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

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Podrobná bibliografie
Název:	A deep-learning framework for multi-level peptide–protein interaction prediction
Autoři:	Yipin Lei, Shuya Li, Ziyi Liu, Fangping Wan, Tingzhong Tian, Shao Li, Dan Zhao, Jianyang Zeng
Zdroj:	Nature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
Informace o vydavateli:	Nature Portfolio, 2021.
Rok vydání:	2021
Sbírka:	LCC:Science
Témata:	Science
Popis:	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.
Druh dokumentu:	article
Popis souboru:	electronic resource
Jazyk:	English
ISSN:	2041-1723
Relation:	https://doaj.org/toc/2041-1723
DOI:	10.1038/s41467-021-25772-4
Přístupová URL adresa:	https://doaj.org/article/3c57279c8dca432e92343f39c256dd79
Přístupové číslo:	edsdoj.3c57279c8dca432e92343f39c256dd79
Databáze:	Directory of Open Access Journals

Popis
Abstrakt:	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.
ISSN:	20411723
DOI:	10.1038/s41467-021-25772-4