Quantitative analysis of a proteome by N‐terminal stable‐isotope labelling of tryptic peptides
Bibliographic record
Abstract
Covalent modification of peptides and proteins with compounds containing stable isotopes (isotope tagging) has become an essential tool to detect dynamic changes in the proteome following external or internal influence; however, using terminal amino groups for global isotope labelling of tryptic peptides is challenged by the similar reactivity of the amino groups of lysine residues. We describe a new quantitative method based on selective tagging of the terminal amino groups of tryptic peptides with pentafluorophenyl esters containing stable isotopes. The labelled peptides were resolved by two-dimensional nanoflow liquid chromatography on weak anion-exchange and reversed-phase columns and then identified and quantified by tandem mass spectrometry. The method was applied to compare the proteomes of plasma membranes from proliferating and differentiated human colorectal adenocarcinoma (Caco-2) cells and endosomes purified from the livers of rats stimulated with insulin and epidermal growth factor. The comparison of the results obtained by isotope tagging and biochemical assays demonstrate that global isotope tagging with pentafluorophenyl esters allows accurate quantification of complex protein samples.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".