Discovery of Toxin-Degrading Enzymes with Positive Unlabeled Deep Learning
Discovery of Toxin-Degrading Enzymes with Positive Unlabeled Deep Learning | |
Author | Zhang, DC; Xing, HD; Liu, DL; Han, MY; Cai, PL; Lin, HK; Tian, Y; Guo, YH; Sun, B; Le, YY; Tian, Y; Wu, AB; Hu, QN |
Journal | ACS CATALYSIS |
Pub Year | 2024 |
Type | |
Abstract | 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. |
Issue | 14 |
Volume | 14 |
SCI | 11.3 |