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J Chem Theory Comput. 2011 Jul 12;7(7):2284-95. doi: 10.1021/ct200133y. Epub 2011 Jun 9.

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

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Department of Chemistry and Center for Computational Molecular Sciences, University of Copenhagen , Universitetsparken 5, 2100 Copenhagen, Denmark.


The new empirical rules for protein pKa 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 pKa shifts of active site residues and ionizable ligand groups in protein-ligand complexes. We present new algorithms that allow pKa 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 pKa prediction may be especially difficult. This is a general improvement to PROPKA and is applied to proteins with and without ligands.


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