N-Glycopeptide Reduction with Exoglycosidases Enables Accurate Characterization of Site-Specific N-Glycosylation
Bibliographic record
Abstract
We report a new approach called NGlycoReduction that generates an oligomannosylated N-glycopeptidome by enzymatically removing sugars outside the N-glycopeptide pentasaccharide core with exoglycosidases. This approach is based on our discovery that the fragmentation of glycopeptides is glycan-structure dependent and glycans with core mannose structures overwhelmingly lead to the generation of Y1 ions when subjected to MS/MS in mass spectrometry. Oligomannosylated glycopeptidome produced by NGlycoReduction can be mixed with the intact N-glycopeptidome and analyzed by HPLC-ESI-MS together to enable the identification of peptide sequence, glycosylation site and the structure of intact glycopeptides. The glycan structure of intact glycopeptides can be identified from MS/MS spectra of their own and their peptide sequences were identified by the MS 3 spectra of the oligomannosylated glycopeptides with the same Y1 ion. Both mass tolerance and difference in retention time were further used to increase the confidence in the Y1 ion alignment. This approach has the advantage of low cost and ease of processing and can be expanded to other samples, especially for characterizing site-specific N-glycosylation involving complex N-glycans. In this study, simultaneous analysis of the combined oligomannosylated N-glycopeptidome and the native glycopeptidome leads to the identification of 609 N-glycopeptides from the secretome and lysates of Huh7 cells.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".