Induction of cross-neutralizing antibodies by a permuted hepatitis C virus glycoprotein nanoparticle vaccine candidate
Bibliographic record
Abstract
Hepatitis C virus (HCV) infection affects approximately 58 million people and causes ~300,000 deaths yearly. The only target for HCV neutralizing antibodies is the highly sequence diverse E1E2 glycoprotein. Eliciting broadly neutralizing antibodies that recognize conserved cross-neutralizing epitopes is important for an effective HCV vaccine. However, most recombinant HCV glycoprotein vaccines, which usually include only E2, induce only weak neutralizing antibody responses. Here, we describe recombinant soluble E1E2 immunogens that were generated by permutation of the E1 and E2 subunits. We displayed the E2E1 immunogens on two-component nanoparticles and these nanoparticles induce significantly more potent neutralizing antibody responses than E2. Next, we generated mosaic nanoparticles co-displaying six different E2E1 immunogens. These mosaic E2E1 nanoparticles elicit significantly improved neutralization compared to monovalent E2E1 nanoparticles. These results provide a roadmap for the generation of an HCV vaccine that induces potent and broad neutralization.
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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 | 0.001 |
| 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".