In silico mechanistic profiling to probe small molecule binding to sulfotransferases.

Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their...

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Autores principales:	Virginie Y Martiny, Pablo Carbonell, David Lagorce, Bruno O Villoutreix, Gautier Moroy, Maria A Miteva
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/13024b9923fb480c877f386bda11f813
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spelling	oai:doaj.org-article:13024b9923fb480c877f386bda11f8132021-11-18T08:56:31ZIn silico mechanistic profiling to probe small molecule binding to sulfotransferases.1932-620310.1371/journal.pone.0073587https://doaj.org/article/13024b9923fb480c877f386bda11f8132013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24039991/?tool=EBIhttps://doaj.org/toc/1932-6203Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug-drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands' binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.Virginie Y MartinyPablo CarbonellDavid LagorceBruno O VilloutreixGautier MoroyMaria A MitevaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e73587 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Virginie Y Martiny Pablo Carbonell David Lagorce Bruno O Villoutreix Gautier Moroy Maria A Miteva In silico mechanistic profiling to probe small molecule binding to sulfotransferases.
description	Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug-drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands' binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.
format	article
author	Virginie Y Martiny Pablo Carbonell David Lagorce Bruno O Villoutreix Gautier Moroy Maria A Miteva
author_facet	Virginie Y Martiny Pablo Carbonell David Lagorce Bruno O Villoutreix Gautier Moroy Maria A Miteva
author_sort	Virginie Y Martiny
title	In silico mechanistic profiling to probe small molecule binding to sulfotransferases.
title_short	In silico mechanistic profiling to probe small molecule binding to sulfotransferases.
title_full	In silico mechanistic profiling to probe small molecule binding to sulfotransferases.
title_fullStr	In silico mechanistic profiling to probe small molecule binding to sulfotransferases.
title_full_unstemmed	In silico mechanistic profiling to probe small molecule binding to sulfotransferases.
title_sort	in silico mechanistic profiling to probe small molecule binding to sulfotransferases.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/13024b9923fb480c877f386bda11f813
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In silico mechanistic profiling to probe small molecule binding to sulfotransferases.

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