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Peptide Identification Using Tandem Mass Spectrometry

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...

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Updated: Sep 9, 2025

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

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

Published on: November 15, 2017

IQUP identifies quantitatively unreliable spectra with machine learning for isobaric labeling-based proteomics.

Yi-Yun Chou¹, Jing-Chen Yang¹, Tun-Chu Tsai¹

¹Department of Bioinformatics and Biomedical Engineering, Asia University, Taichung, 413, Taiwan.

Scientific Reports

|August 29, 2025

Summary

We developed IQUP, a machine learning method to identify unreliable peptide-spectrum matches (PSMs) in mass spectrometry proteomics. This improves quantitation accuracy by filtering out low-quality data, enhancing proteomic analysis reliability.

Keywords:

Isobaric labeling Machine learning Proteomic quantitation TMT iTRAQ

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Last Updated: Sep 9, 2025

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

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Published on: November 15, 2017

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Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling

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Published on: July 1, 2014

Area of Science:

Proteomics
Mass Spectrometry
Computational Biology

Background:

Isobaric labeling technology is widely used for quantitative proteomics.
Current methods using target-decoy search may include peptide-spectrum matches (PSMs) with high quantitation errors.
This can compromise the overall accuracy of proteomic quantitation.

Purpose of the Study:

To introduce IQUP, a machine learning-based method for identifying quantitatively unreliable PSMs (QUPs).
To improve the accuracy of proteomic quantitation in isobaric labeling experiments.

Main Methods:

PSMs were characterized using 16 spectral and distance-based features.
A machine learning approach was employed to train models for QUP identification.
Performance was evaluated on three independent datasets using accuracy, AUC, and MCC metrics.

Main Results:

The best models achieved high performance with accuracies of 0.883-0.966, AUCs of 0.924-0.963, and MCCs of 0.596-0.691.
Significant differences in relative error distributions were observed between QUPs and quantitatively reliable PSMs (QRPs).
Utilizing only predicted QRPs for quantitation significantly reduced peptides with large relative errors (15.3-83.3%) and increased peptides with small relative errors (3.1-25.5%).

Conclusions:

IQUP demonstrates robust performance and generalizability across diverse datasets.
The method effectively identifies quantitatively unreliable PSMs.
IQUP has significant potential to enhance proteomic quantitation accuracy at both PSM and peptide levels.