DeepMBEnzy: An AI-Driven Database of Mycotoxin Biotransformation Enzymes

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.