A Gaussia luciferase reporter assay for the evaluation of coronavirus Nsp5/3CLpro activity
Bibliographic record
Abstract
Human coronaviruses (hCoVs) infect millions of people every year. Among these, MERS, SARS-CoV-1, and SARS-CoV-2 caused significant morbidity and mortality and their emergence highlights the risk of possible future coronavirus outbreaks. Therefore, broadly-active anti-coronavirus drugs are needed. Pharmacological inhibition of the hCoV protease Nsp5 (3CLpro) is clinically beneficial as shown by the wide and effective use of Paxlovid (nirmatrelvir, ritonavir). However, further treatment options are required due to the risk of drug resistance. To facilitate the assessment of coronavirus protease function and its pharmacological inhibition, we developed an assay allowing rapid and reliable quantification of Nsp5 activity under biosafety level 1 conditions. It is based on an ACE2-Gal4 transcription factor fusion protein separated by a Nsp5 recognition site. Cleavage by Nsp5 releases the Gal4 transcription factor, which then induces the expression of Gaussia luciferase. Our assay is compatible with Nsp5 proteases from all hCoVs and allows simultaneous measurement of inhibitory and cytotoxic effects of the tested compounds. Proof-of-concept measurements confirmed that nirmatrelvir, GC376 and lopinavir inhibit SARS-CoV-2 Nsp5 function. Furthermore, the assay accurately predicted the impact of Nsp5 mutations on catalytic activity and inhibitor sensitivity. Overall, the reporter assay is suitable for evaluating viral protease activity.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".