Fragment Mass Spectrum Prediction Facilitates Site Localization of Phosphorylation

Yi Yang, Péter Horvatovich*, Liang Qiao*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)

Abstract

Liquid chromatography tandem mass spectrometry (LC-MS/MS) has been the most widely used technology for phosphoproteomics studies. As an alternative to database searching and probability-based phosphorylation site localization approaches, spectral library searching has been proved to be effective in the identification of phosphopeptides. However, incompletion of experimental spectral libraries limits the identification capability. Herein, we utilize MS/MS spectrum prediction coupled with spectral matching for site localization of phosphopeptides. In silico MS/MS spectra are generated from peptide sequences by deep learning/machine learning models trained with non-phosphopeptides. Then mass shift according to phosphorylation sites, phosphoric acid neutral loss and a "budding" strategy are adopted to adjust the in silico mass spectra. In silico MS/MS spectra can also be generated in one step for phosphopeptides using models trained with phosphopeptides. The method is benchmarked on data sets of synthetic phosphopeptides and is used to process real biological samples. It is demonstrated to be a method requiring only computational resources that supplements the probability-based approaches for phosphorylation site localization of singly and multiply phosphorylated peptides.

Original languageEnglish
Pages (from-to)634-644
Number of pages11
JournalJournal of Proteome Research
Volume20
Issue number1
Early online date28-Sep-2020
DOIs
Publication statusPublished - 1-Jan-2021

Keywords

  • machine learning
  • mass spectrum prediction
  • phosphorylation
  • site localization

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