Occasionally, food and feed may become contaminated with fungi capable of producing mycotoxins that pose health risks and reduce commodity values. Mycotoxin detection is important to prevent exposure and remove contaminated commodities from the food supply. Detection properties related to chemical structure can improve selective detection. Cheminformatic tools offer a means to statistically identify important properties of molecules, including properties related to selective detection and toxicity. B3LYP density functional molecular dynamics was conducted to identify the conformational dynamic behavior of several mycotoxins, including ochratoxins, zearalenone, citrinin, and trichothecenes, as well as their co-occurring biosynthetic precursors and metabolites. Based on these results, quantitative structure-activity relationship (QSAR) models were developed using genetic function algorithms (GFA) and partial least squares regression (PLS) to identify important descriptors related to detection and toxicity. The models serve as economical and convenient tools to explain mycotoxin toxicity and design label-free detection methods.

Quantum molecular dynamics and cheminformatics study of mycotoxin toxicity and detection to improve food safety

256th National Meeting (2018)

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