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

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…

• NI
  National Institutes of Health

  Awards: R01-GM-094231, U24-CA210967, U24-CA271037
• NC
  National Cancer Institute

  Awards: U24-CA210967, 5T32-CA140044-12
• NI
  National Institute of General Medical Sciences

  Award: R01-GM-094231