Predictive quantitative structure-activity relationship modeling of the antifungal and antibiotic properties of triazolothiadiazine compounds

Authors: Appell, Michael; Compton, David - Dave; Evans, Kervin

Submitted to: Methods and Protocols
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/24/2020
Publication Date: 12/27/2020
Citation: Appell, M.D., Compton, D.L., Evans, K.O. 2020. Predictive quantitative structure-activity relationship modeling of the antifungal and antibiotic properties of triazolothiadiazine compounds. Methods and Protocols. 4(1). Article 2. https://doi.org/10.3390/mps4010002.
DOI: https://doi.org/10.3390/mps4010002

Interpretive Summary: Harmful fungal and bacterial pathogens sometimes contaminate and spoil food and can negatively impact human and livestock health. Some classes of chemicals reduce food spoilage and disease caused by these contaminants. In this research, we developed scientific models that predict the antifungal and antibiotic activity for a class of chemicals. The models we developed characterize the chemical properties important to reduce fungal and bacterial growth. Using these models, we can quickly and economically predict the antifungal and antimicrobial activities of other compounds and aid in the development of new antimicrobials with better properties.

Technical Abstract: Predictive models were developed using two-dimensional quantitative structure activity relationship (QSAR) methods coupled with B3LYP/6-311+G** density functional theory modeling that describe the antimicrobial properties of twenty-four triazolothiadiazine compounds against Aspergillus niger, Aspergillus flavus and Penicillium sp., as well as the bacteria Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa. B3LYP/6-311+G** density functional theory calculations indicated the triazolothiadiazine derivatives possess only modest variation between the frontier orbital properties. Genetic function approximation (GFA) analysis identified the topological and density functional theory derived descriptors for antimicrobial models using a population of 200 models with one to three descriptors that were crossed for 10,000 generations. Two or three descriptor models provided validated predictive models for antifungal and antibiotic properties with R2 values between 0.725 and 0.768 and no outliers. The best models to describe antimicrobial activities include descriptors related to connectivity, electronegativity, polarizability, and van der Waals properties. The reported method provided robust two-dimensional QSAR models with topological and density functional theory descriptors that explain a variety of antifungal and antibiotic activities for structurally related heterocyclic compounds.