Synthetic antibodies neutralize SARS-CoV-2 infection of mammalian cells
Bibliographic record
Abstract
ABSTRACT Coronaviruses (CoV) are a large family of enveloped, RNA viruses that circulate in mammals and birds. Three highly pathogenic strains have caused zoonotic infections in humans that result in severe respiratory syndromes including the Middle East Respiratory Syndrome CoV (MERS), Severe Acute Respiratory Syndrome CoV (SARS), and the ongoing Coronavirus Disease 2019 (COVID-19) pandemic. Here, we describe a panel of synthetic monoclonal antibodies, built on a human IgG framework, that bind to the spike protein of SARS-CoV-2 (the causative agent of COVID-19), compete for ACE2 binding, and potently inhibit SARS-CoV-2. All antibodies that exhibited neutralization potencies at sub-nanomolar concentrations against SARS-CoV-2/USA/WA1 in Vero E6 cells, also bound to the receptor binding domain (RBD), suggesting competition for the host receptor ACE2. These antibodies represent strong immunotherapeutic candidates for treatment of COVID-19.
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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) | 0.002 | 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".