Title:Discovery of Potent Natural-Product-Derived SIRT2 Inhibitors Using Structure-Based Exploration of SIRT2 Pharmacophoric Space Coupled With QSAR Analyses
VOLUME: 21
Author(s):Mohammad A. Khanfar* and Saja Alqtaishat
Affiliation:College of Pharmacy, Alfaisal University, Al Takhassusi Rd, Riyadh 11533, Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, P.O Box 13140, Amman 11942
Keywords:SIRT2, cancer, neurodegenerative diseases, pharmacophore, QSAR, virtual screening, natural products
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.
