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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: Mar 9, 2026

Peptide-based Identification of Functional Motifs and their Binding Partners

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

Visualizing and comparing results of different peptide identification methods.

Yassene Mohammed^1,2, Magnus Palmblad¹

¹Center for Proteomics and Metabolomics, Leiden University Medical Center, the Netherlands.

Briefings in Bioinformatics

|December 25, 2016

Summary

This study introduces a new framework to compare peptide identification algorithms in proteomics. It visualizes how different search strategies complement each other, aiding in understanding algorithm performance and false discovery rates.

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Last Updated: Mar 9, 2026

Peptide-based Identification of Functional Motifs and their Binding Partners

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Area of Science:

Proteomics
Mass Spectrometry
Bioinformatics

Background:

Peptide identification in mass spectrometry-based proteomics commonly uses spectrum comparison against predicted sequences.
Alternative methods include spectral libraries and de novo sequencing, each with distinct approaches to peptide identification.

Purpose of the Study:

To present a theoretical framework and data processing workflow for visualizing and comparing peptide identification algorithms.
To assess the complementarity of different search strategies (sequence search, spectral libraries, de novo sequencing).

Main Methods:

Developed a method to visualize and compare results from three distinct peptide identification algorithm types.
Included representative algorithms: X! Tandem (sequence search), SpectraST (spectral library), and PepNovo (de novo sequencing).
Analyzed algorithm agreement classes and complementarity.

Main Results:

The framework visualizes the distinct agreement classes among the three search strategies.
Demonstrated the complementarity of sequence search, spectral libraries, and de novo sequencing.
Highlighted the impact of decoy sequences on false discovery rate evaluation.

Conclusions:

The developed method enables advanced investigation into the relative performance of different peptide identification algorithms.
Provides insights into how combining search strategies can improve proteomic data analysis.
Offers a novel approach to understanding the influence of decoy strategies on evaluating identification accuracy.