Online porous graphic carbon chromatography coupled with tandem mass spectrometry for post‐translational modification analysis
Bibliographic record
Abstract
RATIONALE: Porous graphic carbon chromatography (PGC) has a different mechanism in the retention of tryptic peptides compared with reversed-phase chromatography and in this study we show that coupling PGC with tandem mass spectrometry offer advantages for the quantitation of phosphorylation stoichiometry and characterization of site-specific glycosylation. METHODS: Digests of protein standards (horse myoglobin, bovine fetuin and β-casein) were analyzed with a capillary liquid chromatography/tandem mass spectrometry (LC/MS/MS) system by coupling an Agilent 1100 HPLC system to a Synapt G2-Si HDMS (Waters). Peptides were separated using a HyperCarb PGC column (300 μm i.d. × 100 mm) packed with 3 μm particles. MS/MS data were collected in data-dependent mode and three MS/MS scans were acquired after the full MS scan. RAW data were transformed to .mgf by PLGS (Waters) and searched against the Swissprot database by Mascot. Chromatograms and MS/MS spectra of identified compounds were extracted with Masslynx (Waters) and imported to Origin for analysis. Glycan composition and peptide sequence were manually annotated. RESULTS: PGC/MS/MS enabled accurate quantitation of the stoichiometry of specific phosphorylation sites from β-casein by efficient separation of the phosphopeptide and its non-phosphorylated counterpart, which cannot be achieved by reversed-phase chromatography. PGC/MS/MS also enabled comprehensive characterization of protein sialoglycosylation as isomeric glycopeptides with different combinations of α2-3- and α2-6-linked sialic acids can be separated and the ratios of each combination were verified by exoglycosidase digestion. CONCLUSIONS: PGC has demonstrated superior separation of peptides with phosphorylation and glycosylation and can be used as an alternative in the proteomic characterization of post-translational modifications (PTMs) by polar groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.002 |
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".