Structural basis for neutralizing antibody binding to pertussis toxin
Bibliographic record
Abstract
Pertussis toxin (PT) is a key protective antigen in vaccine- and natural immunity-mediated protection from Bordetella pertussis infection. Despite its importance, no PT-neutralizing epitopes have been characterized structurally. To define neutralizing epitopes and identify key structural elements to preserve during PT antigen design, we determined a 3.6 Å cryoelectron microscopy structure of genetically detoxified PT (PTg) bound to hu11E6 and hu1B7, two potently neutralizing anti-PT antibodies with complementary mechanisms: disruption of toxin adhesion to cells and intracellular activities, respectively. Hu11E6 binds the paralogous S2 and S3 subunits of PTg via a conserved epitope but surprisingly did not span the previously identified sialic acid–binding site implicated in toxin adhesion. Hu11E6 specifically prevented PTg binding to sialylated N-glycans and a sialylated model receptor, as demonstrated by high-throughput glycan array analysis and ELISA, while a T cell activation assay showed that it blocks PTg mitogenic activities to define its neutralizing mechanism. Hu1B7 bound a quaternary epitope spanning the S1 and S5 subunits, although functional studies of hu1B7 variants suggested that S5 binding is not involved in its PT neutralization mechanism. These results structurally define neutralizing epitopes on PT, improving our molecular understanding of immune protection from B. pertussis and providing key information for the future development of PT immunogens.
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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) | 0.001 | 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".