Virtual lead identification of farnesyltransferase inhibitors based on ligand and structure-based pharmacophore techniques

Abstract

Farnesyltransferase enzyme (FTase) is considered an essential enzyme in the Ras signaling pathway associated with cancer. Thus, designing inhibitors for this enzyme might lead to the discovery of compounds with effective anticancer activity. In an attempt to obtain effective FTase inhibitors, pharmacophore hypotheses were generated using structure-based and ligand-based approaches built in Discovery Studio v3.1. Knowing the presence of the zinc feature is essential for inhibitor's binding to the active site of FTase enzyme; further customization was applied to include this feature in the generated pharmacophore hypotheses. These pharmacophore hypotheses were thoroughly validated using various procedures such as ROC analysis and ligand pharmacophore mapping. The validated pharmacophore hypotheses were used to screen 3D databases to identify possible hits. Those which were both high ranked and showed sufficient ability to bind the zinc feature in active site, were further refined by applying drug-like criteria such as Lipiniski's "rule of five" and ADMET filters. Finally, the two candidate compounds (ZINC39323901 and ZINC01034774) were allowed to dock using CDOCKER and GOLD in the active site of FTase enzyme to optimize hit selection.

Figure 1

(a) Training set ligands utilized in common feature pharmacophore generation are shown with their PDB code and inhibitor name. (b) Test set ligands used for common feature pharmacophore validation step.

Figure 2

Molecular properties distribution of the fourteen crystal structures used for common feature pharmacophore. (a) Molecular weight scale. (b) Molecular solubility, Number of aromatic rings, Kappa_1, and SC_1.

Figure 3

Pharm-3A and its overlay with a training set compound (a) Chemical features of pharm-3A with its inter-feature distances. (b) 2ZIS-NH8903 overlaid on pharm-3A hypothesis. HY; hydrophobic, RA; ring aromatic, HBA; hydrogen bond acceptor, ZB; zinc binder.

Figure 4

Structure-based pharmacophore hypothesis, Pharm-B. (a) Arrangement of pharmacophoric features of Pharm-B. HY; hydrophobic, RA; ring aromatic, HBA; hydrogen bond acceptor, ZB; zinc binder. (b) Pharmacophoric features are displayed with inter-feature distances. Tolerance spheres were removed for simplification purposes.

Figure 5

Superimposition of pharm-3A over Pharm-B showing the degree of similarity between the two pharmacophores of RMSD value of 2.52. HY; hydrophobic, RA; ring aromatic, HBA; hydrogen bond acceptor, ZB; zinc binder.

Figure 6

2ZIS-NH8903 overlaid on pharm-B hypothesis. HY; hydrophobic, RA; ring aromatic, HBA; hydrogen bond acceptor, ZB zinc binder.

Figure 7

ROC curve of hypothesis Pharm-3A.

Figure 8

Database screening of three databases employing pharm-3A and pharm-B hypotheses.

Figure 9

Molecular docking result of ZINC01034774 displayed in stick format, zinc atom CPK and active site amino acids presented as line format.

Figure 10

Molecular docking result of ZINC39323901 displayed in stick format, zinc atom CPK and active site amino acids presented as line format.