1 research outputs found
Computationally Guided Identification of Novel <i>Mycobacterium tuberculosis</i> GlmU Inhibitory Leads, Their Optimization, and in Vitro Validation
<i>Mycobacterium tuberculosis</i> (Mtb) infections are
causing serious health concerns worldwide. Antituberculosis drug resistance
threatens the current therapies and causes further need to develop
effective antituberculosis therapy. GlmU represents an interesting
target for developing novel Mtb drug candidates. It is a bifunctional
acetyltransferase/uridyltransferase enzyme that catalyzes the biosynthesis
of UDP-<i>N</i>-acetyl-glucosamine (UDP-GlcNAc) from glucosamine-1-phosphate
(GlcN-1-P). UDP-GlcNAc is a substrate for the biosynthesis of lipopolysaccharide
and peptidoglycan that are constituents of the bacterial cell wall.
In the current study, structure and ligand based computational models
were developed and rationally applied to screen a drug-like compound
repository of 20 000 compounds procured from ChemBridge DIVERSet
database for the identification of probable inhibitors of Mtb GlmU.
The in vitro evaluation of the in silico identified inhibitor candidates
resulted in the identification of 15 inhibitory leads of this target.
Literature search of these leads through SciFinder and their similarity
analysis with the PubChem training data set (AID 1376) revealed the
structural novelty of these hits with respect to Mtb GlmU. IC<sub>50</sub> of the most potent identified inhibitory lead (5810599)
was found to be 9.018 ± 0.04 μM. Molecular dynamics (MD)
simulation of this inhibitory lead (5810599) in complex with protein
affirms the stability of the lead within the binding pocket and also
emphasizes on the key interactive residues for further designing.
Binding site analysis of the acetyltransferase pocket with respect
to the identified structural moieties provides a thorough analysis
for carrying out the lead optimization studies