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

Abstract

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