Dynamic Conformation-Aware Deep Learning for Residue Ionization Prediction in Flexible Protein Systems

Abstract

The accurate prediction of ionizable residue pKa values in proteins is fundamental to understanding pH-dependent structure, function, and molecular recognition. Traditional physics-based methods, while interpretable, suffer from systematic errors when applied to flexible systems where conformational dynamics modulate local electrostatic environments. This work presents a dynamic conformation-aware deep learning framework designed to predict residue ionization states across an ensemble of protein conformations, moving decisively beyond the static single-structure paradigm. The system integrates three-dimensional geometric representations with multi-conformer sampling through an architecture that combines graph neural networks, time-aware transformers, and equivariant message passing to capture pH-dependent protonation probabilities in a conformationally resolved manner. We examine the entire lifecycle of such a system, from data infrastructure and featurization to training strategies, deployment scalability, and the broader governance considerations essential for its adoption in drug discovery, enzyme engineering, and synthetic biology. A detailed analysis of structural trade-offs reveals the balance between conformational granularity and computational tractability, the robustness of the model under input perturbations, and the fairness implications arising from training data biases across protein families and organismal sources. The paper argues that sustainable, equitable deployment of dynamic deep learning models for residue ionization requires not only architectural innovation but also transparent data curation practices, continuous monitoring of performance drifts, and the articulation of policy frameworks that guide the use of predictive biochemical models in high-stakes biomedical decision-making.