N-Terminal finger stabilizes the reversible feline drug GC376 in SARS-CoV-2 M <sup>pro</sup>
Bibliographic record
Abstract
Abstract The main protease (M pro , also known as 3CL protease) of SARS-CoV-2 is a high priority drug target in the development of antivirals to combat COVID-19 infections. A feline coronavirus antiviral drug, GC376, has been shown to be effective in inhibiting the SARS-CoV-2 main protease and live virus growth. As this drug moves into clinical trials, further characterization of GC376 with the main protease of coronaviruses is required to gain insight into the drug’s properties, such as reversibility and broad specificity. Reversibility is an important factor for therapeutic proteolytic inhibitors to prevent toxicity due to off-target effects. Here we demonstrate that GC376 has nanomolar K i values with the M pro from both SARS-CoV-2 and SARS-CoV strains. Restoring enzymatic activity after inhibition by GC376 demonstrates reversible binding with both proteases. In addition, the stability and thermodynamic parameters of both proteases were studied to shed light on physical chemical properties of these viral enzymes, revealing higher stability for SARS-CoV-2 M pro . The comparison of a new X-ray crystal structure of M pro from SARS-CoV complexed with GC376 reveals similar molecular mechanism of inhibition compared to SARS-CoV-2 M pro , and gives insight into the broad specificity properties of this drug. In both structures, we observe domain swapping of the N-termini in the dimer of the M pro , which facilitates coordination of the drug’s P1 position. These results validate that GC376 is a drug with an off-rate suitable for clinical trials.
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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".