DeepRank: a deep learning framework for data mining 3D protein-protein interfaces

The authors present DeepRank, a deep learning framework for the data mining of large sets of 3D protein-protein interfaces (PPI). They use DeepRank to address two challenges in structural biology: distinguishing biological versus crystallographic PPIs in crystal structures, and secondly the ranking...

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Autores principales:	Nicolas Renaud, Cunliang Geng, Sonja Georgievska, Francesco Ambrosetti, Lars Ridder, Dario F. Marzella, Manon F. Réau, Alexandre M. J. J. Bonvin, Li C. Xue
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/b5e9d64b96e1438795a4aebfc8e1dd1c
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spelling	oai:doaj.org-article:b5e9d64b96e1438795a4aebfc8e1dd1c2021-12-05T12:22:28ZDeepRank: a deep learning framework for data mining 3D protein-protein interfaces10.1038/s41467-021-27396-02041-1723https://doaj.org/article/b5e9d64b96e1438795a4aebfc8e1dd1c2021-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-27396-0https://doaj.org/toc/2041-1723The authors present DeepRank, a deep learning framework for the data mining of large sets of 3D protein-protein interfaces (PPI). They use DeepRank to address two challenges in structural biology: distinguishing biological versus crystallographic PPIs in crystal structures, and secondly the ranking of docking models.Nicolas RenaudCunliang GengSonja GeorgievskaFrancesco AmbrosettiLars RidderDario F. MarzellaManon F. RéauAlexandre M. J. J. BonvinLi C. XueNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Nicolas Renaud Cunliang Geng Sonja Georgievska Francesco Ambrosetti Lars Ridder Dario F. Marzella Manon F. Réau Alexandre M. J. J. Bonvin Li C. Xue DeepRank: a deep learning framework for data mining 3D protein-protein interfaces
description	The authors present DeepRank, a deep learning framework for the data mining of large sets of 3D protein-protein interfaces (PPI). They use DeepRank to address two challenges in structural biology: distinguishing biological versus crystallographic PPIs in crystal structures, and secondly the ranking of docking models.
format	article
author	Nicolas Renaud Cunliang Geng Sonja Georgievska Francesco Ambrosetti Lars Ridder Dario F. Marzella Manon F. Réau Alexandre M. J. J. Bonvin Li C. Xue
author_facet	Nicolas Renaud Cunliang Geng Sonja Georgievska Francesco Ambrosetti Lars Ridder Dario F. Marzella Manon F. Réau Alexandre M. J. J. Bonvin Li C. Xue
author_sort	Nicolas Renaud
title	DeepRank: a deep learning framework for data mining 3D protein-protein interfaces
title_short	DeepRank: a deep learning framework for data mining 3D protein-protein interfaces
title_full	DeepRank: a deep learning framework for data mining 3D protein-protein interfaces
title_fullStr	DeepRank: a deep learning framework for data mining 3D protein-protein interfaces
title_full_unstemmed	DeepRank: a deep learning framework for data mining 3D protein-protein interfaces
title_sort	deeprank: a deep learning framework for data mining 3d protein-protein interfaces
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b5e9d64b96e1438795a4aebfc8e1dd1c
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DeepRank: a deep learning framework for data mining 3D protein-protein interfaces

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