Gα <sub>s</sub> -specific structural elements attenuate interactions with RGS proteins
Bibliographic record
Abstract
Abstract Heterotrimeric (αβγ) G proteins are molecular switches that are activated by G protein-coupled receptors (GPCRs) and regulate numerous intracellular signaling cascades. Most active Gα subunits are inactivated by Regulators of G protein Signaling (RGS) proteins, which determine the duration of G protein-mediated signaling by accelerating the catalytic turn-off of the Gα subunit. However, the G protein Gα s does not interact with known RGS proteins. To understand the molecular basis for this divergent phenomenon, we combined a comparative structural analysis of experimental and modelled structures with functional biochemical assays. This analysis showed that Gα s contains unique structural elements in both the helical and the GTPase domains. Modeling identified helical domain insertions, missing in experimental structures, that project towards the interface with RGS proteins, and residues in the GTPase domain that might interfere with RGS binding. Mutagenesis of Gα s and measurements of RGS GAP activity showed that three residues in the Gα s GTPase domain are both necessary and sufficient to prevent Gα s inactivation by RGSs. Indeed, substitution of all three Gα s residues with the corresponding residues from Gα i1 enabled efficient inactivation by RGS proteins. These results shed new light on the mechanistic bases for G protein specificity towards RGS proteins.
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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) | 0.002 | 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".