Enrichment and Characterization of Glycopeptide Epitopes from Complex Mixtures
Bibliographic record
Abstract
Antigen posttranslational modifications, including glycosylation, are recognized by the innate and adaptive arms of the immune system. Analytical approaches, including mass spectrometry and allied techniques, have allowed advances in the enrichment and identification of glyco-antigens, particularly T-cell epitopes. Similarly, major advances have been made in the identification, isolation, and detailed characterization of prokaryotic and eukaryotic glycoproteins and glycopeptides. In particular, peptide centric approaches are now capable of enriching low level glycopeptides from highly complex peptide mixtures. Similarly, advanced mass spectrometry methods allow identification of glycopeptides, characterization of glycans, and mapping of modification sites. Herein, we describe methods developed in our laboratory for the broad study of glycopeptides and illustrate how these approaches can be exploited to further our understanding of the identity and nature of glycopeptide epitopes in various diseases or auto immune disorders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".