EnzyFuse: Cross-View Interaction over Spatial Neighborhoods for Catalytic Residue Prediction

Predicting catalytic residues supports mechanistic annotation and enzyme engineering. When annotated homologs are scarce, prediction can draw on evidence from the protein's sequence, evolutionary and structural views. The challenge is to learn from both view-specific evidence and its organization across spatially neighboring residues. We introduce EnzyFuse, a task-specific multi-view model built on pretrained sequence and alignment encoders and equivariant geometric message passing. Directly supervised view branches provide residue predictions, while an interaction branch combines their representations over spatial neighborhoods. Catalytic partner retrieval reuses the residue annotations as structured auxiliary supervision, identifying catalytic partners near an annotated anchor during training. A learned gate combines branch logits during training; inference uses a validation-selected average of the available logits. We evaluate EnzyFuse under joint sequence–structure similarity control, with post-split audits and controlled ablations to assess generalization and component contributions. Ablations and inference-time view masking indicate that evolutionary and structural information both contribute, and that the gain over a three-branch late-fusion model arises when spatial interaction is combined with catalytic partner retrieval. Under this protocol, a single EnzyFuse model outperforms all evaluated baselines, including a multi-model ensemble, in protein-averaged area under the precision–recall curve. This advantage persists on the subset with no detected neighbor among catalytic-labeled training proteins.