eIF4E3 acts as a tumor suppressor by utilizing an atypical mode of methyl-7-guanosine cap recognition
Bibliographic record
Abstract
Recognition of the methyl-7-guanosine (m(7)G) cap structure on mRNA is an essential feature of mRNA metabolism and thus gene expression. Eukaryotic translation initiation factor 4E (eIF4E) promotes translation, mRNA export, proliferation, and oncogenic transformation dependent on this cap-binding activity. eIF4E-cap recognition is mediated via complementary charge interactions of the positively charged m(7)G cap between the negative π-electron clouds from two aromatic residues. Here, we demonstrate that a variant subfamily, eIF4E3, specifically binds the m(7)G cap in the absence of an aromatic sandwich, using instead a different spatial arrangement of residues to provide the necessary electrostatic and van der Waals contacts. Contacts are much more extensive between eIF4E3-cap than other family members. Structural analyses of other cap-binding proteins indicate this recognition mode is atypical. We demonstrate that eIF4E3 relies on this cap-binding activity to act as a tumor suppressor, competing with the growth-promoting functions of eIF4E. In fact, reduced eIF4E3 in high eIF4E cancers suggests that eIF4E3 underlies a clinically relevant inhibitory mechanism that is lost in some malignancies. Taken together, there is more structural plasticity in cap recognition than previously thought, and this is physiologically relevant.
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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) | 0.000 | 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 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".