Molecular Recognition of Candida albicans (1→2)-β-Mannan Oligosaccharides by a Protective Monoclonal Antibody Reveals the Immunodominance of Internal Saccharide Residues
Bibliographic record
Abstract
A self-consistent model of β-mannan oligosaccharides bound to a monoclonal antibody, C3.1, that protects mice against Candida albicans has been developed through chemical mapping, NMR spectroscopic, and computational studies. This antibody optimally binds di- and trisaccharide epitopes, whereas larger oligomers bind with affinities that markedly decrease with increasing chain length. The (1→2)-β-linked di-, tri-, and tetramannosides bind in helical conformations similar to the solution global minimum. Antibody recognition of the di- and trisaccharide is primarily dependent on the mannose unit at the reducing end, with the hydrophobic face of this sugar being tightly bound. Recognition of a tetrasaccharide involves a frameshift in the ligand interaction, shown by strong binding of the sugar adjacent to the reducing end. We show that frameshifting may also be deliberately induced by chemical modifications. Molecular recognition patterns similar to that of mAb C3.1, determined by saturation transfer difference-NMR, were also observed in polyclonal sera from rabbits immunized with a trisaccharide glycoconjugate. The latter observation points to the importance of internal residues as immunodominant epitopes in (1→2)-β-mannans and to the viability of a glycoconjugate vaccine composed of a minimal length oligosaccharide hapten.
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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) | 0.001 | 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".