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Related Concept Videos

Proteomics01:33

Proteomics

10.2K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
10.2K

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SuperQuant: A Data Processing Approach to Increase Quantitative Proteome Coverage.

Vladimir Gorshkov1, Thiago Verano-Braga1, Frank Kjeldsen1

  • 1Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark.

Analytical Chemistry
|May 16, 2015
PubMed
Summary
This summary is machine-generated.

SuperQuant improves quantitative proteomics by identifying more peptides and proteins using complementary fragment ions. This novel approach enhances data analysis for shotgun proteomics, increasing identification rates significantly.

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Computational Biology

Background:

  • Quantitative proteomics is crucial for understanding biological systems.
  • Accurate identification and quantification of peptides are essential for reliable proteomic analysis.
  • Current methods may have limitations in identifying all co-isolated peptides in complex samples.

Purpose of the Study:

  • To develop and implement SuperQuant, a novel quantitative proteomics data processing approach.
  • To leverage complementary fragment ions for enhanced peptide identification and quantification.
  • To improve the performance of shotgun proteomics data analysis using high mass accuracy.

Main Methods:

  • SuperQuant utilizes complementary fragment ions for peptide identification in tandem mass spectra.
  • The approach was implemented as a processing node in Thermo Proteome Discoverer 2.x.
  • Performance was evaluated using dimethyl-labeled HeLa lysate samples with varying isolation windows and resolutions.

Main Results:

  • SuperQuant identified significantly more peptide-spectrum matches (PSMs), peptides, and proteins compared to conventional high-resolution and ion trap-based methods.
  • Up to 70% more PSMs, 40% more peptides, and 20% more proteins were identified at a 0.01 false discovery rate (FDR).
  • Quantification accuracy was maintained, with coefficients of variation comparable to existing methods.

Conclusions:

  • SuperQuant offers a substantial improvement in peptide and protein identification for quantitative proteomics.
  • The approach is applicable to any shotgun proteomics dataset with high mass accuracy.
  • SuperQuant enhances the depth and reliability of proteomic analyses without compromising quantification accuracy.