Practical Machine Learning-Assisted Design Protocol for Protein Engineering: Transaminase Engineering for the Conversion of Bulky Substrates

• 全文链接

蛋白质工程的实用机器学习辅助设计规程：用于大分子底物转化的转氨酶工程

关键词：

来源：

ACS Catalysis

全文链接：

//agri.nais.net.cn/topic/downloadFile/d048fe41-4ee4-4a54-bec0-5a184f5d6ec0

来源地址：

https://pubs.acs.org/doi/10.1021/acscatal.4c00987

资源所属：

饲料用酶工程

类型：

学术文献

语种：

英语

原文发布日期：

2024-04-12

摘要：

Protein engineering is essential for improving the catalytic performance of enzymes for applications in biocatalysis, in which machine learning provides an emerging approach for variant design. Transaminases are powerful biocatalysts for the stereoselective synthesis of chiral amines but one major challenge is their limited substrate scope. We present a general and practical variant design protocol for protein engineering to combine the advantages of three strategies, including directed evolution, rational design, and machine learning, and demonstrate the application of the protocol in the protein engineering of transaminases with higher activity toward bulky substrates. A high-quality data set was obtained by rational design of selected key positions, which was then applied to create a machine learning model for transaminase activity. This model was applied for the data-assisted design of optimized variants, which showed improved activity (up to 3-fold over parent) for three bulky substrates, maintaining enantioselectivity of the starting enzyme scaffold as well as improving the enantiomeric excess (up to >99%ee).