The eukaryotic initiation factor eIF4H facilitates loop-binding, repetitive RNA unwinding by the eIF4A DEAD-box helicase
Bibliographic record
Abstract
Eukaryotic translation initiation is a highly regulated process in protein synthesis. The principal translation initiation factor eIF4AI displays helicase activity, unwinding secondary structures in the mRNAs 5'-UTR. Single molecule fluorescence resonance energy transfer (sm-FRET) is applied here to directly observe and quantify the helicase activity of eIF4AI in the presence of the ancillary RNA-binding factor eIF4H. Results show that eIF4H can significantly enhance the helicase activity of eIF4AI by strongly binding both to loop structures within the RNA transcript as well as to eIF4AI. In the presence of ATP, the eIF4AI/eIF4H complex exhibits persistent rapid and repetitive cycles of unwinding and re-annealing. ATP titration assays suggest that this process consumes a single ATP molecule per cycle. In contrast, helicase unwinding activity does not occur in the presence of the non-hydrolysable analog ATP-γS. Based on our sm-FRET results, we propose an unwinding mechanism where eIF4AI/eIF4H can bind directly to loop structures to destabilize duplexes. Since eIF4AI is the prototypical example of a DEA(D/H)-box RNA helicase, it is highly likely that this unwinding mechanism is applicable to a myriad of DEAD-box helicases employed in RNA metabolism.
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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) | 0.001 | 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".