High affinity RNA for mammalian initiation factor 4E interferes with mRNA-cap binding and inhibits translation
Bibliographic record
Abstract
The eukaryotic translation initiation factor 4F (eIF4F) consists of three polypeptides (eIF4A, eIF4G, and eIF4E) and is responsible for recruiting ribosomes to mRNA. eIF4E recognizes the mRNA 5'-cap structure (m7GpppN) and plays a pivotal role in control of translation initiation, which is the rate-limiting step in translation. Overexpression of eIF4E has a dramatic effect on cell growth and leads to oncogenic transformation. Therefore, an inhibitory agent to eIF4E, if any, might serve as a novel therapeutic against malignancies that are caused by aberrant translational control. Along these lines, we developed two RNA aptamers, aptamer 1 and aptamer 2, with high affinity for mammalian eIF4E by in vitro RNA selection-amplification. Aptamer 1 inhibits the cap binding to eIF4E more efficiently than the cap analog m7GpppN or aptamer 2. Consistently, aptamer 1 inhibits specifically cap-dependent in vitro translation while it does not inhibit cap-independent HCV IRES-directed translation initiation. The interaction between eIF4E and eIF4E-binding protein 1 (4E-BP1), however, was not inhibited by aptamer 1. Aptamer 1 is composed of 86 nucleotides, and the high affinity to eIF4E is affected by deletions at both termini. Moreover, relatively large areas in the aptamer 1 fold are protected by eIF4E as determined by ribonuclease footprinting. These findings indicate that aptamers can achieve high affinity to a specific target protein via global conformational recognition. The genetic mutation and affinity study of variant eIF4E proteins suggests that aptamer 1 binds to eIF4E adjacent to the entrance of the cap-binding slot and blocks the cap-binding pocket, thereby inhibiting translation initiation.
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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".