Cyanovirin-N binds to select SARS-CoV-2 spike oligosaccharides outside of the receptor binding domain and blocks infection by SARS-CoV-2
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an enveloped positive stranded RNA virus which has caused the recent deadly pandemic called COVID-19. The SARS-CoV-2 virion is coated with a heavily glycosylated Spike glycoprotein which is responsible for attachment and entry into target cells. One, as yet unexploited strategy for preventing SARS-CoV-2 infections, is the targeting of the glycans on Spike. Lectins are carbohydrate-binding proteins produced by plants, algae, and cyanobacteria. Some lectins can neutralize enveloped viruses displaying external glycoproteins, offering an alternative therapeutic approach for the prevention of infection with virulent β-coronaviruses, such as SARS-CoV-2. Here we show that the cyanobacterial lectin cyanovirin-N (CV-N) can selectively target SARS-CoV-2 Spike oligosaccharides and inhibit SARS-CoV-2 infection in vitro and in vivo. CV-N neutralizes Delta and Omicron variants in vitro better than earlier circulating viral variants. CV-N binds selectively to Spike with a Kd as low as 15 nM and a stoichiometry of 2 CV-N: 1 Spike but does not bind to the receptor binding domain (RBD). Further mapping of CV-N binding sites on Spike shows that select high-mannose oligosaccharides in the S1 domain of Spike are targeted by CV-N. CV-N also reduced viral loads in the nares and lungs in vivo to protect hamsters against a lethal viral challenge. In summary, we present an anti-coronavirus agent that works by an unexploited mechanism and prevents infection by a broad range of SARS-CoV-2 strains.
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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 | 0.000 |
| 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".