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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 10, 2026

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

Mass Spectrometry-Based Protein Quantification.

Yun Chen¹, Fuqiang Wang², Feifei Xu²

¹School of Pharmacy, Nanjing Medical University, 818 Tian Yuan East Road, Nanjing, 211166, China. ychen@njmu.edu.

Advances in Experimental Medicine and Biology

|December 16, 2016

Summary

This summary is machine-generated.

Quantitative proteomics offers deeper biological insights than qualitative methods. This chapter guides researchers in selecting bioinformatics tools for accurate protein quantification and data analysis.

Keywords:

APEX ATAQS IsobariQ and Iquant MAXquant PeptideAtlas Progenesis QI Protein quantification bioinformatics Quantitative signal processing SAPRatio Skyline Targeted proteomics Trans-Proteomic Pipeline (TPP)

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Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Quantitative proteomics is crucial for understanding biological processes and disease states.
It provides more detailed insights into protein expression and modifications compared to other methods.
Accurate protein quantification is essential for biomarker discovery and therapeutic target identification.

Purpose of the Study:

To provide a framework for selecting bioinformatics tools for quantitative proteomics.
To guide researchers in data analysis, interpretation, and integration for protein quantification.
To offer an overview of quantitative proteomics and its associated computational approaches.

Main Methods:

Focus on bioinformatics algorithms and software tools for quantitative proteomics.
Leverages existing proteomics methodologies and bioinformatics approaches.
Discusses data analysis, interpretation, and integration strategies.

Main Results:

The chapter aims to equip researchers with the knowledge to choose appropriate bioinformatics tools.
It provides a rational framework for navigating the complexities of quantitative proteomics data.
Enables more insightful analysis of protein expression and posttranslational modifications.

Conclusions:

Effective selection of bioinformatics tools is key to successful quantitative proteomics.
This chapter serves as a guide for researchers to enhance their data analysis capabilities.
Facilitates a deeper understanding of biological systems through precise protein quantification.