3D-QSAR, ADMET, and molecular docking studies of aztreonam analogs as E. colis inhibitors.

Background: The development of multidrug resistant strains of extended-spectrum β-lactamase-producing Escherichia coli has become a global problem; therefore, the discovery of new antibacterial agents is the only available solution.

Objective: To improve and propose new compounds with antibacterial activity, the three-dimensional quantitative structure-activity relationship and molecular docking studies were carried out on Aztreonam analogs as E. coli inhibitors in DNA gyrase B.

Method: This study's 3D-Quantitative structure-activity relationship model was created using on the Comparative Molecular Field Analysis and the Comparative Molecular Similarity Indices Analysis. Using the Comparative Molecular Field Analysis (Q ² = 0.73; R ² = 0.82), excellent predictability was achieved, and the best Comparative Molecular Similarity Indices Analysis model (Q ² = 0.88; R ² = 0.9). The generated model's ability to predict outcomes was assessed through external validation using a test set compound and an applicability domain technique. In this study, the steric, electrostatic, and hydrogen bond acceptor fields played a key role in antibacterial activity.

Results: The results of the molecular docking revealed that the newly generated compound A6 has the highest binding affinity with DNA gyrase B. It forms 10 hydrogen bonds with amino acid residues of Asn104, Asn274, Asn132, Ser70, Ser237, Thr105, Glu273, and 2 salt bridges with amino acid residues of Ser70 and Glu273 and one pi-pi interacting with Gys271 amino acid residue in the binding site of 5G1, and this result was validated by a new assessment method. We created some novel, highly effective DNA gyrase B inhibitors based on the earlier findings, and the most accurate model predicted their inhibitory actions. The ADMET characteristics and pharmacological similarity of these novel inhibitors were also examined.

Conclusion: These findings would be very beneficial in guiding the optimization process for the identification of novel drugs that can address the issue of multiple drug resistance.