Structures of protective antibodies reveal sites of vulnerability on Ebola virus
Bibliographic record
Abstract
Ebola virus (EBOV) and related filoviruses cause severe hemorrhagic fever, with up to 90% lethality, and no treatments are approved for human use. Multiple recent outbreaks of EBOV and the likelihood of future human exposure highlight the need for pre- and postexposure treatments. Monoclonal antibody (mAb) cocktails are particularly attractive candidates due to their proven postexposure efficacy in nonhuman primate models of EBOV infection. Two candidate cocktails, MB-003 and ZMAb, have been extensively evaluated in both in vitro and in vivo studies. Recently, these two therapeutics have been combined into a new cocktail named ZMapp, which showed increased efficacy and has been given compassionately to some human patients. Epitope information and mechanism of action are currently unknown for most of the component mAbs. Here we provide single-particle EM reconstructions of every mAb in the ZMapp cocktail, as well as additional antibodies from MB-003 and ZMAb. Our results illuminate key and recurring sites of vulnerability on the EBOV glycoprotein and provide a structural rationale for the efficacy of ZMapp. Interestingly, two of its components recognize overlapping epitopes and compete with each other for binding. Going forward, this work now provides a basis for strategic selection of next-generation antibody cocktails against Ebola and related viruses and a model for predicting the impact of ZMapp on potential escape mutations in ongoing or future Ebola outbreaks.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".