A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes

Proteomics is often used to map protein-drug interactions but identifying a drug’s protein targets along with the binding interfaces has not been achieved yet. Here, the authors integrate limited proteolysis and machine learning for the proteome-wide mapping of drug protein targets and binding sites...

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Autores principales:	Ilaria Piazza, Nigel Beaton, Roland Bruderer, Thomas Knobloch, Crystel Barbisan, Lucie Chandat, Alexander Sudau, Isabella Siepe, Oliver Rinner, Natalie de Souza, Paola Picotti, Lukas Reiter
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/871b54abd526490392ac0a0d980afcf1
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spelling	oai:doaj.org-article:871b54abd526490392ac0a0d980afcf12021-12-02T15:10:56ZA machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes10.1038/s41467-020-18071-x2041-1723https://doaj.org/article/871b54abd526490392ac0a0d980afcf12020-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18071-xhttps://doaj.org/toc/2041-1723Proteomics is often used to map protein-drug interactions but identifying a drug’s protein targets along with the binding interfaces has not been achieved yet. Here, the authors integrate limited proteolysis and machine learning for the proteome-wide mapping of drug protein targets and binding sites.Ilaria PiazzaNigel BeatonRoland BrudererThomas KnoblochCrystel BarbisanLucie ChandatAlexander SudauIsabella SiepeOliver RinnerNatalie de SouzaPaola PicottiLukas ReiterNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Ilaria Piazza Nigel Beaton Roland Bruderer Thomas Knobloch Crystel Barbisan Lucie Chandat Alexander Sudau Isabella Siepe Oliver Rinner Natalie de Souza Paola Picotti Lukas Reiter A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes
description	Proteomics is often used to map protein-drug interactions but identifying a drug’s protein targets along with the binding interfaces has not been achieved yet. Here, the authors integrate limited proteolysis and machine learning for the proteome-wide mapping of drug protein targets and binding sites.
format	article
author	Ilaria Piazza Nigel Beaton Roland Bruderer Thomas Knobloch Crystel Barbisan Lucie Chandat Alexander Sudau Isabella Siepe Oliver Rinner Natalie de Souza Paola Picotti Lukas Reiter
author_facet	Ilaria Piazza Nigel Beaton Roland Bruderer Thomas Knobloch Crystel Barbisan Lucie Chandat Alexander Sudau Isabella Siepe Oliver Rinner Natalie de Souza Paola Picotti Lukas Reiter
author_sort	Ilaria Piazza
title	A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes
title_short	A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes
title_full	A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes
title_fullStr	A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes
title_full_unstemmed	A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes
title_sort	machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/871b54abd526490392ac0a0d980afcf1
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A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes

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