title: Pharmacophore mapping of diverse classes of farnesyltransferase inhibitors.
abstract: Protein farnesyltransferase (FTase) is a zinc-dependent enzyme that catalyzes the attachment of a farnesyl lipid group to the sulfur atom of a cysteine residue of numerous proteins involved in cell signaling including the oncogenic H-Ras protein. Pharmacophore models were developed by using Catalyst HypoGen program with a training set of 22 farnesyltransferase inhibitors (FTIs), which were carefully selected with great diversity in both molecular structure and bioactivity for discovering new potent FTIs. The best pharmacophore hypothesis (Hypo 1), consisting of four features, namely, one hydrogen-bond acceptor (HBA), one hydrophobic point (HY), and two ring aromatics (RA), has a correlation coefficient of 0.961, a root mean square deviation (RMSD) of 0.885, and a cost difference of 62.436, suggesting that a highly predictive pharmacophore model was successfully obtained. For the test series, a classification scheme was used to distinguish highly active from moderately active and inactive compounds on the basis of activity ranges. Hypo 1 was validated with 181 test set compounds, which has a correlation coefficient of 0.713 between estimated activity and experimentally measured activity. The model was further validated by screening a database spiked with 25 known inhibitors. The model picked up all 25 known inhibitors giving an enrichment factor of 10.892. The results demonstrate that the hypothesis derived in this study can be considered to be a useful and reliable tool in identifying structurally diverse compounds with desired biological activity.
authors.affiliation: Department of Pharmaceutical Science and Drug Research, Punjabi University, Patiala 147-002, India.
authors.forename: Tabish
authors.initials: T
authors.lastname: Equbal
authors.forename: Om
authors.initials: O
authors.lastname: Silakari
authors.forename: Gundla
authors.initials: G
authors.lastname: Rambabu
authors.forename: Muttineni
authors.initials: M
authors.lastname: Ravikumar
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002384:Catalysis; D030541:Databases, Genetic; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D051231:Farnesyltranstransferase / Q000037:antagonists & inhibitors*; D008958:Models, Molecular; D013329:Structure-Activity Relationship
chemical_list: D004791:Enzyme Inhibitors; D051231:Farnesyltranstransferase