Molecular Framework for the Activation of RNA-dependent Protein Kinase
Bibliographic record
Abstract
The RNA-dependent protein kinase (PKR) plays an integral role in the antiviral response to cellular infection. PKR contains three distinct domains consisting of two conserved N-terminal double-stranded RNA (dsRNA)-binding domains, a C-terminal Ser-Thr kinase domain, and a central 80-residue linker. Despite rich structural and biochemical data, a detailed mechanistic explanation of PKR activation remains unclear. Here we provide a framework for understanding dsRNA-dependent activation of PKR using nuclear magnetic resonance spectroscopy, dynamic light scattering, gel filtration, and autophosphorylation kinetics. In the latent state, PKR exists as an extended monomer, with an increase in self-affinity upon dsRNA association. Subsequent phosphorylation leads to efficient release of dsRNA followed by a greater increase in self-affinity. Activated PKR displays extensive conformational perturbations within the kinase domain. We propose an updated model for PKR activation in which the communication between RNA binding, central linker, and kinase domains is critical in the propagation of the activation signal and for PKR dimerization.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".