High-Throughput Global Peptide Proteomic Analysis by Combining Stable Isotope Amino Acid Labeling and Data-Dependent Multiplexed-MS/MS
Bibliographic record
Abstract
In this work, we describe the application of a stable isotope amino acid (lysine) labeling in conjunction with data-dependent multiplexed tandem mass spectrometry (MS/MS) to facilitate the characterization and identification of peptides from proteomic (global protein) digests. Lysine auxotrophic yeast was grown in the presence of 13C-labeled or unlabeled lysine and combined after harvesting in equal proportions. Endoproteinase LysC digestion of the cytosolic fraction produced a global proteomic sample, consisting of heavy/light labeled peptide pairs. Then data-dependent multiplexed-MS/MS was applied to simultaneously select and dissociate only labeled peptide ion pairs. The approach allows differentiation between N-terminal (e.g., b-type ions) and C-terminal fragment ions (e.g., y-type ions) in resulting tandem mass spectra, as well as the capability of differentiation between near-isobaric glutamine and lysine residues. We also describe the utility of peptide composition and fragment information to support peptide identifications and examine the potential application of lysine labeling for differential quantitative protein analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".