Heterotypic interactions drive antibody synergy against a malaria vaccine candidate
Bibliographic record
Abstract
Understanding mechanisms of antibody synergy is important for vaccine design and antibody cocktail development. Examples of synergy between antibodies are well-documented, but the mechanisms underlying these relationships often remain poorly understood. The leading blood-stage malaria vaccine candidate, CyRPA, is essential for invasion of Plasmodium falciparum into human erythrocytes. Here we present a panel of anti-CyRPA monoclonal antibodies that strongly inhibit parasite growth in in vitro assays. Structural studies show that growth-inhibitory antibodies bind epitopes on a single face of CyRPA. We also show that pairs of non-competing inhibitory antibodies have strongly synergistic growth-inhibitory activity. These antibodies bind to neighbouring epitopes on CyRPA and form lateral, heterotypic interactions which slow antibody dissociation. We predict that such heterotypic interactions will be a feature of many immune responses. Immunogens which elicit such synergistic antibody mixtures could increase the potency of vaccine-elicited responses to provide robust and long-lived immunity against challenging disease targets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".