Quantitative Analysis of the Yeast Proteome by Incorporation of Isotopically Labeled Leucine
Bibliographic record
Abstract
Quantitative comparison of protein expression levels in 2D gels is complicated by the variables associated with protein separation and mass spectrometric responses. Metabolic labeling allows cells from different experiments to be mixed prior to analysis. This approach has been reported for prokaryotic cells. Here, we demonstrate that metabolic labeling can also be successfully applied to the eukaryote Saccharormyces cerevisiae. Yeast leucine auxotrophs grown on synthetic complete media containing natural abundance Leu or D10-Leu were mixed prior to 2D gel separation and MALDI analysis of the digested proteins. D10-Leu labeling provided an effective internal calibrant for peptide MS analysis, and the number of Leu residues yielded an additional parameter for peptide identification at low mass resolution (1000). Metabolic incorporation of D10-Leu into yeast proteins was found to be quantitative since the intensities of the peptide peaks corresponded to those expected on the basis of the percent label in the media. Thus, D10-Leu labeling should provide reliable data for comparing proteomes both quantitatively and qualitatively from wild-type and nonessential-gene-null-mutant strains of S. cerevisiae. Given the central role played by yeast in our understanding of eukaryotic gene and protein expression, it is anticipated that the quantitative expressional proteomic method outlined here will have widespread applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".