Peak Identification and Quantification by Proteomic Mass Spectrogram Decomposition

Recent advances in the liquid chromatography/mass
spectrometry
(LC/MS) technology have improved the sensitivity, resolution, and
speed of proteome analysis, resulting in increasing demand for more
sophisticated algorithms to interpret complex mass spectrograms. Here,
we propose a novel statistical method, proteomic mass spectrogram
decomposition (ProtMSD), for joint identification and quantification
of peptides and proteins. Given the proteomic mass spectrogram and
the reference mass spectra of all possible peptide ions associated
with proteins as a dictionary, ProtMSD estimates the chromatograms
of those peptide ions under a group sparsity constraint without using
the conventional careful preprocessing (e.g., thresholding and peak
picking). We show that the method was significantly improved using
protein–peptide hierarchical relationships, isotopic distribution
profiles, reference retention times of peptide ions, and prelearned
mass spectra of noise. We examined the concept of database search,
library search, and match-between-runs. Our ProtMSD showed excellent
agreements of 3277 peptide ions (94.79%) and 493 proteins (98.21%)
with Mascot/Skyline for an Escherichia coli proteome sample and of 4460 peptide ions (103%) and 588 proteins
(101%) with match-between-runs by MaxQuant for a yeast proteome sample.
This is the first attempt to use a matrix decomposition technique
as a tool for LC/MS-based proteome identification and quantification.