Structural studies of the eIF4E–VPg complex reveal a direct competition for capped RNA: Implications for translation
Bibliographic record
Abstract
Viruses have transformed our understanding of mammalian RNA processing, including facilitating the discovery of the methyl-7-guanosine (m 7 G) cap on the 5′ end of RNAs. The m 7 G cap is required for RNAs to bind the eukaryotic translation initiation factor eIF4E and associate with the translation machinery across plant and animal kingdoms. The potyvirus-derived viral genome-linked protein (VPg) is covalently bound to the 5′ end of viral genomic RNA (gRNA) and associates with host eIF4E for successful infection. Divergent models to explain these observations proposed either an unknown mode of eIF4E engagement or a competition of VPg for the m 7 G cap-binding site. To dissect these possibilities, we resolved the structure of VPg, revealing a previously unknown 3-dimensional (3D) fold, and characterized the VPg–eIF4E complex using NMR and biophysical techniques. VPg directly bound the cap-binding site of eIF4E and competed for m 7 G cap analog binding. In human cells, VPg inhibited eIF4E-dependent RNA export, translation, and oncogenic transformation. Moreover, VPg formed trimeric complexes with eIF4E–eIF4G, eIF4E bound VPg– luciferase RNA conjugates, and these VPg–RNA conjugates were templates for translation. Informatic analyses revealed structural similarities between VPg and the human kinesin EG5. Consistently, EG5 directly bound eIF4E in a similar manner to VPg, demonstrating that this form of engagement is relevant beyond potyviruses. In all, we revealed an unprecedented modality for control and engagement of eIF4E and show that VPg–RNA conjugates functionally engage eIF4E. As such, potyvirus VPg provides a unique model system to interrogate eIF4E.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".