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

Authors: TU, YI-SHU - National Science Council; TSENG, YUFENG - National Taiwan University; Appell, Michael

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/23/2018
Publication Date: 8/23/2018
Citation: Tu, Y.-S., Tseng, Y.J., Appell, M. 2018. Quantum molecular dynamics and cheminformatics study of mycotoxin detection and toxicity to improve food safety [Abstract].

Interpretive Summary:

Technical Abstract: 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 were conducted to characterize 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 algorithm (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.