Estudos de modelagem molecular e simulação computacional das atividades antimicrobianas de 1,3,4 oxadiazóis contra xanthomonas sp. patógenas em citrus e arroz

Abstract

Xanthomonas is a genus of Gram-negative bacteria responsible for important plant diseases, affecting a wide range of agricultural crops. These phytopathogens cause, among other symptoms, leaf spots and impairment of plant development. Quantitative Structure–Activity Relationship (QSAR) is a computational chemistry approach aimed at understanding and predicting the biological properties of molecules based on their structural characteristics. In the present study, the MIA-QSAR methodology was employed to investigate the relationship between the structure of chemical compounds and their antibacterial activity against Xanthomonas axonopodis pv. citri (Xac), Xanthomonas oryzae pv. oryzae (Xoo), and Xanthomonas oryzae pv. oryzicola (Xoc). The developed models were used to design new antimicrobial agents targeting infections caused by these bacteria.For the set of molecules with anti-Xac activity, statistical models were constructed using the Partial Least Squares (PLS) method and validated both internally and externally. Molecular docking studies were also performed to elucidate possible pathogenicity mechanisms of Xac, a key step for the effective control of citrus canker. The anti-Xac model exhibited robust performance metrics (r² = 0.88; q² = 0.82; r²pred = 0.91), and MIA-plots highlighted the promising potential of certain fluorinated derivatives. Given the lack of previously reported biological targets for these compounds, molecular docking studies were conducted on three enzymes—enoyl-acyl carrier protein reductase (FabV), superoxide dismutase (SOD), and glutathione S-transferase (GST)—in order to propose a possible inhibition mechanism for 1,3,4-oxadiazoles. For molecules active against Xoc and Xoo, statistical models were developed using both PLS and Support Vector Machine (SVM) approaches, which were also internally and externally validated. Molecular docking and molecular dynamics studies were carried out to better understand the interactions between the compounds and their molecular targets. Although the PLS model showed moderate predictive ability, SVM regression demonstrated greater robustness and reliability in estimating the antibacterial potential of 1,3,4-oxadiazoles. The newly designed candidates exhibited predicted activities superior to those of the original dataset, particularly compounds containing a 2,4- dichlorophenyl substituent, which showed significant and dual antibacterial activity against Xoc and Xoo. Docking and molecular dynamics results corroborated the QSAR findings, highlighting the key structural features responsible for ligand–enzyme binding affinities. Keywords: MIA-QSAR; antibacterials; Xanthomonas; molecular docking; molecular dynamics.