AI Article Synopsis

  • Streptococcus pyogenes is a primary cause of strep throat and has an essential enzyme called FabG, which is crucial for cell membrane synthesis and other virulence factors.
  • A homology model of FabG was created using a similar protein structure from Aquifex aeolicus, and this model was refined to predict active sites for drug design.
  • Thirteen promising compounds were identified through docking studies and screened for their ADMET properties, with subsequent in vitro testing confirming their potential as effective inhibitors against S. pyogenes.

Article Abstract

Streptococcus pyogenes (SP) is the major cause of pharyngitis accompanied by strep throat infections in humans. 3-keto acyl reductase (FabG), an important enzyme involved in the elongation cycle of the fatty acid pathway of S. pyogenes, is essential for synthesis of the cell-membrane, virulence factors and quorum sensing-related mechanisms. Targeting SPFabG may provide an important aid for the development of drugs against S. pyogenes. However, the absence of a crystal structure for FabG of S. pyogenes limits the development of structure-based drug designs. Hence, in the present study, a homology model of FabG was generated using the X-ray crystallographic structure of Aquifex aeolicus (PDB ID: 2PNF). The modeled structure was refined using energy minimization. Furthermore, active sites were predicted, and a large dataset of compounds was screened against SPFabG. The ligands were docked using the LigandFit module that is available from Discovery Studio version 2.5. From this list, 13 best hit ligands were chosen based on the docking score and binding energy. All of the 13 ligands were screened for Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties. From this, the two best descriptors, along with one descriptor that lay outside the ADMET plot, were selected for molecular dynamic (MD) simulation. In vitro testing of the ligands using biological assays further substantiated the efficacy of the ligands that were screened based on the in silico methods.

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http://dx.doi.org/10.1016/j.jmgm.2013.07.009DOI Listing

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