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Identification of protein complexes with quantitative proteomics in S. cerevisiae
Published on: March 4, 2009
Quantitative proteomic analysis of the budding yeast cell cycle using acid-cleavable isotope-coded affinity tag
Mark R Flory1, Hookeun Lee, Richard Bonneau
1Department of Molecular Biology and Biochemistry, Wesleyan University, Middletown, CT 06459, USA. mflory@wesleyan.edu
Proteomics
|November 30, 2006
Summary
This study presents the first quantitative proteomic measurement of the yeast cell cycle, revealing limited concordance with mRNA data. This proteomic dataset offers a valuable resource for understanding eukaryotic gene expression.
Area of Science:
- Proteomics
- Molecular Biology
- Systems Biology
Background:
- Proteins are key effectors of biological functions, necessitating their quantitative measurement alongside mRNA.
- Advancements in stable isotopic labeling and LC-MS/MS analysis enable systematic proteomic profiling.
- Understanding cell cycle dynamics requires comprehensive protein-level data.
Purpose of the Study:
- To perform the first quantitative, global proteomic measurement of a time-course gene expression experiment.
- To analyze the cell cycle in the model eukaryote Saccharomyces cerevisiae at the proteomic level.
- To provide a comprehensive proteomic dataset for the scientific community.
Main Methods:
- Utilized stable isotopic labeling with isotope-coded affinity tag reagents.
- Employed software tools for high-throughput analysis of LC-MS/MS data.
- Quantitatively profiled proteins across a time-course of the yeast cell cycle, sampling 48% of predicted ORFs.
Main Results:
- Generated a quantitative, global proteomic dataset for the S. cerevisiae cell cycle.
- Observed no significant concordance between proteomic and microarray measurements over the time-course.
- Established a sortable matrix of protein identifications, quantitations, and statistical certainty measures.
Conclusions:
- Proteomic measurements provide a complementary perspective to mRNA data for eukaryotic gene expression.
- The generated dataset serves as a rich resource for functional analysis of S. cerevisiae proteins.
- This work facilitates the development of technologies for global proteomic analysis in higher eukaryotes.
