A molecularly engineered, broad-spectrum anti-coronavirus lectin inhibits SARS-CoV-2 and MERS-CoV infection in vivo
Bibliographic record
Abstract
"Pan-coronavirus" antivirals targeting conserved viral components can be designed. Here, we show that the rationally engineered H84T-banana lectin (H84T-BanLec), which specifically recognizes high mannose found on viral proteins but seldom on healthy human cells, potently inhibits Middle East respiratory syndrome coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (including Omicron), and other human-pathogenic coronaviruses at nanomolar concentrations. H84T-BanLec protects against MERS-CoV and SARS-CoV-2 infection in vivo. Importantly, intranasally and intraperitoneally administered H84T-BanLec are comparably effective. Mechanistic assays show that H84T-BanLec targets virus entry. High-speed atomic force microscopy depicts real-time multimolecular associations of H84T-BanLec dimers with the SARS-CoV-2 spike trimer. Single-molecule force spectroscopy demonstrates binding of H84T-BanLec to multiple SARS-CoV-2 spike mannose sites with high affinity and that H84T-BanLec competes with SARS-CoV-2 spike for binding to cellular ACE2. Modeling experiments identify distinct high-mannose glycans in spike recognized by H84T-BanLec. The multiple H84T-BanLec binding sites on spike likely account for the drug compound's broad-spectrum antiviral activity and the lack of resistant mutants.
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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) | 0.000 | 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 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".