Multi-Scale Residue Interaction Learning for Protein pKa Prediction via Physicochemical Graph Representation Networks

Abstract

Accurate prediction of the acid dissociation constants of ionizable residues is a foundational challenge in structural biology, impacting protein engineering, enzyme design, and the understanding of pH-dependent conformational dynamics. Traditional empirical methods, while computationally efficient, often fail to capture the delicate interplay between local chemical microenvironments and long-range electrostatic effects. This paper introduces a systems-oriented architecture for protein pKa prediction that leverages multi-scale residue interaction learning within a physicochemical graph representation framework. The model constructs a hierarchical graph in which atomic, residue, and protein-level features are embedded through physically motivated node attributes, including partial charges, solvent accessibility, and hydrogen-bonding capacities. Message passing is orchestrated across spatial scales, enabling the network to internalize both short-range inductive effects and global dielectric responses. From a large-scale systems perspective, we analyze the trade-offs between graph granularity, computational throughput, and predictive fidelity, highlighting how modular design choices enable deployment on heterogeneous computing clusters. Robustness is examined through perturbations of structural inputs and cross-family generalization, revealing that physically regularized multi-scale aggregation confers resilience against conformational noise. Fairness considerations are addressed by auditing prediction discrepancies across different amino acid types and buried versus surface-exposed residues, leading to architectural adjustments that mitigate systematic biases. The paper further discusses infrastructure and sustainability, including containerized microservice deployment, energy-efficient inference, and policy implications for AI-driven molecular property servers. By reconciling biophysical rigor with scalable system engineering, the proposed framework illustrates how graph-based multi-scale learning can serve as a responsible, interpretable, and deployable component of the computational structural biology ecosystem.