Modulation of the Eukaryotic Initiation Factor 2 α-Subunit Kinase PERK by Tyrosine Phosphorylation
Bibliographic record
Abstract
The endoplasmic reticulum (ER)-resident protein kinase PERK attenuates protein synthesis in response to ER stress through the phosphorylation of translation initiation factor eIF2alpha at serine 51. ER stress induces PERK autophosphorylation at several serine/threonine residues, a process that is required for kinase activation and phosphorylation of eIF2alpha. Herein, we demonstrate that PERK also possesses tyrosine kinase activity. Specifically, we show that PERK is capable of autophosphorylating on tyrosine residues in vitro and in vivo. We further show that tyrosine 615, which is embedded in a highly conserved region of the kinase domain of PERK, is essential for autocatalytic activity. That is, mutation of Tyr-615 to phenylalanine compromises the autophosphorylation capacity of PERK and the phosphorylation of eIF2alpha in vitro and in vivo. The Y615F mutation also impairs the ability of PERK to induce translation of ATF4. Immunoblot analyses with a phosphospecific antibody confirm the phosphorylation of PERK at Tyr-615 both in vitro and in vivo. Thus, our data classify PERK as a dual specificity kinase whose regulation by tyrosine phosphorylation contributes to its optimal activation in response to ER stress.
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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".