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

SUBMITTER: Ju S 

PROVIDER: S-EPMC12780112 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature