Structure-to-Function Learning: Predicting Enzyme Catalytic Residue Activity Through pKa-Aware Graph Representations

Abstract

The accurate prediction of enzyme catalytic residue activity from structural data stands as one of the central challenges in computational biology, with profound implications for drug discovery, industrial biocatalysis, and protein engineering. While recent advances in deep learning have enabled remarkable progress in protein function prediction, most existing methods either treat atomic-level chemical environments in an overly idealized manner or fail to integrate the critical physicochemical property of ionizable residue pKa values into their representations. This paper presents a framework for structure-to-function learning that constructs pKa-aware graph representations of enzyme active sites, jointly capturing three-dimensional spatial organization, evolutionary sequence features, and protonation-state energetics. At the system level, we examine the architectural trade-offs involved in encoding pKa information through physically inspired feature engineering within message-passing neural networks, including the balance between precomputed pKa predictions and on-the-fly electrostatic calculations. We provide a comprehensive analysis of the infrastructure required for large-scale deployment, spanning distributed training strategies, data provenance pipelines, and model-serving latency constraints in high-throughput screening contexts. Robustness is addressed through systematic evaluation of model sensitivity to structural perturbations, conformational sampling, and pKa prediction errors propagated from upstream modules. Fairness and bias considerations are discussed with respect to the overrepresentation of certain protein families in structural databases and the implications for generalizability to understudied enzyme classes. Sustainability concerns related to the computational footprint of training graph networks on massive structural datasets are evaluated alongside emerging efficient architectures. Finally, we outline governance and policy recommendations for the responsible dissemination of predictive models that could inform biocatalyst design, highlighting intellectual property boundaries, dual-use considerations, and the need for open benchmarking standards. Through this multidisciplinary lens, the work positions pKa-aware graph learning not merely as a technical advance in bioinformatics, but as a complex socio-technical system whose design choices reverberate through scientific practice, industrial deployment, and regulatory frameworks.