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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Main Authors:	Yipin Lei, Shuya Li, Ziyi Liu, Fangping Wan, Tingzhong Tian, Shao Li, Dan Zhao, Jianyang Zeng
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-09-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-021-25772-4

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spelling	doaj-3c57279c8dca432e92343f39c256dd792021-09-19T11:48:39ZengNature Publishing GroupNature Communications2041-17232021-09-0112111010.1038/s41467-021-25772-4A deep-learning framework for multi-level peptide–protein interaction predictionYipin Lei0Shuya Li1Ziyi Liu2Fangping Wan3Tingzhong Tian4Shao Li5Dan Zhao6Jianyang Zeng7Institute for Interdisciplinary Information Sciences, Tsinghua UniversityMachine Learning Department, Silexon AI Technology Co., Ltd.Machine Learning Department, Silexon AI Technology Co., Ltd.Machine Learning Department, Silexon AI Technology Co., Ltd.Institute for Interdisciplinary Information Sciences, Tsinghua UniversityInstitute of TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist, Department of Automation, Tsinghua UniversityInstitute for Interdisciplinary Information Sciences, Tsinghua UniversityInstitute for Interdisciplinary Information Sciences, Tsinghua UniversityPeptide-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.https://doi.org/10.1038/s41467-021-25772-4
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Yipin Lei Shuya Li Ziyi Liu Fangping Wan Tingzhong Tian Shao Li Dan Zhao Jianyang Zeng
spellingShingle	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 Nature Communications
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 Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-09-01
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.
url	https://doi.org/10.1038/s41467-021-25772-4
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A deep-learning framework for multi-level peptide–protein interaction prediction

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