Crystallization of Feline Coronavirus Mpro With GC376 Reveals Mechanism of Inhibition
Bibliographic record
Abstract
Coronaviruses infect a variety of hosts in the animal kingdom, and while each virus is taxonomically different, they all infect their host via the same mechanism. The coronavirus main protease (Mpro, also called 3CLpro), is an attractive target for drug development due to its essential role in mediating viral replication and transcription. An Mpro inhibitor, GC376, has been shown to treat feline infectious peritonitis (FIP), a fatal infection in cats caused by internal mutations in the feline enteric coronavirus (FECV). Recently, our lab demonstrated that the feline drug, GC373, and prodrug, GC376, are potent inhibitors of SARS-CoV-2 Mpro and solved the structures in complex with the drugs; however, no crystal structures of the FIP virus (FIPV) Mpro with the feline drugs have been published so far. Here, we present crystal structures of FIPV Mpro-GC373/GC376 complexes, revealing the inhibitors covalently bound to Cys144 in the active site, similar to SARS-CoV-2 Mpro. Additionally, GC376 has a higher affinity for FIPV Mpro with lower nanomolar Ki values compared to SARS-CoV and SARS-CoV-2 Mpro. We also show that improved derivatives of GC376 have higher potency for FIPV Mpro. Since GC373 and GC376 represent strong starting points for structure-guided drug design, determining the crystal structures of FIPV Mpro with these inhibitors are important steps in drug optimization and structure-based broad-spectrum antiviral drug discovery.
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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 | 0.001 |
| 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".