Blocking eIF4E-eIF4G Interaction as a Strategy To Impair Coronavirus Replication
Bibliographic record
Abstract
Coronaviruses are a family of enveloped single-stranded positive-sense RNA viruses causing respiratory, enteric, and neurologic diseases in mammals and fowl. Human coronaviruses are recognized to cause up to a third of common colds and are suspected to be involved in enteric and neurologic diseases. Coronavirus replication involves the generation of nested subgenomic mRNAs (sgmRNAs) with a common capped 5' leader sequence. The translation of most of the sgmRNAs is thought to be cap dependent and displays a requirement for eukaryotic initiation factor 4F (eIF4F), a heterotrimeric complex needed for the recruitment of 40S ribosomes. We recently reported on an ultrahigh-throughput screen to discover compounds that inhibit eIF4F activity by blocking the interaction of two of its subunits (R. Cencic et al., Proc. Natl. Acad. Sci. U. S. A. 108:1046-1051, 2011). Herein we describe a molecule from this screen that prevents the interaction between eIF4E (the cap-binding protein) and eIF4G (a large scaffolding protein), inhibiting cap-dependent translation. This inhibitor significantly decreased human coronavirus 229E (HCoV-229E) replication, reducing the percentage of infected cells and intra- and extracellular infectious virus titers. Our results support the strategy of targeting the eIF4F complex to block coronavirus infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".