Antibody avidity and multi-specificity combined to confer protection against SARS-CoV-2 and resilience against viral escape
Bibliographic record
Abstract
Abstract SARS-CoV-2, the causative agent of COVID-19, has been responsible for a global pandemic. Monoclonal antibodies have been used as antiviral therapeutics, but have been limited in efficacy by viral sequence variability in emerging variants of concern (VOCs), and in deployment by the need for high doses. In this study, we leverage the MULTI-specific, multi-Affinity antiBODY (Multabody, MB) platform, derived from the human apoferritin protomer, to drive the multimerization of antibody fragments and generate exceptionally potent and broad SARS-CoV-2 neutralizers. CryoEM revealed a high degree of homogeneity for the core of these engineered antibody-like molecules at 2.1 Å resolution. We demonstrate that neutralization potency improvements of the MB over corresponding IgGs translates into superior in vivo protection: in the SARS-CoV-2 mouse challenge model, comparable in vivo protection was achieved for the MB delivered at 30x lower dose compared to the corresponding IgGs. Furthermore, we show how MBs potently neutralize SARS-CoV-2 VOCs by leveraging augmented avidity, even when corresponding IgGs lose their ability to neutralize potently. Multiple mAb specificities could also be combined into a single MB molecule to expand the neutralization breadth beyond SARS-CoV-2 to other sarbecoviruses. Our work demonstrates how avidity and multi-specificity combined can be leveraged to confer protection and resilience against viral diversity that exceeds that of traditional monoclonal antibody therapies.
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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 | 0.000 |
| 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".