Synthetic Antibodies with a Human Framework That Protect Mice from Lethal Sudan Ebolavirus Challenge
Bibliographic record
Abstract
The ebolaviruses cause severe and rapidly progressing hemorrhagic fever. There are five ebolavirus species; although much is known about Zaire ebolavirus (EBOV) and its neutralization by antibodies, little is known about Sudan ebolavirus (SUDV), which is emerging with increasing frequency. Here we describe monoclonal antibodies containing a human framework that potently inhibit infection by SUDV and protect mice from lethal challenge. The murine antibody 16F6, which binds the SUDV envelope glycoprotein (GP), served as the starting point for design. Sequence and structural alignment revealed similarities between 16F6 and YADS1, a synthetic antibody with a humanized scaffold. A focused phage library was constructed and screened to impart 16F6-like recognition properties onto the YADS1 scaffold. A panel of 17 antibodies were characterized and found to have a range of neutralization potentials against a pseudotype virus infection model. Neutralization correlated with GP binding as determined by ELISA. Two of these clones, E10 and F4, potently inhibited authentic SUDV and conferred protection and memory immunity in mice from lethal SUDV challenge. E10 and F4 were further shown to bind to the same epitope on GP as 16F6 with comparable affinities. These antibodies represent strong immunotherapeutic candidates for treatment of SUDV infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".