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Deep learning boosts immunopeptidomics one mass spectrum at a time

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 such non-tryptic peptides presents substantial computational challenges. To address these, we synthesized >300,000 peptides within the ProteomeTools project representing HLA class I & II ligands and products of the proteases AspN and LysN and analyzed these by multi-modal LC-MS/MS. The resulting data enabled training of a single model using the deep learning framework Prosit that shows outstanding prediction accuracy of fragment ion spectra for tryptic and non-tryptic peptides. Applying Prosit demonstrates that the identification of HLA peptides can be improved by 50-300% on average, that proteasomal HLA peptide splicing may not exist and that additional neo-epitopes that elicit an immune response can be identified from patient tumors. Together, the provided peptides, spectra and computational tools substantially expand the scope of immunopeptidomics workflows.

INSTRUMENT(S): Orbitrap Fusion Lumos, Q Exactive HF

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Daniel Zolg

LAB HEAD: Bernhard Kuster

PROVIDER: PXD021398 | Pride | 2021-04-26

REPOSITORIES: Pride

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			Action	DRS
	20120321_EXQ1_MiBa_SA_HCC1143_1.raw	Raw
	20120321_EXQ1_MiBa_SA_HCC1143_2.raw	Raw
	20120322_EXQ1_MiBa_SA_HCC1143_1_A.raw	Raw
	20120515_EXQ3_MiBa_SA_HCT116_mHLA-1.raw	Raw
	20120518_EXQ0_MiBa_SA_HCT116_mHLA-2.raw	Raw

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