Structure‐ and Data‐Driven Protein Engineering of Transaminases for Improving Activity and Stereoselectivity

Abstract

AbstractAmine transaminases (ATAs) are powerful biocatalysts for the stereoselective synthesis of chiral amines. Machine learning provides a promising approach for protein engineering, but activity prediction models for ATAs remain elusive due to the difficulty of obtaining high‐quality training data. Thus, we first created variants of the ATA from Ruegeria sp. (3FCR) with improved catalytic activity (up to 2000‐fold) as well as reversed stereoselectivity by a structure‐dependent rational design and collected a high‐quality dataset in this process. Subsequently, we designed a modified one‐hot code to describe steric and electronic effects of substrates and residues within ATAs. Finally, we built a gradient boosting regression tree predictor for catalytic activity and stereoselectivity, and applied this for the data‐driven design of optimized variants which then showed improved activity (up to 3‐fold compared to the best variants previously identified). We also demonstrated that the model can predict the catalytic activity for ATA variants of another origin by retraining with a small set of additional data.