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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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