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

Autor: Dou Z; Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 214122, Wuxi, Jiangsu, P. R. China.; Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, College of Pharmacy, Zhejiang University of Technology, 310014, Hangzhou, Zhejiang, P. R. China., Chen X; Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 214122, Wuxi, Jiangsu, P. R. China., Zhu L; Environmental Research Institute, Shandong University, Jimo, 266237, Qingdao, Shandong, P. R. China., Zheng X; Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 214122, Wuxi, Jiangsu, P. R. China., Chen X; Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 214122, Wuxi, Jiangsu, P. R. China., Xue J; Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 214122, Wuxi, Jiangsu, P. R. China., Niwayama S; Graduate School of Engineering, Muroran Institute of Technology, Muroran, Hokkaido, 050-8585, Japan., Ni Y; Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 214122, Wuxi, Jiangsu, P. R. China. yni@jiangnan.edu.cn., Xu G; Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 214122, Wuxi, Jiangsu, P. R. China. guochaoxu@jiangnan.edu.cn.; The Research Center of Chiral Drugs, Innovation Research Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 201203, Shanghai, China. guochaoxu@jiangnan.edu.cn.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2024 Oct 20; Vol. 15 (1), pp. 9057. Date of Electronic Publication: 2024 Oct 20.
DOI: 10.1038/s41467-024-53191-8
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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