Rigorous estimation of post-translational proteasomal splicing in the immunopeptidome
Cygan, K. J.; Khaledian, E.; Blumenberg, L.; Salzler, R. R.; Shah, D.; Olson, W.; Macdonald, L. E.; Murphy, A. J.; Dhanik, A.
Show abstract
Recently, de novo peptide sequencing has made it possible to gain new insights into the human immunopeptidome without relying on peptide databases, while identifying peptides of unknown origin. Many recent studies have attributed post-translational proteasomal splicing as the origin of those peptides. Here, we describe a peptide source assignment workflow to rigorously assign the source of de novo sequenced peptides and find that the estimated extent of post-translational splicing in the immunopeptidome is much lower than previously reported. Our approach demonstrates that many peptides that were thought to be post-translationally spliced are likely linear peptides, and many peptides that were thought to be trans-spliced could be cis-spliced. We believe our approach furthers the understanding of post-translationally spliced peptides and thus improves the characterization of immunopeptidome which plays a critical role in the immune response to antigens in cancer, autoimmune disease, and infections.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry 95%
- Proteoform Identification by Combining RNA-Seq and Top-down Mass Spectrometry 95%
- Protein sequencing with single amino acid resolution discerns peptides that discriminate tropomyosin proteoforms 95%
Similar papers in this journal
- The Integration of Proteogenomics and Ribosome Profiling Circumvents Key Limitations to Increase the Coverage and Confidence of Novel Microproteins 96%
- Systematic detection of functional proteoform groups from bottom-up proteomic datasets 96%
- Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning 95%
Similar papers in this journal
Similar papers in this journal
- SHEPHARD: a modular and extensible software architecture for analyzing and annotating large protein datasets 95%
- Pepsickle rapidly and accurately predicts proteasomal cleavage sites for improved neoantigen identification 95%
- ProteoDisco: A flexible R approach to generate customized protein databases for extended search space of novel and variant proteins in proteogenomic studies 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.