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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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Fast and Reliable Quantitative Peptidomics with labelpepmatch.

Rik Verdonck¹, Wouter De Haes¹, Dries Cardoen¹

¹Biology Department, KU Leuven , Leuven, Belgium.

Journal of Proteome Research

|February 2, 2016

Summary

This study introduces labelpepmatch, an R-package simplifying the analysis of labeled peptide spectra in quantitative peptidomics. It accelerates the identification of peptide peak pairs, overcoming a key bottleneck in the field.

Keywords:

PBAN TMAB hugin isotope tag label locust neuropeptide peptide peptidomics pyrokinin

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Quantitative Analysis of Chromatin Proteomes in Disease

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Published on: December 28, 2012

Area of Science:

Proteomics
Bioinformatics
Analytical Chemistry

Background:

Quantitative peptidomics using stable isotope labeling offers significant advantages for biological research.
A major challenge in this field is the time-consuming identification of corresponding labeled peptide peaks in complex LC-MS data.
Existing methods often present a bottleneck, hindering rapid data analysis.

Purpose of the Study:

To develop a fast and user-friendly R-package for the analysis of labeled peptide liquid chromatography-mass spectrometry (LC-MS) spectra.
To provide a robust framework for statistical inference in quantitative peptidomics data.
To streamline the identification of matching peptide peak pairs.

Main Methods:

Development of an open-source R-package named 'labelpepmatch'.
Implementation of algorithms for fast and accurate identification of labeled peptide peak pairs from LC-MS spectra.
Integration of statistical inference techniques adapted from other omics disciplines.

Main Results:

The 'labelpepmatch' R-package enables rapid and accurate identification of labeled peptide peak pairs.
The tool provides a statistical framework for quantitative analysis of peptidomics data.
A case study involving the desert locust (Schistocerca gregaria) demonstrated the pipeline's reliability for exploratory analyses.

Conclusions:

'labelpepmatch' effectively addresses the bottleneck of peptide peak pair identification in quantitative peptidomics.
The R-package offers a reliable and efficient solution for exploratory data analysis in the field.
This tool facilitates quicker and more thorough investigations in quantitative peptidomics research.