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.