Biochemical and Structural Insights into the Eukaryotic Translation Initiation Factor eIF4E
Bibliographic record
Abstract
A major question in cell and cancer biology is concerned with understanding the flow of information from gene to protein. Indeed, many studies indicate that the proteome can be decoupled from the transcriptome. A major source of this decoupling is post-transcriptional regulation. The eukaryotic translation initiation factor eIF4E serves as an excellent example of a protein that can modulate the proteome at the post-transcriptional level. eIF4E is elevated in many cancers thus highlighting the relevance of this mode of control to biology. In this review, we provide a brief overview of various functions of eIF4E in RNA metabolism e.g. in nuclear-cytoplasmic RNA export, translation, RNA stability and/or sequestration. We focus on the modalities of eIF4E regulation at the biochemical and particularly structural level. In this instance, we describe not only the importance for the m7Gcap eIF4E interaction but also of recently discovered non-traditional RNA-eIF4E interactions as well as cap-independent activities of eIF4E. Further, we describe several distinct structural modalities used by the cell and some viruses to regulate or co-opt eIF4E, substantially extending the types of proteins that can regulate eIF4E from the traditional eIF4E-binding proteins (e.g. 4E-BP1 and eIF4G). Finally, we provide an overview of the results of targeting eIF4E activity in the clinic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".