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 ...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.展开更多
基金the National Natural Science Foundation of China(No.22078129)the Fundamental Research Funds for the Central Universities(No.JUSRP121014).
文摘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.
