Two Synthetic Antibodies that Recognize and Neutralize Distinct Proteolytic Forms of the Ebola Virus Envelope Glycoprotein
Bibliographic record
Abstract
Ebola virus (EBOV) is a highly pathogenic member of the Filoviridae family of viruses that causes severe hemorrhagic fever. Infection proceeds through fusion of the host cell and viral membranes, a process that is mediated by the viral envelope glycoprotein (GP). Following endosomal uptake, a key step in viral entry is the proteolytic cleavage of GP by host endosomal cysteine proteases. Cleavage exposes a binding site for the host cell receptor Niemann-Pick C1 (NPC1) and may induce conformational changes in GP leading to membrane fusion. However, the precise details of the structural changes in GP associated with proteolysis and the role of these changes in viral entry have not been established. Here, we have employed synthetic antibody technology to identify antibodies targeting EBOV GP prior to and following proteolysis (i.e. in the "uncleaved" [GP(UNCL)] and "cleaved" [GP(CL)] forms). We identified antibodies with distinct recognition profiles: Fab(CL) bound preferentially to GP(CL) (EC(50)=1.7 nM), whereas Fab(UNCL) bound specifically to GP(UNCL) (EC(50)=75 nM). Neutralization assays with GP-containing pseudotyped viruses indicated that these antibodies inhibited GP(CL)- or GP(UNCL)-mediated viral entry with specificity matching their recognition profiles (IC(50): 87 nM for IgG(CL); 1 μM for Fab(UNCL)). Competition ELISAs indicate that Fab(CL) binds an epitope distinct from that of KZ52, a well-characterized EBOV GP antibody, and from that of the luminal domain of NPC1. The binding epitope of Fab(UNCL) was also distinct from that of KZ52, suggesting that Fab(UNCL) binds a novel neutralization epitope on GP(UNCL). Furthermore, the neutralizing ability of Fab(CL) suggests that there are targets on GP(CL) available for neutralization. This work showcases the applicability of synthetic antibody technology to the study of viral membrane fusion, and provides new tools for dissecting intermediates of EBOV entry.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".