Interactions between the double-stranded RNA-binding proteins TRBP and PACT define the Medipal domain that mediates protein-protein interactions
Bibliographic record
Abstract
The double-stranded (ds) RNA binding proteins, TRBP and PACT bind the interferon-induced protein kinase PKR and dsRNA. TRBP inhibits, whereas PACT activates PKR. They have two dsRNA binding domains (dsRBDs) and a C-terminal domain that does not bind RNA. All three domains show a strong homology between the two proteins. Interaction assays by in vitro binding, yeast two-hybrid, and immunoprecipitations show that TRBP and PACT form heterodimers in the absence of dsRNA. In cells, TRBP and PACT colocalize in specific dots of the perinuclear space. Analysis of the individual domains shows that the two dsRBDs of each protein interact with each other. In contrast, the C-terminal domain of PACT homodimerizes and interacts with its homologous region in TRBP, but the same domain in TRBP does not homodimerize. Because the C-terminal domain in TRBP binds to the tumor suppressor Merlin, the RNase III Dicer and PACT, we name it the Merlin Dicer PACT liaison (Medipal) domain. Based on known interactions Medipal is defined as aminoacids 228-366 in TRBP and 195-313 in PACT. TRBP-PACT interaction correlates with an absence of eIF2alpha activation by PACT, suggesting that the heterodimer does not activate PKR. We propose that the Medipal domain mediates specialized functions through protein-protein interactions and contributes to the RNA interference pathway and to PKR activation.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".