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Cited by 3Advanced Proteomics Techniques and ApplicationsUbiquitin and proteasome pathwaysMass Spectrometry Techniques and Applications

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Implementing N-terminomics and machine learning to probe Nt-arginylation

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Ju, Shinyeong, Nawale, Laxman, Lee, Seonjeong, Kim, Jung Gi, Lee, Hankyul, Park, Narae, Kim, Dong Hyun, Cha-Molstad, Hyunjoo, Lee, Cheolju·2025·Nature Communications
DOI 10.1038/s41467-025-66883-6PMC PMC12780112

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Abstract

Abstract N-terminal arginylation (Nt-arginylation) is a degradation signal in the ubiquitin-proteasome and autophagy-lysosomal pathways, but its study has been limited by technical challenges. Here, we developed an integrated approach combining N-terminomics with machine learning-based filtering to identify in vivo Nt-arginylation. By using Arg-starting missed cleavage peptides as proxies for ATE1-mediated arginylation, we trained a transfer learning model to predict MS2 spectra and retention times. By applying the prediction models with an additional statistical filter, we identified 134 Nt-arginylation sites in thapsigargin-treated HeLa cells. Arginylation was enriched in proteins from various organelles, especially at caspase cleavage and signal peptide processing sites. Several proteins were further validated for their interaction with p62 ZZ domain. Temporal profiling revealed that ATF4 increased early post-stress, followed by arginylation at caspase-3 substrates and ER signal-cleaved proteins. Our approach enables sensitive detection of rare N-terminal modifications, offering potential for biomarker and drug target discovery.

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Metadata

Authors
Ju, S., Nawale, L., Lee, S., Kim, J. G., Lee, H., Park, N., Kim, D. H., Cha-Molstad, H., & Lee, C.
Affiliations
  • Korea Institute of Science and Technology (KR)
  • Korea Research Institute of Bioscience and Biotechnology (KR)
  • Kyung Hee University (KR)
  • National Cancer Center (KR)
Corresponding author
Lee, Cheolju
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2025-12-09
Volume
17
Issue
1
Pages
172-172
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-025-66883-6
PMC
PMC12780112
OpenAlex
W4417151308
Cited by
3
References
3
Citations by year
2026: 3
FWCI
0.76
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Research Square (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Research Foundation of Korea (RS-2023-00279134, RS-2022-NR068428, RS-2024-00444177)
  • Korea Institute of Science and Technology (Grand Challenge)
Topics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Ubiquitin and proteasome pathways
  • Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
Keywords
N-terminomics, ATE1, post-translational modification, transfer learning, caspase cleavage sites, endoplasmic reticulum stress, ATF4, targeted mass spectrometry, false positive reduction, HeLa cells
MeSH terms
Machine Learning, Arginine, HeLa Cells, Humans, Protein Processing, Post-Translational, Thapsigargin, Proteomics, Activating Transcription Factor 4, Caspase 3

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