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Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF

Author

Listed:
  • Charlotte Adams

    (University of Antwerp)

  • Wassim Gabriel

    (Technical University of Munich)

  • Kris Laukens

    (University of Antwerp)

  • Mario Picciani

    (Technical University of Munich)

  • Mathias Wilhelm

    (Technical University of Munich
    Technical University of Munich)

  • Wout Bittremieux

    (University of Antwerp)

  • Kurt Boonen

    (University of Antwerp
    Flemish Institute for Technological Research (VITO))

Abstract

Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. We analyze 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project on a timsTOF-Pro to generate a ground-truth dataset containing 93,227 MS/MS spectra of 74,847 unique peptides, that is used to fine-tune the deep learning-based fragment ion intensity prediction model Prosit. We demonstrate up to 3-fold improvement in the identification of immunopeptides, as well as increased detection of immunopeptides from low input samples.

Suggested Citation

  • Charlotte Adams & Wassim Gabriel & Kris Laukens & Mario Picciani & Mathias Wilhelm & Wout Bittremieux & Kurt Boonen, 2024. "Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48322-0
    DOI: 10.1038/s41467-024-48322-0
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    References listed on IDEAS

    as
    1. Florian Meier & Niklas D. Köhler & Andreas-David Brunner & Jean-Marc H. Wanka & Eugenia Voytik & Maximilian T. Strauss & Fabian J. Theis & Matthias Mann, 2021. "Deep learning the collisional cross sections of the peptide universe from a million experimental values," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
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    6. Kevin L. Yang & Fengchao Yu & Guo Ci Teo & Kai Li & Vadim Demichev & Markus Ralser & Alexey I. Nesvizhskii, 2023. "MSBooster: improving peptide identification rates using deep learning-based features," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
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