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

Area of Science:

• Biochemistry
• Molecular Biology
• Proteomics

Background:

• N-terminal arginylation (Nt-arginylation) is a crucial post-translational modification (PTM) impacting protein quality control, organelle homeostasis, and stress signaling.
• Previous studies of Nt-arginylation were hindered by significant technical challenges.

Purpose of the Study:

• To develop an integrated approach for identifying N-terminal arginylation sites in cells.
• To overcome technical limitations in studying this important PTM.

Main Methods:

• Combined N-terminomics with machine learning-based filtering to detect Nt-arginylation in cellulo.
• Utilized Arg-starting missed cleavage peptides as proxies for ATE1-mediated arginylation.
• Trained a transfer learning model to predict mass spectra and retention times, followed by statistical filtering.

Main Results:

• Identified 134 novel Nt-arginylation sites in thapsigargin-treated HeLa cells.
• Found arginylation enrichment in proteins involved in various organelles, particularly at caspase cleavage and signal peptide processing sites.
• Validated interactions of eight tested proteins with the p62 ZZ domain and observed temporal changes in arginylation post-stress.

Conclusions:

• The developed approach enables sensitive detection of rare N-terminal modifications.
• This method holds potential for discovering novel biomarkers and drug targets related to Nt-arginylation.
• Provides new insights into the roles of Nt-arginylation in cellular stress responses.