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Deep Learning-Assisted Analysis of Immunopeptidomics Data

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Peptidomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2758))

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Abstract

Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.

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Acknowledgments

We thank all members of the kusterlab and wilhelmlab for fruitful discussions. This work was in part funded by the German Federal Ministry of Education and Research (BMBF; Grant No 031L0008A and 031 L0168), European Union’s Horizon 2020 Program under Grant Agreement 823839 (H2020-INFRAIA-2018-1; EPIC-XS).

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Authors and Affiliations

  1. Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany

    Wassim Gabriel, Mario Picciani & Mathias Wilhelm

  2. Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany

    Matthew The

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  1. Wassim Gabriel
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  2. Mario Picciani
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  4. Mathias Wilhelm
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Correspondence to Mathias Wilhelm .

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Editors and Affiliations

  1. Department of Bioengineering Sciences, Weihenstephan-Tr. University of Applied Science, Freising, Germany

    Michael Schrader

  2. Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY, USA

    Lloyd D. Fricker

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© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Gabriel, W., Picciani, M., The, M., Wilhelm, M. (2024). Deep Learning-Assisted Analysis of Immunopeptidomics Data. In: Schrader, M., Fricker, L.D. (eds) Peptidomics. Methods in Molecular Biology, vol 2758. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3646-6_25

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  • DOI: https://doi.org/10.1007/978-1-0716-3646-6_25

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3645-9

  • Online ISBN: 978-1-0716-3646-6

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