Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics.
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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. |
Year of Publication | 2021
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Journal | Nat Commun
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Volume | 12
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Issue | 1
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Pages | 3346
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Date Published | 2021 06 07
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ISSN | 2041-1723
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DOI | 10.1038/s41467-021-23713-9
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PubMed ID | 34099720
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PubMed Central ID | PMC8184761
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Grant list | U01 CA214125 / CA / NCI NIH HHS / United States
U24 CA210986 / CA / NCI NIH HHS / United States
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