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- Document type:
- Zeitschriftenaufsatz
- Author(s):
- Wilhelm, Mathias; Zolg, Daniel P.; Graber, Michael; Gessulat, Siegfried; Schmidt, Tobias; Schnatbaum, Karsten; Schwencke-Westphal, Celina; Seifert, Philipp; de Andrade Krätzig, Niklas; Zerweck, Johannes; Knaute, Tobias; Bräunlein, Eva; Samaras, Patroklos; Lautenbacher, Ludwig; Klaeger, Susan; Wenschuh, Holger; Rad, Roland; Delanghe, Bernard; Huhmer, Andreas; Carr, Steven A.; Clauser, Karl R.; Krackhardt, Angela M.; Reimer, Ulf; Kuster, Bernhard
- Title:
- Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics
- Abstract:
- Characterizing the human leukocyte antigen (HLA) bound ligandome by mass spectrometry (MS) holds great promise for developing vaccines and drugs for immune-oncology. Still, the identification of non-tryptic peptides presents substantial computational challenges. To address these, we synthesized and analyzed >300,000 peptides by multi-modal LC-MS/MS within the ProteomeTools project representing HLA class I & II ligands and products of the proteases AspN and LysN. The resulting data enabled training of a single model using the deep learning framework Prosit, allowing the accurate prediction of fragment ion spectra for tryptic and non-tryptic peptides. Applying Prosit demonstrates that the identification of HLA peptides can be improved up to 7-fold, that 87% of the proposed proteasomally spliced HLA peptides may be incorrect and that dozens of additional immunogenic neo-epitopes can be identified from patient tumors in published data. Together, the provided peptides, spectra and computational tools substantially expand the analytical depth of immunopeptidomics workflows.
- Journal title:
- Nature Communications
- Year:
- 2021
- Journal volume:
- 12
- Journal issue:
- 1
- Fulltext / DOI:
- doi:10.1038/s41467-021-23713-9
- Publisher:
- Springer Science and Business Media LLC
- E-ISSN:
- 2041-1723
- Date of publication:
- 07.06.2021
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