Redefining Antibacterial Strategies with computational Screening of Benzimidazole Ligands Against VanZ Protein for Alternatives of Antibiotic.

29 January 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

VanZ has crucial involvement in the modification of the bacterial peptidoglycan precursor and blocking its competence to bind with vancomycin and other related antibiotics. Hence targeting this protein can be an excellent option for combating the antibacterial resistance, particularly in the context of glycopeptide antibiotics like vancomycin. Hence, in this study, we have focused on the screening of 323 benzimidazole-based ligands for their possible interaction with the binding pocket of VanZ. The screening was based on the binding affinity values derived from molecular docking analysis. Furthermore, we had conducted an interacting amino acid analysis and we found six ligands that demand additional investigation. Consequently, we conducted molecular dynamics (MD) simulations using the optimal pose of VanZ to validate the stability of these VanZ–ligand complex and strengthen the consistency of the molecular docking results. Additionally, the pharmacological parameter was checked for all the six compounds. In summary, using the computational studies, we have successfully identified the putative candidates, which can be used for further in-vivo analyses. Our comprehensive approach can serve as a basis for the development of targeted compounds with enhanced efficacy against VanZ.

Keywords

Antibacterial Resistance
Glycopeptide Antibiotics
Drug screening
Ligands
Molecular Docking
MD simulations.

Supplementary materials

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Figure S1: Predicted four models out of five model. A-D model 2 to model 5, Table S1: Z score of the ten templates used for the VanZ structure prediction, Table S2: C-Score of the top 5 structure predicted, Figure S2: Confidence Score for the predicted protein structure, Figure S3: The predicted normalized B-factor value, Figure S4: Predicted Solvent accessibility, Table S3: Binding pockets predicted by CastP, Table S4: Potential optimal sites for ligand binding, Figure S5: Drug likeness for the 3GK, Figure S6: Drug likeness for the 0U0, Figure S7: Drug likeness for the F32, Figure S8: Drug likeness for the 0JB, Figure S9: Drug likeness for the FVV, Figure S10: Drug likeness for the K11.
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