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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.
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GC–MS is a powerful hyphenated method commonly used in forensics and environmental...

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Related Experiment Video

Updated: Aug 27, 2025

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Published on: April 1, 2017

Improving Peptide-Level Mass Spectrometry Analysis via Double Competition.

Andy Lin¹, Temana Short², William Stafford Noble^3,4

¹Chemical and Biological Signatures, Pacific Northwest National Laboratory, Seattle, Washington 98109, United States.

Journal of Proteome Research

|September 27, 2022

Summary

This summary is machine-generated.

Researchers should shift from peptide-spectrum match (PSM)-level to peptide-level false discovery rate (FDR) analysis in proteomics. A dual competition method (PSM-and-peptide) significantly increases peptide identification power compared to standard approaches.

Keywords:

FDR control PSM-level FDR database search false discovery rate peptide-level FDR target-decoy competition

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Last Updated: Aug 27, 2025

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Published on: April 1, 2017

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Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

Published on: March 23, 2020

Area of Science:

Proteomics
Bioinformatics
Mass Spectrometry

Background:

Shotgun proteomics data analysis commonly uses false discovery rate (FDR) control via target-decoy competition (TDC).
PSM-level TDC, while standard, can yield biased FDR estimates.
Current peptide-level TDC methods have limitations in statistical power.

Purpose of the Study:

To highlight and empirically support findings on biased PSM-level FDR estimates.
To investigate and compare different peptide-level TDC strategies.
To identify the most statistically powerful method for peptide discovery in proteomics.

Main Methods:

Empirical augmentation of previous work on TDC bias.
Investigation of three peptide-level TDC approaches.
Comparative analysis of statistical power across methods at a 1% FDR threshold.

Main Results:

PSM-level TDC can be liberally biased.
The common "PSM-only" peptide-level TDC method shows the lowest statistical power.
The "PSM-and-peptide" double competition method offers the highest statistical power, increasing peptide discovery by 17%.

Conclusions:

Peptide-level analysis is recommended over PSM-level analysis for more reliable FDR control.
The "PSM-and-peptide" TDC strategy is the most effective for maximizing peptide identification in proteomics datasets.
Adopting the "PSM-and-peptide" method can significantly enhance the discovery of peptides from mass spectrometry data.