1. Academic Validation
  2. Identification of DprE1 inhibitors for tuberculosis through integrated in-silico approaches

Identification of DprE1 inhibitors for tuberculosis through integrated in-silico approaches

  • Sci Rep. 2024 May 17;14(1):11315. doi: 10.1038/s41598-024-61901-x.
Swagatika Dash 1 Ekta Rathi 1 Avinash Kumar 2 Kiran Chawla 3 Suvarna G Kini 4 5
Affiliations

Affiliations

  • 1 Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India, 576104.
  • 2 Department of Medical Affairs, Curie Sciences Private Limited, Samastipur, Bihar, India, 848125.
  • 3 Department of Microbiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India, 576104.
  • 4 Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India, 576104. suvarna.gk@manipal.edu.
  • 5 Manipal Mc Gill Centre for Infectious Diseases, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India, 576104. suvarna.gk@manipal.edu.
Abstract

Decaprenylphosphoryl-β-D-ribose-2'-epimerase (DprE1), a crucial Enzyme in the process of arabinogalactan and lipoarabinomannan biosynthesis, has become the target of choice for anti-TB drug discovery in the recent past. The current study aims to find the potential DprE1 inhibitors through in-silico approaches. Here, we built the pharmacophore and 3D-QSAR model using the reported 40 azaindole derivatives of DprE1 inhibitors. The best pharmacophore hypothesis (ADRRR_1) was employed for the virtual screening of the chEMBL database. To identify prospective hits, molecules with good phase scores (> 2.000) were further evaluated by molecular docking studies for their ability to bind to the DprE1 Enzyme (PDB: 4KW5). Based on their binding affinities (< - 9.0 kcal/mole), the best hits were subjected to the calculation of free-binding energies (Prime/MM-GBSA), pharmacokinetic, and druglikeness evaluations. The top 10 hits retrieved from these results were selected to predict their inhibitory activities via the developed 3D-QSAR model with a regression coefficient (R2) value of 0.9608 and predictive coefficient (Q2) value of 0.7313. The induced fit docking (IFD) studies and in-silico prediction of anti-TB sensitivity for these top 10 hits were also implemented. Molecular dynamics simulations (MDS) were performed for the top 5 hit molecules for 200 ns to check the stability of the hits with DprE1. Based on their conformational stability throughout the 200 ns simulation, hit 2 (chEMBL_SDF:357100) was identified as the best hit against DprE1 with an accepted safety profile. The MD results were also in accordance with the docking score, MM-GBSA value, and 3D-QSAR predicted activity. The hit 2 molecule, (N-(3-((2-(((1r,4r)-4-(dimethylamino)cyclohexyl)amino)-9-isopropyl-9H-purin-6-yl)amino)phenyl)acrylamide) could serve as a lead for the discovery of a novel DprE1 inhibiting anti-TB drug.

Keywords

Mycobacterium tuberculosis; 3D-QSAR; DprE1; Molecular dynamics; Pharmacophore.

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