Hierarchical phosphorylation of the translation inhibitor 4E-BP1
Bibliographic record
Abstract
In most instances, translation is regulated at the initiation phase, when a ribosome is recruited to the 5' end of an mRNA. The eIF4E-binding proteins (4E-BPs) interdict translation initiation by binding to the translation factor eIF4E, and preventing recruitment of the translation machinery to mRNA. The 4E-BPs inhibit translation in a reversible manner. Hypophosphorylated 4E-BPs interact avidly with eIF4E, whereas 4E-BP hyperphosphorylation, elicited by stimulation of cells with hormones, cytokines, or growth factors, results in an abrogation of eIF4E-binding activity. We reported previously that phosphorylation of 4E-BP1 on Thr 37 and Thr 46 is relatively insensitive to serum deprivation and rapamycin treatment, and that phosphorylation of these residues is required for the subsequent phosphorylation of a set of unidentified serum-responsive sites. Here, using mass spectrometry, we identify the serum-responsive, rapamycin-sensitive sites as Ser 65 and Thr 70. Utilizing a novel combination of two-dimensional isoelectric focusing/SDS-PAGE and Western blotting with phosphospecific antibodies, we also establish the order of 4E-BP1 phosphorylation in vivo; phosphorylation of Thr 37/Thr 46 is followed by Thr 70 phosphorylation, and Ser 65 is phosphorylated last. Finally, we show that phosphorylation of Ser 65 and Thr 70 alone is insufficient to block binding to eIF4E, indicating that a combination of phosphorylation events is necessary to dissociate 4E-BP1 from 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".