Enhanced stereodivergent evolution of carboxylesterase for efficient kinetic resolution of near-symmetric esters through machine learning.

Carboxylesterases serve as potent biocatalysts in the enantioselective synthesis of chiral carboxylic acids and esters. However, naturally occurring carboxylesterases exhibit limited enantioselectivity, particularly toward ethyl 3-cyclohexene-1-carboxylate (CHCE, S1), due to its nearly symmetric structure. While machine learning effectively expedites directed evolution, the lack of models for predicting the enantioselectivity for carboxylesterases has hindered progress, primarily due to challenges in obtaining high-quality training datasets. In this study, we devise a high-throughput method by coupling alcohol dehydrogenase to determine the apparent enantioselectivity of the carboxylesterase AcEst1 from Acinetobacter sp. JNU9335, generating a high-quality dataset. Leveraging seven features derived from biochemical considerations, we quantitively describe the steric, hydrophobic, hydrophilic, electrostatic, hydrogen bonding, and π-π interaction effects of residues within AcEst1. A robust gradient boosting regression tree model is trained to facilitate stereodivergent evolution, resulting in the enhanced enantioselectivity of AcEst1 toward S1. Through this approach, we successfully obtain two stereocomplementary variants, DR3 and DS6, demonstrating significantly increased and reversed enantioselectivity. Notably, DR3 and DS6 exhibit utility in the enantioselective hydrolysis of various symmetric esters. Comprehensive kinetic parameter analysis, molecular dynamics simulations, and QM/MM calculations offer insights into the kinetic and thermodynamic features underlying the manipulated enantioselectivity of DR3 and DS6.