A natural product compound inhibits coronaviral replication in vitro by binding to the conserved Nsp9 SARS-CoV-2 protein
Bibliographic record
Abstract
The Nsp9 replicase is a conserved coronaviral protein that acts as an essential accessory component of the multi-subunit viral replication/transcription complex. Nsp9 is the predominant substrate for the essential nucleotidylation activity of Nsp12. Compounds specifically interfering with this viral activity would facilitate its study. Using a native mass-spectrometry-based approach to screen a natural product library for Nsp9 binders, we identified an ent-kaurane natural product, oridonin, capable of binding to purified SARS-CoV-2 Nsp9 with micromolar affinities. By determining the crystal structure of the Nsp9-oridonin complex, we showed that oridonin binds through a conserved site near Nsp9's C-terminal GxxxG-helix. In enzymatic assays, oridonin's binding to Nsp9 reduces its potential to act as substrate for Nsp12's Nidovirus RdRp-Associated Nucleotidyl transferase (NiRAN) domain. We also showed using in vitro cellular assays oridonin, while cytotoxic at higher doses has broad antiviral activity, reducing viral titer following infection with either SARS-CoV-2 or, to a lesser extent, MERS-CoV. Accordingly, these preliminary findings suggest that the oridonin molecular scaffold may have the potential to be developed into an antiviral compound to inhibit the function of Nsp9 during coronaviral replication.
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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".