DeepMBEnzy: An AI-Driven Database of Mycotoxin Biotransformation Enzymes.

Journal: Journal of agricultural and food chemistry
Published Date: May 28, 2025

Abstract

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.

Authors

  • Pengli Cai
    Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
  • Dongliang Liu
  • Huadong Xing
    CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
  • Dachuan Zhang
    School of Information Science and Technology, Fudan University, Shanghai, China.
  • Yingying Le
    Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
  • AiBo Wu
    Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
  • Qian-Nan Hu
    CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200333, P.R. China.

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