Discovery of New Inhibitors of Mycobacterium tuberculosis InhA Enzyme Using Virtual Screening and a 3D-Pharmacophore-Based Approach

نویسندگان

  • Ivani Pauli
  • Ricardo N. dos Santos
  • Diana C. Rostirolla
  • Leonardo K. Martinelli
  • Rodrigo G. Ducati
  • Luís Fernando S. M. Timmers
  • Luiz Augusto Basso
  • Diógenes Santiago Santos
  • Rafael V. C. Guido
  • Adriano D. Andricopulo
  • Osmar Norberto de Souza
چکیده

Mycobacterium tuberculosis InhA (MtInhA) is an attractive enzyme to drug discovery efforts due to its validation as an effective biological target for tuberculosis therapy. In this work, two different virtual-ligand-screening approaches were applied in order to identify new InhA inhibitors' candidates from a library of ligands selected from the ZINC database. First, a 3-D pharmacophore model was built based on 36 available MtInhA crystal structures. By combining structure-based and ligand-based information, four pharmacophoric points were designed to select molecules able to satisfy the binding features of MtInhA substrate-binding cavity. The second approach consisted of using four well established docking programs, with different search algorithms, to compare the binding mode and score of the selected molecules from the aforementioned library. After detailed analyses of the results, six ligands were selected for in vitro analysis. Three of these molecules presented a satisfactory inhibitory activity with IC50 values ranging from 24 (±2) μM to 83 (±5) μM. The best compound presented an uncompetitive inhibition mode to NADH and 2-trans-dodecenoyl-CoA substrates, with Ki values of 24 (±3) μM and 20 (±2) μM, respectively. These molecules were not yet described as antituberculars or as InhA inhibitors, making its novelty interesting to start efforts on ligand optimization in order to identify new effective drugs against tuberculosis having InhA as a target. More studies are underway to dissect the discovered uncompetitive inhibitor interactions with MtInhA.

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عنوان ژورنال:
  • Journal of chemical information and modeling

دوره 53 9  شماره 

صفحات  -

تاریخ انتشار 2013