Docking and 3D-QSAR studies of diverse classes of human aromatase (CYP19) inhibitors

Abstract

Aromatase (cytochrome 19) inhibitors have emerged as promising candidates for treatment of breast cancer. In search of potent aromatase inhibitors, docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using molecular shape, spatial, electronic, structural and thermodynamic descriptors have been performed on a diverse set of compounds having human aromatase inhibitory activities. An attempt has also been made to include two-dimensional (2D) descriptors in the QSAR studies. The chemometric tools used for model development are genetic function approximation (GFA) and genetic partial least squares (G/PLS). The docking study shows that the important interacting amino acids in the active site cavity are Met374, Arg115, Ile133, Ala306, Thr310, Asp309, Val370 and Ser478. One or more hydrogen bond formation with Met374 is one of the essential requirements for the ligands for optimum aromatase inhibition. The binding is further stabilized by van der Waals interactions with a few non-polar amino acid residues in the active site. The developed QSAR models indicate the importance of different shape, Jurs parameters, structural parameters, topological branching index and E-state index for different fragments. The results obtained from the QSAR analysis are supported by our docking observations. There should be one or two hydrogen bond acceptor groups (like -NO2, -CN) and optimal hydrophobicity for ideal aromatase inhibitors. A GFA model with spline option obtained using 3D descriptors was found to be the best model based on internal validation (Q2=0.668) while the best (externally) predictive model was a GFA model with spline option using combined set (2D and 3D) descriptors (Rpred2=0.687). Based on rm2(overall) criterion, the best model was a G/PLS model (using 3D descriptors) with spline option (rm2(overall)=0.606).