Dual inhibition of coronavirus M <sup>pro</sup> and PL <sup>pro</sup> enzymes by phenothiazines and their antiviral activity
Bibliographic record
Abstract
ABSTRACT Coronavirus (CoV) replication requires efficient cleavage of viral polyproteins into an array of non-structural proteins involved in viral replication, organelle formation, viral RNA synthesis, and host shutoff. Human CoVs (HCoVs) encode two viral cysteine proteases, main protease (M pro ) and papain-like protease (PL pro ), that mediate polyprotein cleavage. Using a structure-guided approach, a phenothiazine urea derivative that inhibits both SARS-CoV-2 M pro and PL pro protease activity in vitro was identified. In silico docking studies also predicted binding of the phenothiazine to the active sites of M pro and PL pro from distantly related alphacoronavirus, HCoV-229E (229E) and the betacoronavirus, HCoV-OC43 (OC43). The lead phenothiazine urea derivative displayed broad antiviral activity against all three HCoVs tested in cell culture infection models. It was further demonstrated that the compound inhibited 229E and OC43 at an early stage of viral replication, with diminished formation of viral replication organelles and the RNAs that are made within them, as expected following viral protease inhibition. These observations suggest that the phenothiazine urea derivative inhibits viral replication and may broadly inhibit proteases of diverse coronaviruses. Graphical Abstract Highlights Coronavirus cysteine proteases M pro and PL pro are targets for novel antiviral agents Phenothiazine ureas inhibit SARS-CoV-2 M pro and PL pro protease activity Some phenothiazine ureas inhibit replication of diverse coronaviruses with minimal cytotoxicity Phenothiazine ureas inhibit early stages of coronavirus replication consistent with failure of viral polyprotein cleavage
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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) | 0.001 | 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) | 0.003 | 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".