Fragment Mass Spectrum Prediction Facilitates Site Localization of Phosphorylation

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 nonphosphopeptides.
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.