Inhibition of coronavirus HCoV-OC43 by targeting the eIF4F complex
Bibliographic record
Abstract
The translation initiation complex 4F (eIF4F) is a rate-limiting factor in protein synthesis. Alterations in eIF4F activity are linked to several diseases, including cancer and infectious diseases. To this end, coronaviruses require eIF4F complex activity to produce proteins essential for their life cycle. Efforts to target coronaviruses by abrogating translation have been largely limited to repurposing existing eIF4F complex inhibitors. Here, we report the results of a high throughput screen to identify small molecules that disrupt eIF4F complex formation and inhibit coronavirus RNA and protein levels. Of 338,000 small molecules screened for inhibition of the eIF4F-driven, CAP-dependent translation, we identified SBI-1232 and two structurally related analogs, SBI-5844 and SBI-0498, that inhibit human coronavirus OC43 (HCoV-OC43; OC43) with minimal cell toxicity. Notably, gene expression changes after OC43 infection of Vero E6 or A549 cells were effectively reverted upon treatment with SBI-5844 or SBI-0498. Moreover, SBI-5844 or SBI-0498 treatment effectively impeded the eIF4F complex assembly, with concomitant inhibition of newly synthesized OC43 nucleocapsid protein and OC43 RNA and protein levels. Overall, we identify SBI-5844 and SBI-0498 as small molecules targeting the eIF4F complex that may limit coronavirus transcripts and proteins, thereby representing a basis for developing novel therapeutic modalities against coronaviruses.
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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".