Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Peterson YK

PROVIDER: S-EPMC2726652 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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

Peterson Yuri K YK Wang Xiang S XS Casey Patrick J PJ Tropsha Alexander A

Journal of medicinal chemistry 20090701 14

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 (k ...[more]

PMID: 19537691

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Project description:Objective:The nucleoside transporter family is an emerging target for cancer, viral and cardiovascular diseases. Due to the difficulty in the expression, isolation and crystallization of membrane proteins, there is a lack of structural information on any of the mammalian and for that matter the human proteins. Thus the objective of this study was to build homology models for the three cloned concentrative nucleoside transporters hCNT1, hCNT2 and hCNT3 and validate them for screening towards the discovery of much needed inhibitors and probes. Methods:The recently reported crystal structure of the Vibrio cholerae concentrative nucleoside transporter (vcCNT), has satisfactory similarity to the human CNT orthologues and was thus used as a template to build homology models of all three hCNTs. The Schrödinger modeling suite was used for the exercise. External validation of the homology models was carried out by docking a set of recently reported known hCNT1 nucleoside class inhibitors at the putative binding site using induced fit docking (IDF) methodology with the Glide docking program. Then, the hCNT1 homology model was subsequently used to conduct a virtual screening of a 360,000 compound library, and 172 compounds were obtained and biologically evaluated for hCNT 1, 2 and 3 inhibitory potency and selectivity. Results:Good quality homology models were obtained for all three hCNTs as indicated by interrogation for various structural parameters and also external validated by docking of known inhibitors. The IDF docking results showed good correlations between IDF scores and inhibitory activities; particularly for hCNT1. From the top 0.1% of compounds ranked by virtual screening with the hCNT1 homology model, 172 compounds selected and tested for against hCNT1, hCNT2 and hCNT3, yielded 14 new inhibitors (hits) of (i.e., 8% success rate). The most active compound exhibited an IC50 of 9.05 ?M, which shows a greater than 25-fold higher potency than phlorizin the standard CNT inhibitor (IC50 of 250 ?M). Conclusion:We successfully undertook homology modeling and validation for all human concentrative nucleoside transporters (hCNT 1, 2 and 3). The proof-of-concept that these models are promising for virtual screening to identify potent and selective inhibitors was also obtained using the hCNT1 model. Thus we identified a novel potent hCNT1 inhibitor that is more potent and more selective than the standard inhibitor phlorizin. The other hCNT1 hits also mostly exhibited selectivity. These homology models should be useful for virtual screening to identify novel hCNT inhibitors, as well as for optimization of hCNT inhibitors.

Dataset Information

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

Publications

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

Similar Datasets

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