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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

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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In inductively coupled plasma–mass spectrometry (ICP–MS), an inductively coupled plasma (ICP) torch is used as an atomizer and ionizer. Solid samples are dissolved and volatilized before being introduced into the high-temperature argon plasma, while solution samples are nebulized and passed through the high-temperature argon plasma. Plasma dissociates the analytes and ionizes their component atoms to form a mixture of positive ions and molecular species. The positive ions are then passed on to...

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Related Experiment Video

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The EIPeptiDi tool: enhancing peptide discovery in ICAT-based LC MS/MS experiments.

Mario Cannataro1, Giovanni Cuda, Marco Gaspari

  • 1Bioinformatics Laboratory, Experimental and Clinical Medicine Department, Magna Graecia University, Catanzaro, Italy. cannataro@unicz.it <cannataro@unicz.it>

BMC Bioinformatics
|July 17, 2007
PubMed
Summary

This study introduces EIPeptiDi, a new tool that enhances peptide identification in quantitative proteomics. EIPeptiDi improves the reproducibility of isotope-coded affinity tags (ICAT) and mass spectrometry (MS) analysis, aiding clinical studies.

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biotechnology

Background:

  • Isotope-coded affinity tags (ICAT) is a quantitative proteomics method using differential isotopic labeling and mass spectrometry (MS).
  • The ICAT LC-MS/MS method identifies and quantifies proteins in two samples by labeling cysteine residues and analyzing peptides.
  • Current limitations include insufficient reproducibility in peptide identification, challenging comparative analysis, and low information overlap.

Purpose of the Study:

  • To develop a method for improving data processing and peptide identification in ICAT labeling and LC-MS/MS analysis.
  • To enhance the accuracy and comprehensiveness of quantitative proteomics data.
  • To address the challenges of sample-to-sample variability and low information overlap in complex proteomic datasets.

Main Methods:

  • A novel method was designed for cross-validating MS/MS results to improve peptide identification.
  • The method was implemented in a software tool named EIPeptiDi.
  • EIPeptiDi assigns unassigned Heavy/Light (H/L) pairs to identified peptide sequences using similarity criteria (retention time, mass attributes).

Main Results:

  • EIPeptiDi significantly increases the number of identified peptides per sample.
  • The tool improves peptide identification across the entire dataset by boosting existing ICAT data analysis software.
  • This leads to a considerable impact on protein identification and the amount of critical information available for clinical studies.

Conclusions:

  • EIPeptiDi substantially increases the number of identified and quantified peptides in analyzed samples.
  • The tool effectively reduces the number of unassigned H/L pairs.
  • This facilitates better comparative analysis of sample datasets, enhancing the utility of ICAT-based quantitative proteomics.