AI Article Synopsis

  • New anti-tubercular agents are needed due to drug resistance, focusing on targets like ATP synthase.
  • A novel approach improved the correlation between amino acid interactions in target proteins and drug activity, effectively predicting the inhibitory activity of specific compounds.
  • The developed models showed strong predictive capabilities and could aid in discovering and optimizing new drugs for tuberculosis.

Article Abstract

Development of new anti-tubercular agents is required in the wake of resistance to the existing and newly approved drugs through novel-validated targets like ATP synthase, etc. The major limitation of poor correlation between docking scores and biological activity by SBDD was overcome by a novel approach of quantitatively correlating the interactions of different amino acid residues present in the target protein structure with the activity. This approach well predicted the ATP synthase inhibitory activity of imidazo[1,2-a] pyridine ethers and squaramides ( = 0.84) in terms of Glu65b interactions. Hence, the models were developed on combined ( = 0.78), and training ( = 0.82) sets of 52, and 27 molecules, respectively. The training set model well predicted the diverse dataset ( = 0.84), test set ( = 0.755), and, external dataset ( = 0.76). This model predicted three compounds from a focused library generated by incorporating the essential features of the ATP synthase inhibition with the pIC values in the range of 0.0508-0.1494 µM. Molecular dynamics simulation studies ascertain the stability of the protein structure and the docked poses of the ligands. The developed model(s) may be useful in the identification and optimization of novel compounds against TB.

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Source
http://dx.doi.org/10.1080/1062936X.2023.2225872DOI Listing

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