1 research outputs found
Prediction of Peptide Fragment Ion Mass Spectra by Data Mining Techniques
Accurate
prediction of peptide fragment ion mass spectra is one
of the critical factors to guarantee confident peptide identification
by protein sequence database search in bottom-up proteomics. In an
attempt to accurately and comprehensively predict this type of mass
spectra, a framework named MS<sup>2</sup>PBPI is proposed. MS<sup>2</sup>PBPI first extracts fragment ions from large-scale MS/MS spectra
data sets according to the peptide fragmentation pathways and uses
binary trees to divide the obtained bulky data into tens to more than
1000 regions. For each adequate region, stochastic gradient boosting
tree regression model is constructed. By constructing hundreds of
these models, MS<sup>2</sup>PBPI is able to predict MS/MS spectra
for unmodified and modified peptides with reasonable accuracy. Moreover,
high consistency between predicted and experimental MS/MS spectra
derived from different ion trap instruments with low and high resolving
power is achieved. MS<sup>2</sup>PBPI outperforms existing algorithms
MassAnalyzer and PeptideART