Regulation of AGS3 and Gialpha1 interaction in living cells
Bibliographic record
Abstract
Activator of G Protein Signaling 3 (AGS3) and related GPR proteins provide unexpected regulatory mechanisms for G‐protein signaling systems. AGS3 contains 4 GPR motifs, each of which can serve as a docking site for Giα‐GDP free of Gβγ. Two of the key questions for AGS3 is what controls its interaction with G‐protein and where does this interaction occur within the cell. As an initial approach to this question, we evaluated the interaction of AGS3 with Giα1 in living cells using bioluminescence resonance energy transfer (BRET) to measure interaction between proteins tagged with Renilla luciferase (RLuc) and the YFP‐variant Venus in HEK cells. AGS3 and Giα1 reciprocally tagged with either RLuc or YFP exhibited a BRET signal that was saturable and specific. The net BRET signal was blocked by Gβγ expression and it was not observed with the AGS3‐Q/A mutant in which each of the GPR motifs are rendered incapable of binding Giα. BRET was observed with AGS3‐YFP and Giα1‐RLuc or with AGS3‐RLuc and Giα1‐YFP pairs. The latter donor and acceptor pair exhibited a significantly stronger net BRET signal potentially reflecting the assembly of a Giα1‐YFP acceptor on each of the four GPR docking sites in an individual AGS3‐RLuc molecule. These data indicate a robust interaction of GPR proteins and Giα1 in the living cell and provide a platform to determine how this interaction is regulated and where it occurs within the cell.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".