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Predicting Ligand Binding to Nuclear Receptors Using a Pipeline Combining Docking and Pharmacophore Models

Abstract : Endocrine disrupting chemicals (EDCs) are compounds able to penetrate the body and to interfere with the functions of the endocrine system. EDCs are considered as a public health threat since human exposure to these compounds have been associated with increased risk of several diseases. It has been shown that EDCs can act through direct binding to nuclear receptors (NR) which leads to either inhibition or overactivation of the hormonal activity. Early detection of potential EDCs becomes an imperative as it is a guarantee of safety for several fields including pharmaceutical, food industry and agriculture. Several health and environmental authorities have been investigating suspicious compounds through experimental testing. However, this remains a challenging task due to the considerable number of compounds to be evaluated. In silico methods can be used in complement to prioritize compounds for experimental testing. In this work, we propose a pipeline combining structure-based (SB) and ligand-based (LB) models to predict potential EDCs based on their ability to bind six nuclear receptors: AR, ERα, ERβ, GR, PPARγ and TRα. The pipeline output enables to categorize query compounds into “high”, “intermediate”, “medium” and “low” risk of being NRs binding compounds and thus, accordingly to the direct mechanism, potential EDCs. To build the pipeline, data was collected from the EPA Comptox dashboard gathering structurally diverse compounds experimentally tested in multiple endpoints against several protein receptors. The dashboard was filtered to only keep the compounds tested in binding assays against each studied NR leading to six individual datasets. Each one of these datasets was then employed to build docking, SB and LB pharmacophores models. Each model was optimized, and their combinations have been assessed to select the protocol associated with the best performances for each receptor. The best performances among the six studied NRs were obtained with the ERβ data set for which the combination of docking and pharmacophore models reached high sensitivity, specificity and accuracy values (0.8, 0.6 and 0.65 respectively)
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https://hal-cnam.archives-ouvertes.fr/hal-03778832
Contributor : Marie-Liesse Bertram Connect in order to contact the contributor
Submitted on : Friday, September 16, 2022 - 11:07:35 AM
Last modification on : Sunday, September 18, 2022 - 3:44:11 AM

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  • HAL Id : hal-03778832, version 1

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Asma Sellami, Matthieu Montes, Nathalie Lagarde. Predicting Ligand Binding to Nuclear Receptors Using a Pipeline Combining Docking and Pharmacophore Models. 8th Chemoinformatics Strasbourg Summer School, Jun 2022, Strasbourg, France. ⟨hal-03778832⟩

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