Optimizing Identification and Quantitation of <sup>15</sup>N-Labeled Proteins in Comparative Proteomics
Bibliographic record
Abstract
Comparative proteomics has emerged as a powerful approach to determine differences in protein abundance between biological samples. The introduction of stable-isotopes as internal standards especially paved the road for quantitative proteomics for comprehensive approaches to accurately determine protein dynamics. Metabolic labeling with (15)N isotopes is applied to an increasing number of organisms, including Drosophila, C. elegans, and rats. However, (15)N-enrichment is often suboptimal (<98%), which may hamper identification and quantitation of proteins. Here, we systematically investigated two independent (15)N-labeled data sets to explore the influence of heavy nitrogen enrichment on the number of identifications as well as on the error in protein quantitation. We show that specifically larger (15)N-labeled peptides are under-represented when compared to their (14)N counterparts and propose a correction method, which significantly increases the number of identifications. In addition, we developed a method that corrects for inaccurate peptide ratios introduced by incomplete (15)N enrichment. This results in improved accuracy and precision of protein quantitation. Altogether, this study provides insight into the process of protein identification and quantitation, and the methods described here can be used to improve both qualitative and quantitative data obtained by labeling with heavy nitrogen with enrichment less than 100%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".