Abstract
Situin 2 (SIRT2) enzyme is a histone deacetylase that has important role in neuronal development. SIRT2 is clinically validated target for neurodegenerative diseases and some cancers. In this study, exhaustive unsupervised pharmacophore modeling was combined with quantitative structure‐activity relationship (QSAR) analysis to explore the structural requirements for potent SIRT2 inhibitors using 146 known SIRT2 ligands. A computational workflow that combines genetic function algorithm with k‐nearest neighbor or multiple linear regression was implemented to build self‐consistent and predictive QSAR models based on combinations of pharmacophores and physicochemical descriptors. Successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve profiles. Optimal QSAR models and their associated pharmacophore hypotheses were experimentally validated by identification and in vitro evaluation of several new promising SIRT2 inhibitory leads retrieved from the National Cancer Institute structural database. The most potent hit illustrated IC50 value of 5.4μM. The chemical structures of active hits were validated by proton nuclear magnetic resonance and mass spectroscopy.
SIRT2 is a clinically validated target for neurodegenerative diseases. Unsupervised pharmacophore modeling combined with QSAR analysis was applied to model SIRT2 inhibitors. The resulting models were used to identify new low micromolar SIRT2 inhibitors via virtual screening.