Expansion and Additional Validation of PKA17: A Fast Real-Time and Web-Based pKa Predictor

Abstract

We have further improved and validated PKA17, our fast software for predicting p[Formula: see text] values of protein residues. The methodology employs coarse-grained lattice-based model of proteins. It was previously demonstrated to perform ca. an order of magnitude faster than such successful and widely used frameworks as PROPKA without losing accuracy of the calculations. In this paper, we report the following improvements: (i) We have expanded our training and testing sets of protein residues by 128%, from 442 to 1009 cases; (ii) we have added and parameterized PKA17’s capability to predict acidity constants of cysteine residues that are important in many biomedical applications, including but not limited to binding of such transition metal ions as copper(I) and platinum(II); (iii) we have carried out the comparison of accuracy of predicted Asp and Glu p[Formula: see text] values not only between PKA17 and PROPKA, but also with DelPhiPKa and H[Formula: see text]. The computational speed of PKA17 remains the highest of all the methods used in our studies, and the accuracy of PKA17 is somewhat inferior only to those of such more sophisticated methods as Multi-Conformation Continuum Electrostatic (MCCE) ones. For instance, the average unsigned deviations of predicted p[Formula: see text] values from the experiment for 416 Glu residues were found to be 0.706, 0.766, 0.867, and 0.520[Formula: see text]pH units when obtained with PROPKA, DelPhiPKa, H[Formula: see text], and PKA17, respectively (0.487[Formula: see text]pH units with PKA17 after refitting). The average unsigned errors for cysteine p[Formula: see text] values calculated with PROPKA, DelPhiPKa, H[Formula: see text], and PKA17 were 3.50, 2.06, 3.17, and 1.26[Formula: see text]pH units. PKA17 has also performed well in assessing the cysteine acidity constants of the CXXC motif of CopZ protein involved in binding of copper(I) metal ions. Our results demonstrate that the PKA17 methodology and current parameters are accurate and robust, and its computational speed makes it possible to be employed in large-scale p[Formula: see text] screening calculations and in constant-pH protein dynamics simulations. The resulting PKA17 software has been deployed online at http://kaminski.wpi.edu/PKA17/pka_calc.html .