Explainable Deep Graph Framework for Deciphering Electrostatic Determinants of Protein Residue Ionization
Keywords:
deep graph networks, protein ionization, electrostatic determinants, explainable artificial intelligence, molecular systems, pKa prediction, fairness, computational infrastructureAbstract
The accurate prediction of protein residue ionization states under physiological conditions remains a foundational challenge in structural biology, with direct repercussions for drug design, enzyme engineering, and the understanding of macromolecular recognition. Traditional physics-based tools, while grounded in continuum electrostatics, often struggle to capture the nuanced microenvironments that shift pKa values of ionizable residues, while purely empirical methods lack transferability across diverse protein families. This paper presents a system-level perspective on an explainable deep graph framework that integrates graph neural networks with physically inspired feature engineering to decode the electrostatic determinants of residue ionization. The framework treats each ionizable residue as a node within a protein graph, where edges encode both covalent topology and spatial proximity, allowing the model to learn context-dependent pKa shifts directly from structural data. A central contribution of this work is the deliberate emphasis on architectural transparency and post-hoc interpretability, enabling the extraction of electrostatic determinants such as local hydrogen bonding, desolvation effects, and charge-charge interactions without sacrificing predictive accuracy. We examine the entire deployment pipeline, from large-scale data ingestion of curated pKa databases and structure repositories to the training of attention-based graph models and their validation on benchmark sets. The discussion extends to system robustness under structural perturbations, fairness across underrepresented residue types such as cysteine and histidine, and the environmental sustainability of training large models. Policy and governance dimensions, including reproducibility standards, open-source model dissemination, and the responsible use of AI-driven predictions in pharmaceutical pipelines, are thoroughly analyzed. Through this comprehensive lens, we argue that explainability is not an optional add-on but a critical design requirement for machine learning systems operating in high-stakes molecular sciences.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
