Proteomics

Dataset Information

0

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

Similar Datasets

2021-04-26 | PXD021013 | Pride
2015-07-14 | PXD001904 | Pride
2015-07-14 | PXD001872 | Pride
2021-04-26 | PXD023120 | Pride
2020-10-08 | GSE159191 | GEO
2023-07-17 | MSV000092461 | MassIVE
2024-02-04 | GSE236154 | GEO
2021-09-17 | PXD028088 | Pride
2017-02-21 | GSE93315 | GEO
2023-07-17 | MSV000092456 | MassIVE