Navigating the Sequence-Function Landscape: AI-Driven Discovery of Unseen and Synergistic Mutations in an Amine Transaminase

Transaminases are essential biocatalysts for asymmetric synthesis in the pharmaceutical and fine chemical industries. Here, we report the application of 3DM Engineeringan AI-driven protein engineering platformto optimize transaminase function by systematically exploring sequence-activity landscapes beyond those represented in the training data set. Our approach integrated the identification of hotspots from substrate tunnel analysis, enabling the construction of a focused, high-quality variant library targeting 53 residues for mutagenesis, which were subsequently used to train a protein language model. Further exploration of the sequence space identified mutations with previously unknown functional utility as salient targets for combination. The resulting higher-order variants displayed up to 21-fold improvement in catalytic efficiency and superior performance in the stereoselective synthesis of (S)-1-(2-chlorophenyl)ethanamine, achieving complete conversion and high enantiomeric excess (>99% ee). These results highlight the power of combining systematic hotspot identification with AI-driven exploration to discover unseen enzyme variants.