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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: Jun 2, 2026

Synthesis and Structure Determination of µ-Conotoxin PIIIA Isomers with Different Disulfide Connectivities

Synthesis and Structure Determination of µ-Conotoxin PIIIA Isomers with Different Disulfide Connectivities

Published on: October 2, 2018

Peptide identification quality control.

Marc Vaudel¹, Julia M Burkhart, Albert Sickmann

¹ISAS-Leibniz Institut für Analytische Wissenschaften-ISAS-eV, Dortmund, Germany.

|April 19, 2011

Summary

This summary is machine-generated.

New quality control methods improve proteomics identification. Target/decoy searches enhance data analysis, increasing identifications by 13% while maintaining confidence levels for better proteomics results.

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Last Updated: Jun 2, 2026

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Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Published on: August 17, 2015

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Proteomics data set identification relies on search engines.
Current identification processes often lack rigorous quality control.
Lab habits dictate configuration, leading to suboptimal results.

Purpose of the Study:

Introduce simple, broadly applicable quality control (QC) criteria for proteomics identification.
Enhance the reliability and reproducibility of proteomics data analysis.
Improve the unsupervised execution of search engine configurations.

Main Methods:

Implement target/decoy searches for unbiased quality control of identification parameters.
Develop metrics to evaluate the precision and robustness of false discovery rate (FDR).
Quality control the application of the target/decoy strategy itself.

Main Results:

Target/decoy searches significantly improve upon existing identification standards.
MASCOT identifications increased by 13% at a constant confidence level.
Established intuitive metrics for FDR evaluation.

Conclusions:

Simple QC methods, including target/decoy searches, offer significant improvements in proteomics identification.
The developed metrics provide a robust framework for assessing FDR.
Standardized QC enhances the reliability of large-scale proteomics data analysis.