A data-driven integrative platform for computational prediction of toxin biotransformation with a case study

Area of Science:

• Environmental Chemistry
• Biochemistry
• Computational Biology

Background:

• Biogenic toxins pose significant contamination risks in food, feed, and the environment.
• Existing research lacks integrated platforms connecting computational prediction with experimental validation for toxin biotransformation.

Purpose of the Study:

• To develop a novel, integrated platform for studying biogenic toxin biotransformation.
• To facilitate the discovery of novel toxin metabolites and detoxification enzymes.

Main Methods:

• Construction of ToxinDB, a comprehensive database of over 4836 biogenic toxins.
• Extraction of over 8000 biotransformation reaction rules from a large corpus of biochemical reactions.
• Development of a toxin biotransformation prediction model and exploration of the global chemical space of toxins and their metabolites.

Main Results:

• ToxinDB was established, containing extensive data on biogenic toxins.
• A predictive model for toxin biotransformation was created, revealing ~550,000 toxins and metabolites, with 94.7% of metabolites previously unreported.
• A case study identified a novel citrinin metabolite using the ToxinDB prediction tool.

Conclusions:

• The developed integrative platform effectively links computational and experimental approaches for toxin biotransformation.
• This platform facilitates the exploration of unknown toxin metabolomes and accelerates the discovery of enzymes for detoxification.
• The findings have implications for managing environmental contamination and ensuring food/feed safety.