A statistical approach to the prediction of pKa values in proteins

We propose a simple model for the calculation of pK. values of ionizable residues in proteins. It is based on the premise that the pK, shift of ionizable residues is linearly correlated to the interaction between a particular residue and the local environment created by the surrounding residues. Despite its simplicity, the model displays good prediction performance. Under the sixfold cross test prediction over a data set of 405 experimental pKa values in 73 protein chains with known structures, the root-mean-square deviation (RMSD) between the experimental and calculated pKa was found to be 0.77. The accuracy of this model increases with increasing size of the data set: the RMSD is 0.609, for glutamate (the largest data set with 141 sites) and similar to 1 pH unit for lysine, with a data set containing 45 sites.