Potent broad-spectrum anti-coronaviral frameshift inhibitors from virtual screen of RNA binding
Bibliographic record
Abstract
Coronavirus genomes contain an RNA pseudoknot that directs −1 programmed ribosomal frameshifting (−1 PRF) to control expression of viral proteins crucial for replication. Ligands that inhibit −1 PRF can thus attenuate viral propagation and have potential as drugs for limiting coronavirus infections. To search for novel small-molecule frameshift inhibitors with anticoronaviral activity, we computationally screened over 14 million compounds for binding to the SARS-CoV-2 pseudoknot, followed by experimental validation of the top hits for inhibition of −1 PRF and viral replication. We identified multiple potent −1 PRF inhibitors, effective at nM concentrations, some of which significantly suppressed SARS-CoV-2 replication in cell culture. Several compounds also inhibited −1 PRF in multiple representative bat coronaviruses, indicating broad-spectrum activity. These results showcase the promise of viral RNA structures like frameshift-stimulatory pseudoknots as targets for broad-spectrum antiviral drugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".