| Abstract | The near neutral pKₐ of histidine is commonly exploited to engineer pH-sensitive biomolecules. For example, histidine mutations introduced in the complementarity-determining region (CDR) of therapeutic antibodies can enhance selectivity for antigens in the acidic microenvironment of solid tumors or increase dissociation rates in the acidic early endosomes of cells. While solvent-exposed histidines typically have a pKₐ near 6.5, interacting histidines can experience pKₐ shifts of up to 4 pH units in either direction, making histidine one of the most variable titratable residues. To assist in selecting potential histidine mutation sites, pKₐ prediction software should achieve an accuracy significantly better than the current standard of around 1.0 pH unit. However, the limited availability of experimental histidine pKₐ measurements hinders the use of AI-based methods. This study evaluates histidine pKₐ predictions using Amber force field electrostatics combined with a continuum solvent model, previously calibrated in the solvated interaction energy (SIE) function for binding affinity predictions. By incorporating limited rotameric sampling, proton optimization, and an empirical correction for buried side-chains, the method achieves a mean unsigned error of 0.4 pH units across a diverse set of 91 histidines from 38 distinct protein structures obtained from the PKAD database. This approach should improve the in-silico design of pH-responsive mutations. The method is implemented in the software program JustHISpKa (https://mm.nrc-cnrc.gc.ca/software/JustHISpKa). |
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