Subtractive Proteomics Supported with Rational Drug Design Approach Revealed ZINC23121280 as a Potent Lead Inhibitory Molecule for Multi-drug Resistant Francisella tularensis
Drug Designing for Multidrug-Resistant Francisella tularensis
DOI:
https://doi.org/10.53560/PPASB(58-1)349Keywords:
Francisella tularensis, Subtractive proteomics, Glucose-1-phosphate thymidylyltransferase, Pharmacophore, Molecular docking, Molecular Dynamic simulation, MMPB/GBSAAbstract
Francisella tularensis is a Gram-negative bacterium and is the etiological agent of taluremia. The prolonged use of antibiotics is the reason for pathogen resistance to antibiotics such as beta-lactams and macrolides. This leads to the search to explore novel drug targets for F. tularensis to inhibit its growth. Subtractive proteomics revealed Glucose-1-phosphate thymidylyltransferase (G1PTT) as the most promising protein as a drug target. A pharmacophore model was generated for virtual screening of a druglike library comprised of 1,000,000 drug molecules. Based on a pharmacophore-based search, a set of 152 compounds was predicted as the most potent inhibitors against this enzyme. The screened hits were docked with the target enzyme; which unveiled ZINC23121280 as the best-docked inhibitor having Autdock Vina binding energy of -7.2 kcal/mol and the GOLD score of 64.06. Moreover, the timedependent dynamic behavior of the complex was analyzed using Molecular Dynamics (MD) simulation studies that revealed a stable system with a Root Mean Square Deviation (RMSD) average value of 2.25 Å and Root Mean Square
Fluctuations (RMSF) of 1.16 Å. Radial Distribution Function (RDF) predicted strong hydrogen interactions between the ligand and Trp221 from the enzyme active pocket. The higher affinity of the antagonist for the enzyme was further supported by Molecular Mechanics Energies combined with the Poisson–Boltzmann and Surface Area (MMPBSA) and or Generalized Born and Surface Area (MMGBSA) with the estimated binding free energy of −1.07 kcal/mol and −29.59 kcal/mol, respectively. Findings from this present computational framework may provide the foundation for future drug discovery against F. tularensis.
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