Fractionation of Isotopically Labeled Peptides in Quantitative Proteomics
Bibliographic record
Abstract
The goal of quantitative proteomics is to examine the expression levels of all of the proteins in a biological system and recognize those that change as a function of some stimulus. Quantification is now frequently based on derivatization of peptides with isotopically distinguishable labeling agents. This study examines the extent to which isotopic forms of peptides having the same amino acid sequence are resolved by reversed-phase chromatography and assesses the degree to which resolution of these isotopically different forms of a peptide impact quantification. Three derivatizing agents were examined, the do and d3 forms of N-acetoxysuccinimide, the do and d4 forms of succinic anhydride, and the do and d8 forms of the commercial ICAT reagent Peptide mixtures from control and experimental samples were derivatized individually, mixed, subjected to reversed-phase chromatography, and analyzed by ESI-MS. When partial resolution of the isotopic forms of a peptide occurs, the largest error in assessing the true isotope ratio in a sample occurs when sampling at the extremes of a peak. Early in the elution of a peak, the sample will be enriched in the deuterated species, whereas the opposite is true at the tailing edge of a peak. Acetylated peptides showed the lowest degree of separation. Resolution of the deuterated and nondeuterated forms in this case was 0.023. This amounts to slightly over a 1-s difference in their peak maxima and can cause a typical error of +/- 6% at the leading and tailing edges of a peak. In contrast, resolution of the deuterated and nondeuterated forms of the ICAT reagent were calculated to be 0.45. This means that in a peak of 1-min width (W1/2), the peak maxima will vary by approximately 30 s, and measurement errors of -83 and +500% can occur at the leading and tailing edges of a peak. It is concluded that resolution of isotopic forms of a peptide can cause substantial quantification errors in quantitative proteomics.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".