A potent synthetic nanobody targets RBD and protects mice from SARS-CoV-2 infection
Bibliographic record
Abstract
ABSTRACT SARS-CoV-2, the causative agent of COVID-19 1 , recognizes host cells by attaching its receptor-binding domain (RBD) to the host receptor ACE2 2–7 . Neutralizing antibodies that block RBD-ACE2 interaction have been a major focus for therapeutic development 8–18 . Llama-derived single-domain antibodies (nanobodies, ∼15 kDa) offer advantages including ease of production and possibility for direct delivery to the lungs by nebulization 19 , which are attractive features for bio-drugs against the global respiratory disease. Here, we generated 99 synthetic nanobodies (sybodies) by in vitro selection using three libraries. The best sybody, MR3 bound to RBD with high affinity ( K D = 1.0 nM) and showed high neutralization activity against SARS-CoV-2 pseudoviruses (IC 50 = 0.40 μg mL −1 ). Structural, biochemical, and biological characterization of sybodies suggest a common neutralizing mechanism, in which the RBD-ACE2 interaction is competitively inhibited by sybodies. Various forms of sybodies with improved potency were generated by structure-based design, biparatopic construction, and divalent engineering. Among these, a divalent MR3 conjugated with the albumin-binding domain for prolonged half-life displayed highest potency (IC 50 = 12 ng mL −1 ) and protected mice from live SARS-CoV-2 challenge. Our results pave the way to the development of therapeutic nanobodies against COVID-19 and present a strategy for rapid responses for future outbreaks.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".