A molecularly engineered, broad-spectrum anti-coronavirus lectin inhibits SARS-CoV-2 and MERS-CoV infection in vivo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it