Receptor-regulated Interaction of Activator of G-protein Signaling-4 and Gαi
Bibliographic record
Abstract
Activator of G-protein signaling-4 (AGS4), via its three G-protein regulatory motifs, is well positioned to modulate G-protein signal processing by virtue of its ability to bind Galpha(i)-GDP subunits free of Gbetagamma. Apart from initial observations on the biochemical activity of the G-protein regulatory motifs of AGS4, very little is known about the nature of the AGS4-G-protein interaction, how this interaction is regulated, or where the interaction takes place. As an initial approach to these questions, we evaluated the interaction of AGS4 with Galpha(i1) in living cells using bioluminescence resonance energy transfer (BRET). AGS4 and Galpha(i1) reciprocally tagged with either Renilla luciferase (RLuc) or yellow fluorescent protein (YFP) demonstrated saturable, specific BRET signals. BRET signals observed between AGS4-RLuc and Galpha(i1)-YFP were reduced by G-protein-coupled receptor activation, and this agonist-induced reduction in BRET was blocked by pertussis toxin. In addition, specific BRET signals were observed for AGS4-RLuc and alpha(2)-adrenergic receptor-Venus, which were Galpha(i)-dependent and reduced by agonist, indicating that AGS4-Galpha(i) complexes are receptor-proximal. These data suggest that AGS4-Galpha(i) complexes directly couple to a G-protein-coupled receptor and may serve as substrates for agonist-induced G-protein 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".