Discovery of geranylgeranyltransferase-I inhibitors with novel scaffolds by the means of quantitative structure-activity relationship modeling, virtual screening, and experimental validation.

Journal Article (Journal Article)

Geranylgeranylation is critical to the function of several proteins including Rho, Rap1, Rac, Cdc42, and G-protein gamma subunits. Geranylgeranyltransferase type I (GGTase-I) inhibitors (GGTIs) have therapeutic potential to treat inflammation, multiple sclerosis, atherosclerosis, and many other diseases. Following our standard workflow, we have developed and rigorously validated quantitative structure-activity relationship (QSAR) models for 48 GGTIs using variable selection k nearest neighbor (kNN), automated lazy learning (ALL), and partial least squares (PLS) methods. The QSAR models were employed for virtual screening of 9.5 million commercially available chemicals, yielding 47 diverse computational hits. Seven of these compounds with novel scaffolds and high predicted GGTase-I inhibitory activities were tested in vitro, and all were found to be bona fide and selective micromolar inhibitors. Notably, these novel hits could not be identified using traditional similarity search. These data demonstrate that rigorously developed QSAR models can serve as reliable virtual screening tools, leading to the discovery of structurally novel bioactive compounds.

Full Text

Duke Authors

Cited Authors

  • Peterson, YK; Wang, XS; Casey, PJ; Tropsha, A

Published Date

  • July 23, 2009

Published In

Volume / Issue

  • 52 / 14

Start / End Page

  • 4210 - 4220

PubMed ID

  • 19537691

Pubmed Central ID

  • PMC2726652

Electronic International Standard Serial Number (EISSN)

  • 1520-4804

Digital Object Identifier (DOI)

  • 10.1021/jm8013772


  • eng

Conference Location

  • United States