Graph-Based Deep Learning Models for Predicting p <i>K</i> _a Values of Protein-Ionizable Residues via Physically Inspired Feature Engineering

The p<i>K</i>_a value of a protein-ionizable residue reflects its potency to donate a proton at a given pH value, which is essential for understanding a wide range of biological activity. Therefore, the accurate prediction of p<i>K</i>_a values of protein residues is crucial for understanding enzymatic activity and protein-ligand binding, which are fundamental to drug discovery. Despite significant time and resources being invested to develop computational methods for protein residue p<i>K</i>_a prediction, the accuracy of existing tools, such as the widely used PROPKA, remains limited. In this study, an integrated framework that fuses molecular dynamics simulations and deep learning models is proposed to improve the predictive accuracy of p<i>K</i>_a values for ionizable residues. Specifically, we employ high-throughput molecular modeling using the AMOEBA polarized force field to construct a protein structure data set enriched with atomic electrostatics and other physics-inspired features. Using the experimentally determined p<i>K</i>_a values from the PKAD-2 data set, we trained three graph-based neural network models. All three models demonstrated substantial improvements in prediction accuracy across four ionizable residue types, aspartic acid, glutamic acid, lysine, and histidine, when compared to PROPKA3.5.1, with the graph attention networks-based model exhibiting both high accuracy and strong generalizability when benchmarking against several recently published machine learning models. Beyond these improvements in predictive performance, feature importance analysis of the best-performing models revealed physically meaningful patterns of the descriptive features that aligned with the underlying biophysical principles governing protein residue p<i>K</i>_a values, most notably, the complexity of the local microenvironment and the atomic geometric arrangement within the protein structure. Together, the trained p<i>K</i>_a models and the curated dipole moment-enhanced data set based on a polarizable FF offer a valuable resource for the research community, with potential applications in early-stage drug target identification and protein engineering.