Very fast empirical prediction and rationalization of protein pK _a values

A very fast empirical method is presented for structure-based protein pK_a prediction and rationalization. The desolvation effects and intra-protein interactions, which cause variations in pK_a values of protein ionizable groups, are empirically related to the positions and chemical nature of the groups proximate to the pK_a sites. A computer program is written to automatically predict pK_a values based on these empirical relationships within a couple of seconds. Unusual pK_a values at buried active sites, which are among the most interesting protein pK_a values, are predicted very well with the empirical method. A test on 233 carboxyl, 12 cysteine, 45 histidine, and 24 lysine pK_a values in various proteins shows a root-mean-square deviation (RMSD) of 0.89 from experimental values. Removal of the 29 pK_a values that are upper or lower limits results in an RMSD = 0.79 for the remaining 285 pK_a values.