Eigenvalue Analysis of Peroxisome Proliferator-Activated Receptor γ Agonists

Important pharmacophoric features of pan PPAR agonists: common chemical feature analysis and virtual screening

HipHop program was used to generate a common chemical feature hypothesis for pan Peroxisome Proliferator-Activated Receptor (PPAR) agonists. The top scoring hypothesis (hypo-1) was found to differentiate the pan agonists (actives) from subtype-specific and dual PPAR agonists (inactives). The importance of individual features in hypo-1 was assessed by deleting a particular feature to generate a new hypothesis and observing its discriminating ability between 'actives' and 'inactives'. Deletion of aromatic features AR-1 (hypo-1b), AR-2 (hypo-1e) and a Hydrophobic feature HYD-1 (hypo-1c) individually did not affect the discriminating power of the hypo-1 significantly. However, deletion of a Hydrogen Bond Acceptor (HBA) feature (hypo-1f) in the hydrophobic tail group was found to be highly detrimental for the specificity of hypo-1 leading to high hit rate of 'inactives'. Since hypo-1 did not produce any useful hits from the database search, hypo-1b, hypo-1c and hypo-1e were used for virtual screening leading to the identification of new potential pan PPAR ligands. The docking studies were used to predict the binding pose of the proposed molecules in PPARgamma active site.

2D QSAR of PPARγ agonist binding and transactivation

Multilinear QSAR models are developed for the largest and most diverse set of PPARgamma agonists treated hitherto. Binding of these small molecules to the human nuclear receptor PPARgamma is described by models that are built on simple 2D molecular descriptors and nevertheless are of good quality and predictive power (e.g., 144 compounds, 10 descriptors, r2=0.79, r2(cv)=0.76). The models presented are thoroughly validated by crossvalidation, randomization experiments, bootstrapping, and training set/test set partitioning. They may therefore be helpful in the design of new antidiabetic drug candidates. For gene transactivation, the functional activity of the agonists, a corresponding model for a similarly diverse compound set is of somewhat lower statistical quality.