PKR and GCN2 Kinases and Guanine Nucleotide Exchange Factor Eukaryotic Translation Initiation Factor 2B (eIF2B) Recognize Overlapping Surfaces on eIF2α
Bibliographic record
Abstract
Four stress-responsive protein kinases, including GCN2 and PKR, phosphorylate eukaryotic translation initiation factor 2alpha (eIF2alpha) on Ser51 to regulate general and gene-specific protein synthesis. Phosphorylated eIF2 is an inhibitor of its guanine nucleotide exchange factor, eIF2B. Mutations that block translational regulation were isolated throughout the N-terminal OB-fold domain in Saccharomyces cerevisiae eIF2alpha, including those at residues flanking Ser51 and around 20 A away in the conserved motif K79GYID83. Any mutation at Glu49 or Asp83 blocked translational regulation; however, only a subset of these mutations impaired Ser51 phosphorylation. Substitution of Ala for Asp83 eliminated phosphorylation by GCN2 and PKR both in vivo and in vitro, establishing the critical contributions of remote residues to kinase-substrate recognition. In contrast, mutations that blocked translational regulation but not Ser51 phosphorylation impaired the binding of eIF2B to phosphorylated eIF2alpha. Thus, two structurally distinct effectors of eIF2 function, eIF2alpha kinases and eIF2B, have evolved to recognize the same surface and overlapping determinants on eIF2alpha.
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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 | 0.001 |
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".