RNA-tethering assay and eIF4G:eIF4A obligate dimer design uncovers multiple eIF4F functional complexes
Bibliographic record
Abstract
Eukaryotic cellular mRNAs possess a 5' cap structure (m7GpppN) which plays a critical role in translation initiation mediated by eukaryotic initiation factor (eIF) 4F. The heterotrimeric eIF4F complex possesses several activities imparted by its subunits that include cap recognition (by eIF4E), RNA unwinding (eIF4A), and factor/ribosome recruitment (eIF4G). Mammalian cells have paralogs of all three eIF4F subunits and it remains an open question as to whether these all can participate in the process of ribosome recruitment. To query the activities of the eIF4F subunits in translation initiation, we adopted an RNA-tethering assay in which select subunits are recruited to a specific address on a reporter mRNA template. We find that all eIF4F subunits can participate in the initiation process. Based on eIF4G:eIF4A structural information, we also designed obligate dimer pairs to probe the activity of all combinations of eIF4G and eIF4A paralogs. We demonstrate that both eIF4GI and eIF4GII can associate with either eIF4A1 or eIF4A2 to recruit ribosomes to mRNA templates. In combination with eIF4E and eIF4E3, our results indicate the presence of up to eight eIF4F complexes that can operate in translation initiation.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".