Abstract
The elevated level of dual-specificity tyrosine phosphorylation-regulated kinase 1A (Dyrk1A) is associated with the pathology of neurodegenerative diseases and has been implicated in some neurobiological alterations of Down syndrome, such as mental retardation. In the present article, a pharmacophore based 3D-QSAR model was developed for a series of leucettines possessing Dyrk1A inhibitory activity. The crucial molecular features observed in the developed pharmacophore model that account for binding affinity of ligands with the enzyme, include three H-bond acceptors (A), one positive ionic site (P) and one hydrophobic aromatic ring (R). Excellent statistical results of QSAR model such as good correlation coefficient (r > 0.9), higher F value (F > 20), excellent predictive power (Q2 > 0.6) and higher enrichment of known actives during virtual screening application strongly suggest that the developed model will be highly useful in designing new inhibitors and for predicting activity of new inhibitors.
Keywords: Anti-Alzheimers, Dyrk1A, Leucettines, Pharmacophore model, 3D-QSAR, Partial least square analysis.
Letters in Drug Design & Discovery
Title:Pharmacophore Based 3D-QSAR Modeling and Molecular Docking of Leucettines as Potent Dyrk1A Inhibitors
Volume: 10 Issue: 8
Author(s): Anu Bahl, Prashant Joshi, Sandip B. Bharate and Harish Chopra
Affiliation:
Keywords: Anti-Alzheimers, Dyrk1A, Leucettines, Pharmacophore model, 3D-QSAR, Partial least square analysis.
Abstract: The elevated level of dual-specificity tyrosine phosphorylation-regulated kinase 1A (Dyrk1A) is associated with the pathology of neurodegenerative diseases and has been implicated in some neurobiological alterations of Down syndrome, such as mental retardation. In the present article, a pharmacophore based 3D-QSAR model was developed for a series of leucettines possessing Dyrk1A inhibitory activity. The crucial molecular features observed in the developed pharmacophore model that account for binding affinity of ligands with the enzyme, include three H-bond acceptors (A), one positive ionic site (P) and one hydrophobic aromatic ring (R). Excellent statistical results of QSAR model such as good correlation coefficient (r > 0.9), higher F value (F > 20), excellent predictive power (Q2 > 0.6) and higher enrichment of known actives during virtual screening application strongly suggest that the developed model will be highly useful in designing new inhibitors and for predicting activity of new inhibitors.
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Bahl Anu, Joshi Prashant, Bharate B. Sandip and Chopra Harish, Pharmacophore Based 3D-QSAR Modeling and Molecular Docking of Leucettines as Potent Dyrk1A Inhibitors, Letters in Drug Design & Discovery 2013; 10 (8) . https://dx.doi.org/10.2174/15701808113100890024
DOI https://dx.doi.org/10.2174/15701808113100890024 |
Print ISSN 1570-1808 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-628X |
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