Bispecific antibodies against the hepatitis C virus E1E2 envelope glycoprotein
Bibliographic record
Abstract
Abstract Hepatitis C virus (HCV) currently causes about one million infections and 240,000 deaths worldwide each year. To reach the goal set by the World Health Organization (WHO) of global HCV elimination by 2030, it is critical to develop a prophylactic vaccine. Broadly neutralizing antibodies (bNAbs) target the E1E2 envelope glycoproteins on the viral surface, can neutralize a broad range of the highly diverse circulating HCV strains and are essential tools to inform vaccine design. However, bNAbs targeting a single E1E2 epitope might be limited in neutralization breadth, which can be enhanced by using combinations of bNAbs that target different envelope epitopes. We have generated 60 IgG-like bispecific antibodies (bsAbs) that can simultaneously target two distinct epitopes on E1E2. We combine non-overlapping E1E2 specificities into three types of bsAbs, each containing a different hinge length. The bsAbs show retained or increased potency and breadth against a diverse panel of HCV pseudoparticles (HCVpp) and HCV produced in cell culture (HCVcc) compared to monospecific and cocktail controls. Additionally, we demonstrate that changes in the hinge length of bsAbs can alter the binding stoichiometry to E1E2. These results provide insights into the binding modes and the role of avidity in bivalent targeting of diverse E1E2 epitopes, and suggest structural differences between HCVpp and HCVcc. This study illustrates how potential cooperative effects of HCV bNAbs can be utilized by strategically designing bispecific constructs. These new HCV bsAbs can guide vaccine development and unlock novel therapeutic and prophylactic strategies against HCV and other (flavi)viruses.
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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) | 0.001 | 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) | 0.001 | 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".