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MSBooster: improving peptide identification rates using deep learning-based features

Author

Listed:
  • Kevin L. Yang

    (University of Michigan)

  • Fengchao Yu

    (University of Michigan)

  • Guo Ci Teo

    (University of Michigan)

  • Kai Li

    (University of Michigan)

  • Vadim Demichev

    (Charité Universitätsmedizin
    University of Cambridge)

  • Markus Ralser

    (Charité Universitätsmedizin
    University of Oxford
    Max Planck Institute for Molecular Genetics)

  • Alexey I. Nesvizhskii

    (University of Michigan
    University of Michigan)

Abstract

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40129-9
    DOI: 10.1038/s41467-023-40129-9
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    References listed on IDEAS

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    1. Bo Wen & Kai Li & Yun Zhang & Bing Zhang, 2020. "Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    2. Mathias Wilhelm & Daniel P. Zolg & Michael Graber & Siegfried Gessulat & Tobias Schmidt & Karsten Schnatbaum & Celina Schwencke-Westphal & Philipp Seifert & Niklas Andrade Krätzig & Johannes Zerweck &, 2021. "Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    3. Mathias Wilhelm & Daniel P. Zolg & Michael Graber & Siegfried Gessulat & Tobias Schmidt & Karsten Schnatbaum & Celina Schwencke-Westphal & Philipp Seifert & Niklas Andrade Krätzig & Johannes Zerweck &, 2021. "Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics," Nature Communications, Nature, vol. 12(1), pages 1-1, December.
    4. 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.
    5. Wen-Feng Zeng & Xie-Xuan Zhou & Sander Willems & Constantin Ammar & Maria Wahle & Isabell Bludau & Eugenia Voytik & Maximillian T. Strauss & Matthias Mann, 2022. "AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    6. Ruedi Aebersold & Matthias Mann, 2016. "Mass-spectrometric exploration of proteome structure and function," Nature, Nature, vol. 537(7620), pages 347-355, September.
    7. Michal Bassani-Sternberg & Eva Bräunlein & Richard Klar & Thomas Engleitner & Pavel Sinitcyn & Stefan Audehm & Melanie Straub & Julia Weber & Julia Slotta-Huspenina & Katja Specht & Marc E. Martignoni, 2016. "Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry," Nature Communications, Nature, vol. 7(1), pages 1-16, December.
    8. Fengchao Yu & Guo Ci Teo & Andy T. Kong & Klemens Fröhlich & Ginny Xiaohe Li & Vadim Demichev & Alexey I. Nesvizhskii, 2023. "Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    9. Yi Yang & Xiaohui Liu & Chengpin Shen & Yu Lin & Pengyuan Yang & Liang Qiao, 2020. "In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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    4. 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.

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