Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics.

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

doi:10.1038/s41467-021-23713-9

2021

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Dokumenttyp:: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Autor(en):: 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
Titel:: 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.
Zeitschriftentitel:: Nat Commun
Jahr:: 2021
Band / Volume:: 12
Heft / Issue:: 1
Volltext / DOI:: doi:10.1038/s41467-021-23713-9
PubMed:: http://view.ncbi.nlm.nih.gov/pubmed/34099720
Print-ISSN:: 2041-1723
TUM Einrichtung:: III. Medizinische Klinik und Poliklinik (Hämatologie / Onkologie); Professur für Translationale gastroenterologische Onkologie (Prof. Rad)
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