Computer-Based Identification of Potential Druggable Targets in Multidrug-Resistant : A Combined In Silico, In Vitro and In Vivo Study.

The remarkable rise in antimicrobial resistance is alarming for , which necessitates effective strategies for the discovery of promising anti-acinetobacter agents. We used a subtractive proteomics approach to identify unique protein drug targets. Shortlisted targets passed through subtractive channels, including essentiality, non-homology to the human proteome, druggability, sub-cellular localization prediction and conservation. Sixty-eight drug targets were shortlisted; among these, glutamine synthetase, dihydrodipicolinate reductase, UDP-N-acetylglucosamine acyltransferase, aspartate 1-decarboxylase and bifunctional UDP-N-acetylglucosamine diphosphorylase/glucosamine-1-phosphate N-acetyltransferase were evaluated in vitro by determining the minimum inhibitory concentration (MIC) of candidate ligands, citric acid, dipicolinic acid, D-tartaric acid, malonic acid and 2-(N-morpholino)ethanesulfonic acid (MES), respectively, which ranged from 325 to 1500 μg/mL except for MES (25 mg/mL). The candidate ligands, citric acid, D-tartaric acid and malonic acid, showed good binding energy scores to their targets upon applying molecular docking, in addition to a significant reduction in microbial load in the wound infection mouse model. These ligands also exhibited good tolerability to human skin fibroblast. The significant increase in the MIC of malonic acid in β-alanine and pantothenate-supplemented media confirmed its selective inhibition to aspartate 1-decarboxylase. In conclusion, three out of sixty-eight potential drug targets were effectively inhibited in vitro and in vivo by promising ligands.