Methanol tolerance upgrading of Proteus mirabilis lipase by machine learning‑assisted directed evolution  

作　　者：Rui Ma Yingnan Li Meng Zhang Fei Xu 

机构地区：[1]Ministry of Education Key Laboratory of Industrial Biotechnology,School of Biotechnology,Jiangnan University,Wuxi 214122,China

出　　处：《Systems Microbiology and Biomanufacturing》2023年第3期427-439,共13页系统微生物学与生物制造（英文）

基　　金：the National Natural Science Foundation of China(No.22078129);the Fundamental Research Funds for the Central Universities(No.JUSRP121014).

摘　　要：For many crucial industrial applications,enzyme-catalyzed processes take place in harsh organic solvent environments.However,it remains a challenging problem to improve enzyme stability in organic solvents.This study utilized the MLDE(machine learning-assisted directed evolution)protocol to improve the methanol tolerance of Proteus mirabilis lipase(PML).The machine learning(ML)models were trained based on 266 combinatorial mutants.Using top 3 in 22 regression models based on evaluation of tenfold cross-validation,the fitness landscape of the 8000 full-space combinatorial mutants was predicted.All mutants in the restricted library showed higher methanol tolerance,among which the methanol tolerance of G202N/K208G/G266S(NGS)was up to 13-fold compared with the wild-type.Molecular dynamics(MD)simulation showed that reconstructing of critical hydrogen bond network in the mutant region of NGS provides a more stable local structure.This compact structure may improve the methanol tolerance by preventing organic solvent molecules into the activity site and resisting structural destruction.This work provides a successful case of evolution guided by ML for higher organic solvent tolerance of enzyme,and may also be a reference for broad enzyme modifications.

关 键 词：Organic solvent tolerance Enzyme engineering Fitness prediction Site striation mutagenesis Molecular dynamics 

分 类 号：O62[理学—有机化学]