SS148 and WZ16 inhibit the activities of nsp10-nsp16 complexes from all seven human pathogenic coronaviruses
Bibliographic record
Abstract
Seven coronaviruses have infected humans (HCoVs) to-date. SARS-CoV-2 caused the current COVID-19 pandemic with the well-known high mortality and severe socioeconomic consequences. MERS-CoV and SARS-CoV caused epidemic of MERS and SARS, respectively, with severe respiratory symptoms and significant fatality. However, HCoV-229E, HCoV-NL63, HCoV-HKU1, and HCoV-OC43 cause respiratory illnesses with less severe symptoms in most cases. All coronaviruses use RNA capping to evade the immune systems of humans. Two viral methyltransferases, nsp14 and nsp16, play key roles in RNA capping and are considered valuable targets for development of anti-coronavirus therapeutics. But little is known about the kinetics of nsp10-nsp16 methyltransferase activities of most HCoVs, and reliable assays for screening are not available. Here, we report the expression, purification, and kinetic characterization of nsp10-nsp16 complexes from six HCoVs in parallel with previously characterized SARS-CoV-2. Probing the active sites of all seven by SS148 and WZ16, the two recently reported dual nsp14 / nsp10-nsp16 inhibitors, revealed pan-inhibition. Overall, our study show feasibility of developing broad-spectrum dual nsp14 / nsp10-nsp16-inhibitor therapeutics.
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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".