Discovery of Toxin-Degrading Enzymes with Positive Unlabeled Deep Learning

• 全文链接

利用阳性非标记深度学习发现毒素降解酶

关键词：

来源：

ACS Catalysis

全文链接：

//agri.nais.net.cn/topic/downloadFile/b304b3ec-b89a-4d24-9e9c-e44e1568fd01

来源地址：

https://doi.org/10.1021/acscatal.3c04461

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：

2024-02-16

摘要：

Identifying functional enzymes for the catalysis of specific biochemical reactions is a major bottleneck in the de novo design of biosynthesis and biodegradation pathways. Conventional methods based on microbial screening and functional metagenomics require long verification periods and incur high experimental costs; recent data-driven methods apply only to a few common substrates. To enable rapid and high-throughput identification of enzymes for complex and less-studied substrates, we propose a robust enzyme’s substrate promiscuity prediction model based on positive unlabeled learning. Using this model, we identified 15 new degrading enzymes specific for the mycotoxins ochratoxin A and zearalenone, of which six could degrade >90% mycotoxin content within 3 h. We anticipate that this model will serve as a useful tool for identifying new functional enzymes and understanding the nature of biocatalysis, thereby advancing the fields of synthetic biology, metabolic engineering, and pollutant biodegradation.