Discovery of Potent Natural-Product-Derived SIRT2 Inhibitors Using Structure- Based Exploration of SIRT2 Pharmacophoric Space Coupled With QSAR Analyses

(E-pub Ahead of Print)

Author(s): Mohammad A. Khanfar*, Saja Alqtaishat

Journal Name: Anti-Cancer Agents in Medicinal Chemistry
(Formerly Current Medicinal Chemistry - Anti-Cancer Agents)


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Abstract:

Background: SIRT2 belongs to a class III of histone deacetylase (HDAC) and has crucial roles in neurodegeneration and malignancy.

Objective: Discover structurally novel natural-product-derived SIRT2 inhibitors.

Methods: Structure-based pharmacophore modeling integrated with validated QSAR analysis were implemented to discover structurally novel SIRT2 inhibitors from natural products database. The targeted QSAR model combined molecular descriptors with structure-based pharmacophore capable of explaining bioactivity variation of structurally diverse SIRT2 inhibitors. Manually built pharmacophore model, validated with receiver operating characteristic curve, and selected using the statistically optimum QSAR equation, was applied as a 3D-search query to mine AnalytiCon Discovery database of natural products.

Results: Experimental in vitro testing of highest-ranked hits identified asperphenamate and salvianolic acid B as active SIRT2 inhibitors with IC50 values in low micromolar range.

Conclusion: New chemical scaffolds of SIRT2 inhibitors have been identified that could serve as a starting point for lead-structure optimization.

Keywords: SIRT2, cancer, neurodegenerative diseases, pharmacophore, QSAR, virtual screening, natural products

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Article Details

Published on: 12 January, 2021
(E-pub Ahead of Print)
DOI: 10.2174/1871520621666210112121523
Price: $95

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