Evaluating MSA-Derived Evolutionary Information in Enzyme Recommendation for Biocatalytic Reactions

Abstract Reaction-conditioned enzyme recommendation ranks candidate enzymes for a query chemical transformation, but the contribution of explicit evolutionary context to this task remains insufficiently characterized. Here, we present an exploratory evaluation of multiple sequence alignment (MSA)-derived evolutionary features, integrating full-sequence and pocket-level MSA Transformer representations into EnzymeCage and a benchmark-adapted Horizyn implementation through concatenation or gated fusion. On the fixed Enzyme-405 candidate pools, the evaluated EnzymeCage Pocket-MSA model achieved a Top-1 success rate of 20.3% versus 14.6% for the baseline, while Full + Pocket MSA achieved a Top-10 success rate of 61.4% versus 55.6%; changes on Orphan-335 and in the adapted Horizyn implementation varied across configurations. Although a focused three-seed EnzymeCage baseline–Pocket comparison did not establish a consistent improvement across training runs and ranking cutoffs, analyses of MSA diversity, training-reference sequence similarity, and alignment-context removal characterize the observed effects and guide further evaluation of MSA-augmented enzyme recommendation.