GeoProt-GNN: Geometry-Aware Graph Neural Networks for Predicting Functional Ionization Landscapes in Protein Structures
Keywords:
protein ionization, graph neural networks, geometry-aware deep learning, pKa prediction, molecular systems, AI governance, sustainable computing, structural bioinformaticsAbstract
The prediction of functional ionization landscapes in proteins, encoded by the pKa values of titratable residues, is a fundamental challenge that underpins mechanistic understanding of enzyme catalysis, molecular recognition, and pH-dependent structural transitions. Traditional computational methods rooted in continuum electrostatics or empirical parameterization, while valuable, often fall short in capturing the subtle geometric and dynamic features that govern site-specific protonation equilibria. This work introduces GeoProt-GNN, a comprehensive system architecture that leverages geometry-aware graph neural networks to predict residue-level ionization states directly from three-dimensional protein structures. We present a holistic examination of the system-level design principles required to build, deploy, and govern such a predictive infrastructure. Our discussion moves beyond algorithmic novelty to emphasize structural trade-offs between all-atom resolution and computational tractability, the integration of equivariant message-passing mechanisms that respect roto-translational symmetries, and the design of multi-scale graph representations that balance local chemical detail with long-range electrostatic context. We detail the large-scale training pipeline that amalgamates curated structural data from the Protein Data Bank and AlphaFold Database, highlighting data governance, provenance tracking, and the sustainability metrics of model development. Robustness and fairness are analyzed through the lens of representation bias across protein families and the need for well-calibrated uncertainty quantification in safety-critical applications such as drug design. Furthermore, we outline governance frameworks and policy implications for the responsible deployment of ionization landscape predictors, including model documentation standards, biosecurity considerations, and alignment with FAIR data principles. The modular architecture of GeoProt-GNN enables cross-domain extensions to redox potential prediction, metal-binding site identification, and the design of pH-responsive biological systems. By framing the system as a sociotechnical infrastructure, we provide a blueprint for the next generation of machine learning tools in structural biology that are not only accurate but also robust, fair, sustainable, and ethically aligned.
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