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

Journal: Nature communications
Published Date: Jul 27, 2023

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.

Authors

  • Kevin L Yang
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Fengchao Yu
    Department of Pathology, University of Michigan, Ann Arbor, MI, USA. yufe@umich.edu.
  • Guo Ci Teo
    Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
  • Kai Li
    Department of Gastroenterology, Shanghai First People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
  • Vadim Demichev
    Department of Biochemistry and The Milner Therapeutics Institute, University of Cambridge, Cambridge, UK.
  • Markus Ralser
    The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK. markus.ralser@crick.ac.uk.
  • Alexey I Nesvizhskii
    Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.

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