| DeepMBEnzy: An AI-Driven Database of Mycotoxin Biotransformation Enzymes |
| 論文作者 |
Cai, PL; Liu, DL; Xing, HD; Zhang, DC; Le, YY; Wu, AB; Hu, QN |
| 期刊/會(huì)議名稱 |
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY |
| 論文年度 |
2025 |
| 論文類別 |
|
| 摘要 |
Mycotoxins are toxic fungal metabolites that pose significant health risks. Enzyme biotransformation is a promising option for detoxifying mycotoxins and for elucidating their intracellular metabolism. However, few mycotoxin-biotransformation enzymes have been identified thus far. Here, we developed an enzyme promiscuity prediction for mycotoxin biotransformation (EPP-MB) model by fine-tuning a pretrained model using a cold protein data-splitting approach. The EPP-MB model leverages deep learning to predict enzymes capable of mycotoxin biotransformation, achieving a validation accuracy of 79% against a data set of experimentally confirmed mycotoxin-biotransforming enzymes. We applied the model to predict potential biotransformation enzymes for over 4000 mycotoxins and compiled these into the DeepMBEnzy database, which archives the predicted enzymes and related information for each mycotoxin, providing researchers with a user-friendly, publicly accessible interface at https://synbiodesign.com/DeepMBEnzy/. DeepMBEnzy is designed to facilitate the exploration and utilization of enzyme candidates in mycotoxin biotransformation, supporting further advancements in mycotoxin detoxification research and applications. |
| 卷 |
73 |
| 影響因子 |
6.2 |
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