A deep-learning framework for multi-level peptide–protein interaction prediction

Peptide-protein interactions play fundamental roles in cellular processes and are crucial for designing peptide therapeutics. Here, the authors present a deep learning framework for simultaneously predicting peptide-protein interactions and identifying peptide binding residues involved in the intera...

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Autores principales:	Yipin Lei, Shuya Li, Ziyi Liu, Fangping Wan, Tingzhong Tian, Shao Li, Dan Zhao, Jianyang Zeng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/3c57279c8dca432e92343f39c256dd79
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id	oai:doaj.org-article:3c57279c8dca432e92343f39c256dd79
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spelling	oai:doaj.org-article:3c57279c8dca432e92343f39c256dd792021-12-02T18:02:16ZA deep-learning framework for multi-level peptide–protein interaction prediction10.1038/s41467-021-25772-42041-1723https://doaj.org/article/3c57279c8dca432e92343f39c256dd792021-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25772-4https://doaj.org/toc/2041-1723Peptide-protein interactions play fundamental roles in cellular processes and are crucial for designing peptide therapeutics. Here, the authors present a deep learning framework for simultaneously predicting peptide-protein interactions and identifying peptide binding residues involved in the interactions.Yipin LeiShuya LiZiyi LiuFangping WanTingzhong TianShao LiDan ZhaoJianyang ZengNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Yipin Lei Shuya Li Ziyi Liu Fangping Wan Tingzhong Tian Shao Li Dan Zhao Jianyang Zeng A deep-learning framework for multi-level peptide–protein interaction prediction
description	Peptide-protein interactions play fundamental roles in cellular processes and are crucial for designing peptide therapeutics. Here, the authors present a deep learning framework for simultaneously predicting peptide-protein interactions and identifying peptide binding residues involved in the interactions.
format	article
author	Yipin Lei Shuya Li Ziyi Liu Fangping Wan Tingzhong Tian Shao Li Dan Zhao Jianyang Zeng
author_facet	Yipin Lei Shuya Li Ziyi Liu Fangping Wan Tingzhong Tian Shao Li Dan Zhao Jianyang Zeng
author_sort	Yipin Lei
title	A deep-learning framework for multi-level peptide–protein interaction prediction
title_short	A deep-learning framework for multi-level peptide–protein interaction prediction
title_full	A deep-learning framework for multi-level peptide–protein interaction prediction
title_fullStr	A deep-learning framework for multi-level peptide–protein interaction prediction
title_full_unstemmed	A deep-learning framework for multi-level peptide–protein interaction prediction
title_sort	deep-learning framework for multi-level peptide–protein interaction prediction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3c57279c8dca432e92343f39c256dd79
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A deep-learning framework for multi-level peptide–protein interaction prediction

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