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

Abstract

N-terminal arginylation (Nt-arginylation) is a multifunctional post-translational modification (PTM) with roles in protein quality control, organelle homeostasis and stress signaling, but its study has been limited by technical challenges. Here, we develop an integrated approach combining N-terminomics with machine learning-based filtering to identify in cellulo Nt-arginylation. Using Arg-starting missed cleavage peptides as proxies for ATE1-mediated arginylation, we train a transfer learning model to predict mass spectra and retention times. By applying the prediction models with an additional statistical filter, we identify 134 Nt-arginylation sites in thapsigargin-treated HeLa cells. Arginylation is enriched in proteins from various organelles, especially at caspase cleavage and signal peptide processing sites. Eight of twelve tested proteins are further validated for their interaction with p62 ZZ domain. Temporal profiling reveals that ATF4 increases 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.