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Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics.

Mathias Wilhelm1,2, Daniel P Zolg3, Michael Graber3

  • 1Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany. mathias.wilhelm@tum.de.

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|June 8, 2021
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Summary

This study introduces a deep learning model to accurately predict peptide fragment spectra, significantly improving the identification of human leukocyte antigen (HLA) peptides for immune-oncology applications.

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Area of Science:

  • Immunology
  • Proteomics
  • Computational Biology

Background:

  • Mass spectrometry (MS) is crucial for characterizing the human leukocyte antigen (HLA) bound ligandome for immune-oncology drug and vaccine development.
  • Identifying non-tryptic peptides in HLA ligandome analysis presents significant computational challenges.

Purpose of the Study:

  • To develop a computational model for accurate prediction of fragment ion spectra for both tryptic and non-tryptic peptides.
  • To enhance the identification of HLA peptides and immunogenic neo-epitopes.

Main Methods:

  • Synthesis and analysis of over 300,000 peptides (HLA class I & II ligands, AspN, LysN products) using multi-modal LC-MS/MS.
  • Training a single deep learning model (Prosit) on the generated data for spectral prediction.
  • Application of the Prosit model to published immunopeptidomics data.

Main Results:

  • The Prosit model accurately predicts fragment ion spectra for tryptic and non-tryptic peptides.
  • Identification of HLA peptides improved up to 7-fold.
  • Re-evaluation suggests 87% of proposed proteasomally spliced HLA peptides may be incorrect.
  • Dozens of additional immunogenic neo-epitopes were identified in patient tumor data.

Conclusions:

  • The developed Prosit model significantly enhances HLA peptide identification in immunopeptidomics.
  • The findings challenge previous assumptions about proteasomally spliced HLA peptides.
  • This work provides valuable data and tools to deepen the analytical scope of immunopeptidomics workflows.