To better understand these results, 21 additional sets of predictions were performed with the same program, varying the internal dielectric, reference pK(a), partial charge set, and dielectric boundary. The internal dielectric (ε(p)) and dielectric boundary were the two most important factors contributing to the quality of the predictions. Although the lowest overall errors were observed with a molecular dielectric boundary at ε(p) = 8, predictions using a smooth Gaussian dielectric boundary performed almost as well at lower ε(p) values because the Gaussian boundary implicitly accounts for a significant level of solvent penetration. Improved pK(a) predictions with the Gaussian boundary methodology will require better prediction and modeling of structural changes due to changes in ionization state, perhaps without resorting to the more exhaustive sampling of conformational states used by other recent continuum methods.