Method for Investigation of Oligosaccharides from Glycopeptides: Direct Determination of Glycosylation Sites in Proteins
Bibliographic record
Abstract
Characterization of glycopeptides has become an important tool toward a better understanding of the molecular details in carbohydrate-protein interactions. In this approach, oligosaccharides are commonly not detectable under mass spectrometric conditions because of ionization suppression by deglycosylated peptides. Their composition is only deduced from the mass differences between glycopeptides and corresponding deglycosylated peptides. Here, we describe how carbohydrates can be easily detected in the PNGase-treated samples and structurally investigated next to the peptides. The efficacy of this method is demonstrated through the analysis of tryptic glycopeptides obtained from human IgG. Following deglycosylation with PNGaseF and derivatization with phenylhydrazine, MALDI spectra produced ion peaks of labeled oligosaccharides and deglycosylated peptides. The relative abundances of individual oligosaccharides were consistent with those of the glycopeptides. MALDI-MS/MS provided useful data for the structural elucidation of oligosaccharides, including the assignment of dominant isomers and glycosylation sites in peptides. MALDI-MS/MS fragmentation patterns of deglycosylated peptide ions indicated glycosylation sites at asparagine 297 and 299. The observed peptide of the composition ADQTVYR, described for the first time in this study, indicated new glycosylation sites in IgG1 human myeloma plasma.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".