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Computational screening of inhibitors for HIV-1 integrase using a receptor based pharmacophore model.

ABSTRACT: The HIV (human immuno-deficiency virus) integrase has a crucial role in viral replication. Moreover, it has no cellular homologue in humans. Hence, it is considered as an attractive drug target. Many inhibitors against the integrase protein has been designed and discussed. The Y-3 inhibitor (4-acetyl amino-5-hydroxy naphthalene - 2, 7- disulfonic acid) is already known to inhibit HIV-1 integrase. However, it is not suitable as a drug like candidate due to its high cyto-toxicity. In this report, a pharmacophore model for HIV integrase is described using the already known Y-3 inhibitor binding site. Fourteen compounds chemically related to the Y-3 inhibitor were generated using the described pharmacophore model and reported. Subsequent computational analysis showed that these compounds have interactions with the Y3 binding site and their possible utility as an integrase inhibitor is discussed.

SUBMITTER: Jaganatharaja J

PROVIDER: S-EPMC1891665 | biostudies-literature | 2006 Mar

REPOSITORIES: biostudies-literature

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Computational screening of inhibitors for HIV-1 integrase using a receptor based pharmacophore model.

Jaganatharaja Janani J Gowthaman Ragul R

Bioinformation 20060326 4

The HIV (human immuno-deficiency virus) integrase has a crucial role in viral replication. Moreover, it has no cellular homologue in humans. Hence, it is considered as an attractive drug target. Many inhibitors against the integrase protein has been designed and discussed. The Y-3 inhibitor (4-acetyl amino-5-hydroxy naphthalene - 2, 7- disulfonic acid) is already known to inhibit HIV-1 integrase. However, it is not suitable as a drug like candidate due to its high cyto-toxicity. In this report, ...[more]

PMID: 17597868

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Project description:AIM:To discover novel ligands of estrogen receptor (ER) ? using pharmacophore mapping and structure-based screening. METHODS:A computer-aided strategy combining pharmacophore mapping and structure-based screening was used to screen the Maybridge and Enamine databases. Yeast two-hybrid (Y2H) assay was used to detect the activity and selectivity of the chosen compounds. The transcriptional activities of the chosen compounds were demonstrated with luciferase reporter assays. The anti-proliferative effects of ER antagonists against MCF-7 and MDA-MB-231 breast cancer cells were examined using MTT assay, and the mechanisms of action were analyzed with flow cytometry analysis and Western blotting. RESULTS:Through in silico screen, 95 compounds were chosen for testing in Y2H assay, which led to 20 potent ligands, including 10 agonists, 8 antagonists and 2 partial agonists with EC50 or IC50 values at ?mol/L. Furthermore, 6 agonists exhibited absolute selectivity for ER?, and 3 agonists showed higher selectivity for ER?. The agonists 1g and 1h (10, 25, and 50 ?mol/L) dose-dependently increased ER transcriptional activities, whereas the antagonists 2a and 2d (10, 25, and 50 ?mol/L) caused dose-dependent inhibition on the activities. The antagonists and partial agonists at 100 ?mol/L suppressed the proliferation of ER? positive MCF-7 cells and ER? positive MDA-MB-231 cells, but were more effective against MDA-MB-231 cells. Treatment of MDA-MB-231 cells with antagonists 2a and 2d (25 and 50 ?mol/L) dose-dependently increased the population of cells in the S phase. Both 2a and 2d treatment dose-dependently decreased the expression levels of cyclin A and CDK2. Meanwhile, the downregulation of cyclin E was only caused by 2d, while 2a treatment did not cause significant changes in the protein levels of cyclin E. CONCLUSION:The selective ligands discovered in this study are promising drug candidates to be used as molecular probes to explore the differences between ER? and ER?.

Dataset Information

Computational screening of inhibitors for HIV-1 integrase using a receptor based pharmacophore model.

Publications

Computational screening of inhibitors for HIV-1 integrase using a receptor based pharmacophore model.

Similar Datasets

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