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Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds

The androgen receptor (AR) is a transcription factor that plays a key role in sexual phenotype and neuromuscular development. AR can be modulated by exogenous compounds such as pharmaceuticals or chemicals present in the environment, and particularly by AR agonist compounds that mimic the action of...

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Autores principales: Réau, Manon, Lagarde, Nathalie, Zagury, Jean-François, Montes, Matthieu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6912550/
https://www.ncbi.nlm.nih.gov/pubmed/31766271
http://dx.doi.org/10.3390/cells8111431
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author Réau, Manon
Lagarde, Nathalie
Zagury, Jean-François
Montes, Matthieu
author_facet Réau, Manon
Lagarde, Nathalie
Zagury, Jean-François
Montes, Matthieu
author_sort Réau, Manon
collection PubMed
description The androgen receptor (AR) is a transcription factor that plays a key role in sexual phenotype and neuromuscular development. AR can be modulated by exogenous compounds such as pharmaceuticals or chemicals present in the environment, and particularly by AR agonist compounds that mimic the action of endogenous agonist ligands and whether restore or alter the AR endocrine system functions. The activation of AR must be correctly balanced and identifying potent AR agonist compounds is of high interest to both propose treatments for certain diseases, or to predict the risk related to agonist chemicals exposure. The development of in silico approaches and the publication of structural, affinity and activity data provide a good framework to develop rational AR hits prediction models. Herein, we present a docking and a pharmacophore modeling strategy to help identifying AR agonist compounds. All models were trained on the NR-DBIND that provides high quality binding data on AR and tested on AR-agonist activity assays from the Tox21 initiative. Both methods display high performance on the NR-DBIND set and could serve as starting point for biologists and toxicologists. Yet, the pharmacophore models still need data feeding to be used as large scope undesired effect prediction models.
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spelling pubmed-69125502020-01-02 Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds Réau, Manon Lagarde, Nathalie Zagury, Jean-François Montes, Matthieu Cells Article The androgen receptor (AR) is a transcription factor that plays a key role in sexual phenotype and neuromuscular development. AR can be modulated by exogenous compounds such as pharmaceuticals or chemicals present in the environment, and particularly by AR agonist compounds that mimic the action of endogenous agonist ligands and whether restore or alter the AR endocrine system functions. The activation of AR must be correctly balanced and identifying potent AR agonist compounds is of high interest to both propose treatments for certain diseases, or to predict the risk related to agonist chemicals exposure. The development of in silico approaches and the publication of structural, affinity and activity data provide a good framework to develop rational AR hits prediction models. Herein, we present a docking and a pharmacophore modeling strategy to help identifying AR agonist compounds. All models were trained on the NR-DBIND that provides high quality binding data on AR and tested on AR-agonist activity assays from the Tox21 initiative. Both methods display high performance on the NR-DBIND set and could serve as starting point for biologists and toxicologists. Yet, the pharmacophore models still need data feeding to be used as large scope undesired effect prediction models. MDPI 2019-11-13 /pmc/articles/PMC6912550/ /pubmed/31766271 http://dx.doi.org/10.3390/cells8111431 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Réau, Manon
Lagarde, Nathalie
Zagury, Jean-François
Montes, Matthieu
Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds
title Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds
title_full Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds
title_fullStr Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds
title_full_unstemmed Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds
title_short Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds
title_sort hits discovery on the androgen receptor: in silico approaches to identify agonist compounds
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6912550/
https://www.ncbi.nlm.nih.gov/pubmed/31766271
http://dx.doi.org/10.3390/cells8111431
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