Development and application of in silico models to design new antibacterial 5-amino-4-cyano-1,3-oxazoles against colistin-resistant E. coli strains

Abstract

Here we describe the results of QSAR analysis based on artificial neural networks, synthesis, activity evaluation and molecular docking of a number of 1,3-oxazole derivatives as anti-E. coli antibacterials. All developed QSAR models showed excellent statistics on training (with determination coefficient q2 as 0.76 ± 0.01) and test samples (with q2 as 0.78 ± 0.01). The models were successfully used to identify nine novel 5-amino-4-cyano-1,3-oxazoles with potential anti-E. coli activity. All nine 1,3-oxazoles with predicted high antibacterial potential showed different levels of anti- E. coli in vitro activity. 5-amino-4-cyano-1,3-oxazoles 1 and 3 showed the highest antibacterial activity on average from 17 to 27 mm against MDR, hemolytic MDR and ATCC 25922 E. coli colistin-resistant strains, respectively. The comparative docking analysis demonstrated a possible mechanism of the antibacterial action of the studied 1, 3-oxazoles 1 and 3 through inhibition of E. coli enoyl-ACP reductase (ENR) involved in the biosynthesis of bacterial fatty acids. The localization type is shown of 5-amino-4-cyano-1,3-oxazoles 1 and 3 into the E. coli ENR active site with estimated binding energy from − 10.1 to − 9.5 kcal/mol and hydrogen bonds formation with key amino acids similar to Triclosan. These facts confirm the validity of the hypothesis put forward about the potential antibacterial mechanism of 5-amino-4- cyano-1,3-oxazoles.

Keywords

• Machine learning modeling
• QSAR
• Molecular docking
• 5-amino-4-cyano-1,3-oxazoles
• E. coli
• Colistin resistance