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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: Jul 2, 2026

Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling

Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling

Published on: July 1, 2014

Computational quantification of peptides from LC-MS data.

Ole Schulz-Trieglaff¹, Rene Hussong, Clemens Gröpl

¹International Max Planck Research School for Computational Biology and Scientific Computing, Department of Mathematics and Computer Science, Free University Berlin, Berlin, Germany. trieglaf@inf.fu-berlin.de

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|August 19, 2008

Summary

This summary is machine-generated.

A new algorithm rapidly detects and quantifies peptides in liquid chromatography-mass spectrometry (LC-MS) data. This computational tool processes large datasets, providing essential compound information for biological research.

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Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics

Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics

Published on: October 13, 2020

Area of Science:

Proteomics
Analytical Chemistry
Computational Biology

Background:

Liquid chromatography-mass spectrometry (LC-MS) is crucial for biological studies.
High-throughput LC-MS generates vast datasets, overwhelming manual analysis.
Automated computational tools are needed for rapid LC-MS data interpretation.

Purpose of the Study:

To develop an algorithm for efficient peptide detection and quantification in LC-MS data.
To create a flexible tool applicable across various mass spectrometry technologies.
To address the need for rapid data processing in high-throughput LC-MS experiments.

Main Methods:

Algorithm combines the sweep line paradigm with a novel wavelet function for isotopic pattern detection.
A voting schema utilizes redundant scan data for accurate monoisotopic mass and charge state determination.
Instrument inaccuracy is modeled to handle diverse data quality and resolution.

Main Results:

The algorithm rapidly estimates peptide mass, retention time centroid, and abundance.
Demonstrated effectiveness on data from multiple instruments.
Performance comparable to existing techniques across varying data complexities.

Conclusions:

The developed algorithm offers a sound framework for peptide analysis in LC-MS data.
Provides a rapid and accurate solution for high-throughput proteomics.
Enhances the utility of LC-MS in biological process studies.