Antibody recognition of a unique tumor-specific glycopeptide antigen
Bibliographic record
Abstract
Aberrant glycosylation and the overexpression of certain carbohydrate moieties is a consistent feature of cancers, and tumor-associated oligosaccharides are actively investigated as targets for immunotherapy. One of the most common aberrations in glycosylation patterns is the presentation of a single O-linked N-acetylgalactosamine on a threonine or serine residue known as the "Tn antigen." Whereas the ubiquitous nature of Tn antigens on cancers has made them a natural focus of vaccine research, such carbohydrate moieties are not always tumor-specific and have been observed on embryonic and nonmalignant adult tissue. Here we report the structural basis of binding of a complex of a monoclonal antibody (237mAb) with a truly tumor-specific glycopeptide containing the Tn antigen. In contrast to glycopeptide-specific antibodies in complex with simple peptides, 237mAb does not recognize a conformational epitope induced in the peptide by sugar substitution. Instead, 237mAb uses a pocket coded by germ-line genes to completely envelope the carbohydrate moiety itself while interacting with the peptide moiety in a shallow groove. Thus, 237mAb achieves its striking tumor specificity, with no observed physiological cross-reactivity to the unglycosylated peptide or the free glycan, by a combination of multiple weak but specific interactions to both the peptide and to the glycan portions of the antigen.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".