Cooperativity Enables Non-neutralizing Antibodies to Neutralize Ebolavirus
Bibliographic record
Abstract
Drug combinations are synergistic when their combined efficacy exceeds the sum of the individual actions, but they rarely include ineffective drugs that become effective only in combination. We identified several "enabling pairs" of neutralizing and non-neutralizing anti-ebolavirus monoclonal antibodies, whose combination exhibited new functional profiles, including transforming a non-neutralizing antibody to a neutralizer. Sub-neutralizing concentrations of antibodies 2G4 or m8C4 enabled non-neutralizing antibody FVM09 (IC 50 >1 μM) to exhibit potent neutralization (IC 50 1–10 nM). While FVM09 or m8C4 alone failed to protect Ebola-virus-infected mice, a combination of the two antibodies provided 100% protection. Furthermore, non-neutralizers FVM09 and FVM02 exponentially enhanced the potency of two neutralizing antibodies against both Ebola and Sudan viruses. We identified a hotspot for the binding of these enabling antibody pairs near the interface of the glycan cap and GP2. Enabling cooperativity may be an underappreciated phenomenon for viruses, with implications for the design and development of immunotherapeutics and vaccines.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".