Improved Treatment of Ligands and Coupling Effects in Empirical Calculation and Rationalization of pKa Values

The new empirical rules for protein pK(a) predictions implemented in the PROPKA3.0 software package (Olsson et al. J. Chem. Theory Comput. 2010, 7, 525-537) have been extended to the prediction of pK(a) shifts of active site residues and ionizable ligand groups in protein ligand complexes. We present new algorithms that allow pK(a) shifts due to inductive (i.e., covalently coupled) intraligand interactions, as well as noncovalently coupled interligand interactions in multiligand complexes, to be included in the prediction. The number of different ligand chemical groups that are automatically recognized has been increased to 18, and the general implementation has been changed so that new functional groups can be added easily by the user, aided by a new and more general protonation scheme. Except for a few cases, the new algorithms in PROPKA3.1 are found to yield results similar to or better than those obtained with PROPKA2.0 (Bas et al. Proteins: Struct., Funct., Bioinf 2008, 73, 765-783). Finally, we present a novel algorithm that identifies noncovalently coupled ionizable groups, where pK(a) prediction may be especially difficult. This is a general improvement to PROPKA and is applied to proteins with and without ligands.