Rapamycin-insensitive mTORC1 activity controls eIF4E:4E-BP1 binding
Bibliographic record
Abstract
The recent development of mammalian target of rapamycin (mTOR) kinase domain inhibitors and genetic dissection of rapamycin-sensitive and -insensitive mTOR protein complexes (mTORC1 and mTORC2) have revealed that phosphorylation of the mTOR substrate 4E-BP1 on amino acids Thr37 and/or Thr46 represents a rapamycin-insensitive activity of mTORC1. Despite numerous previous reports utilizing serine (Ser)-to-alanine (Ala) and threonine (Thr)-to-Ala phosphorylation site mutants of 4E-BP1 to assess which post-translational modification(s) directly regulate binding to eIF4E, an ambiguous understanding persists. This manuscript demonstrates that the initial, rapamycin-insensitive phosphorylation event at Thr46 is sufficient to prevent eIF4E:4E-BP1 binding. This finding is relevant, particularly as mTOR kinase domain inhibitors continue to be assessed for clinical efficacy, since it clarifies a difference between the action of these second-generation mTOR inhibitors and those of rapamycin analogues.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".