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

Yipin Lei, Shuya Li, Ziyi Liu, Fangping Wan, Tingzhong Tian, Shao Li, Dan Zhao () and Jianyang Zeng ()
Additional contact information
Yipin Lei: Institute for Interdisciplinary Information Sciences, Tsinghua University
Shuya Li: Machine Learning Department, Silexon AI Technology Co., Ltd.
Ziyi Liu: Machine Learning Department, Silexon AI Technology Co., Ltd.
Fangping Wan: Machine Learning Department, Silexon AI Technology Co., Ltd.
Tingzhong Tian: Institute for Interdisciplinary Information Sciences, Tsinghua University
Shao Li: Institute of TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist, Department of Automation, Tsinghua University
Dan Zhao: Institute for Interdisciplinary Information Sciences, Tsinghua University
Jianyang Zeng: Institute for Interdisciplinary Information Sciences, Tsinghua University

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract Peptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide-protein interactions. However, most of the existing prediction approaches heavily depend on high-resolution structure data. Here, we present a deep learning framework for multi-level peptide-protein interaction prediction, called CAMP, including binary peptide-protein interaction prediction and corresponding peptide binding residue identification. Comprehensive evaluation demonstrated that CAMP can successfully capture the binary interactions between peptides and proteins and identify the binding residues along the peptides involved in the interactions. In addition, CAMP outperformed other state-of-the-art methods on binary peptide-protein interaction prediction. CAMP can serve as a useful tool in peptide-protein interaction prediction and identification of important binding residues in the peptides, which can thus facilitate the peptide drug discovery process.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-021-25772-4 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25772-4

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-25772-4

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().