立体选择性
定向分子进化
亚胺
生物催化
化学
蛋白质工程
产量(工程)
定向进化
立体化学
酶
催化作用
组合化学
计算生物学
生物化学
生物
反应机理
基因
材料科学
突变体
冶金
作者
J Gilbert Eric,Elina Siirola,Charles M. Moore,Arkadij Kummer,Markus Stoeckli,Michael Faller,Caroline Bouquet,Fabian Eggimann,Mathieu Ligibel,Dan Huynh,Geoffrey J. Cutler,Luca Siegrist,Richard A. Lewis,Anne-Christine Acker,Ernst Freund,Elke Koch,Markus Vogel,Holger Schlingensiepen,Edward J. Oakeley,Radka Šnajdrová
出处
期刊:ACS Catalysis
[American Chemical Society]
日期:2021-09-24
卷期号:11 (20): 12433-12445
被引量:77
标识
DOI:10.1021/acscatal.1c02786
摘要
Biocatalysis is an effective tool to access chiral molecules that are otherwise hard to synthesize or purify. Time-efficient processes are needed to develop enzymes that adequately perform the desired chemistry. We evaluated machine-directed evolution as an enzyme engineering strategy using a moderately stereoselective imine reductase as the model system. We compared machine-directed evolution approaches to deep mutational scanning (DMS) and error-prone PCR. Within one cycle, it was found that machine-directed evolution yielded a library of high-activity mutants with a dramatically shifted activity distribution compared to that of traditional directed evolution. Structure-guided analysis revealed that linear additivity might provide a simple explanation for the effectiveness of machine-directed evolution. The most active and selective enzyme mutant, which was identified through DMS and error-prone PCR, was used for the gram-scale synthesis of the H4 receptor antagonist ZPL389 with full conversion, > 99% ee (R), and a 72% yield.
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