Remarkable Structural Diversity of <i>N</i>-Glycan Sulfation on Influenza Vaccines
Bibliographic record
Abstract
Sulfated N-glycans are biologically important structures derived from enzymatically post-glycosylational modifications of glycoproteins in many therapeutic biologics. The high-throughput analysis of sulfated N-glycomes remains a daunting technical challenge, because of negatively charged heterogeneous composition, large molecular structures, lability of sulfate attachments, and a lack of highly selective enrichment methods. Using liquid chromatography-mass spectrometry, we have analyzed the N-glycans of influenza viral hemagglutinin and neuraminidase from several subtypes of influenza vaccines, and utilized the existing resource to establish an N-glycan library consisting of 927 N-glycan structures and 387 sulfated N-glycan compositions. With the aid of database for data mining, 1380 unique N-glycopeptides were identified and manually validated by de novo glycopeptide sequencing, of which 514 were sulfated at the site-specific locations. We report here a mass spectrometric method that is able to identify and distinguish the isobaric structures of complex and hybrid N-glycans flanked by a terminal sulfation sequon on Gal-GlcNAc and GalNAc-GlcNAc of sulfated-3-Gal, sulfated-6-GlcNAc, and sulfated-4-GalNAc. The database-aided glycoproteomic analyses enable rapid determination of new sulfated-N-glycan structures in large sets of influenza vaccines, including those highly branched nonsialyl sulfo-N-glycans bearing lactosaminic extensions in both complex and hybrid N-glycans that especially interact with sulfotransferases. The novel findings highlight the tremendous structural diversity of sulfated N-glycans and strongly suggest potential functional importance of N-glycan sulfation of influenza glycoproteins.
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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 | 0.000 |
| 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".