Pharmacoinformatics analysis to identify inhibitors ofMtb-ASADH

Kumar, Rajender ; Garg, Prabha ; Bharatam, P.V. (2016) Pharmacoinformatics analysis to identify inhibitors ofMtb-ASADH Journal of Biomolecular Structure and Dynamics, 34 (1). pp. 1-14. ISSN 0739-1102

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Official URL: http://doi.org/10.1080/07391102.2015.1005137

Related URL: http://dx.doi.org/10.1080/07391102.2015.1005137

Abstract

Aspartate-semialdehyde dehydrogenase (ASADH; EC 1.2.1.11) is a key enzyme in the biosynthesis of essential amino acids in prokaryotes and fungi, inhibition of ASADH leads to the development of novel antitubercular agents. In the present work, a combined structure and ligand-based pharmacophore modeling, molecular docking, and molecular dynamics (MD) approaches were employed to identify potent inhibitors of mycobacterium tuberculosis (Mtb)-ASADH. The structure-based pharmacophore hypothesis consists of three hydrogen bond acceptor (HBA), two negatively ionizable, and one positively ionizable center, while ligand-based pharmacophore consists of additional one HBA and one hydrogen bond donor features. The validated pharmacophore models were used to screen the chemical databases (ZINC and NCI). The screened hits were subjected to ADME and toxicity filters, and subsequently to the molecular docking analysis. Best-docked 25 compounds carry the characteristics of highly electronegative functional groups (-COOH and -NO2) on both sides and exhibited the H-bonding interactions with highly conserved residues Arg99, Arg249, and His256. For further validation of docking results, MD simulation studies were carried out on two representative compounds NSC51108 and ZINC04203124. Both the compounds remain bound to the key active residues of Mtb-ASADH during the MD simulations. These identified hits can be further used for lead optimization and in the design more potent inhibitors against Mtb-ASADH.

Item Type:Article
Source:Copyright of this article belongs to FOIA Privacy.
Keywords:Mtb-ASADH; Inhibitor Design; Molecular Docking and Molecular Dynamics; Pharmacophore Modeling.
ID Code:116423
Deposited On:12 Apr 2021 09:35
Last Modified:12 Apr 2021 09:35

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