Learning Enzyme Optimal pH Ranges from Multimodal Molecular Representations.

Enzyme optimal pH is a key determinant of catalytic activity, yet existing computational methods often treat it as a fixed value and rely mainly on sequence features. In reality, enzyme activity is stable over a pH range and is strongly influenced by three-dimensional structural context. Structural information is underutilized due to limited experimental data. To address this gap, we present DeepPH, a structure-aware framework that models enzyme optimal pH as an interval regression problem to capture inherent uncertainty. DeepPH integrates sequence embeddings with residue-level features from predicted protein structures and encodes three-dimensional geometry using spatial radius graphs and E(3)-equivariant message-passing networks. An attention mechanism adaptively fuses biochemical and structural information. We further conducted downstream analyses, including residue-level attention, solvent accessibility, and three-dimensional visualization, to uncover the biochemical and structural determinants learned by the model. Case studies demonstrate that interval predictions more accurately reflect enzymes with broad pH activity profiles. Extensive experiments show that DeepPH outperforms existing methods under both standard and interval-aware evaluations and generalizes well to extreme-length sequences. The code, datasets, and supplementary materials are publicly available at https://github.com/LabJunBMI/DeepPH.git.