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Related Experiment Video

Updated: Nov 15, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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Peak Identification and Quantification by Proteomic Mass Spectrogram Decomposition.

Pasrawin Taechawattananant¹, Kazuyoshi Yoshii^2,3, Yasushi Ishihama^1,4

¹Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan.

Journal of Proteome Research

|March 4, 2021

Summary

We developed Proteomic Mass Spectrogram Decomposition (ProtMSD), a novel statistical method for identifying and quantifying peptides and proteins from complex liquid chromatography/mass spectrometry data. This approach improves proteome analysis accuracy without extensive preprocessing.

Keywords:

bioinformatics machine learning mass spectrogram match-between-runs matrix decomposition protein identification and quantification shotgun proteomics

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Area of Science:

Proteomics
Analytical Chemistry
Computational Biology

Background:

Liquid chromatography/mass spectrometry (LC/MS) technology advances proteome analysis.
Complex mass spectrograms require sophisticated algorithms for interpretation.

Purpose of the Study:

To propose a novel statistical method, ProtMSD, for joint peptide and protein identification and quantification.
To improve the accuracy and efficiency of proteomic data analysis.

Main Methods:

ProtMSD utilizes matrix decomposition with a group sparsity constraint.
Incorporates protein-peptide hierarchy, isotopic profiles, retention times, and noise spectra.
Avoids conventional preprocessing steps like thresholding and peak picking.

Main Results:

ProtMSD demonstrated high agreement with established tools (Mascot/Skyline, MaxQuant).
Achieved 94.79% peptide and 98.21% protein agreement for *E. coli*.
Achieved 103% peptide and 101% protein agreement for yeast, validating match-between-runs.

Conclusions:

ProtMSD offers a robust alternative for LC/MS-based proteome identification and quantification.
This is the first application of matrix decomposition for this purpose.
The method enhances accuracy by leveraging inherent biological and spectral information.