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Isotope-coded affinity tags for protein quantification.

Christopher M Colangelo1, Kenneth R Williams

  • 1W. M. Keck Foundation Biotechnology Resource Laboratory, Yale University, New Haven, CT, USA.

Methods in Molecular Biology (Clifton, N.J.)
|June 21, 2006
PubMed
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This study introduces an isotope-coded affinity tag technique for comparing protein levels in biological samples. This method enhances protein identification and quantitation in mass spectrometry experiments.

Area of Science:

  • Proteomics
  • Biochemistry
  • Analytical Chemistry

Background:

  • Comparing protein abundance across biological samples is crucial for understanding physiological states.
  • Existing methods face challenges in reducing sample complexity and increasing protein identification.
  • Mass spectrometry is a key technology for protein analysis.

Purpose of the Study:

  • To develop and apply a novel technique for quantitative proteomics.
  • To improve the efficiency and depth of protein identification in complex biological samples.
  • To enable accurate comparison of protein expression levels between different conditions.

Main Methods:

  • Utilizing isotope-coded affinity tags that selectively bind to cysteine residues.
  • Differential isotopic labeling of two biological samples (heavy and light).

Related Experiment Videos

  • Combining, separating via chromatography, and analyzing samples using tandem mass spectrometry.
  • Database searching of mass spectrometry data to identify proteins and quantify ratios.
  • Main Results:

    • Significant reduction in peptide mixture complexity.
    • Increased number of protein sequences identified per experiment.
    • Generation of a comprehensive list of hundreds of proteins with their heavy:light ratios.
    • Enabling relative quantitation of protein abundance.

    Conclusions:

    • The isotope-coded affinity tag technique is effective for quantitative proteomics.
    • This method significantly enhances protein identification and quantitation capabilities.
    • It provides a powerful tool for correlating protein expression changes with physiological states.