Molecular determinants underlying differential recruitment of p115RhoGEF and PDZRhoGEF to activated Gα <sub>13</sub>
Bibliographic record
Abstract
ABSTRACT Heterotrimeric G proteins, particularly Gα 12 and Gα 13 , are pivotal regulators of cellular signaling pathways. Their direct downstream effectors, which include p115RhoGEF and PDZRhoGEF, engage downstream signaling via Rho activation. Yet the molecular determinants that dictate their differential recruitment by Gα 12/13 are not fully understood. Here, we combined quantitative computational residue-level analysis with site-directed mutagenesis and bioluminescence resonance energy transfer (BRET)-based assays to dissect Gα 13 interactions with these RhoGEFs. We mapped the contributions of individual residues to binding and identified specific Gα 13 residues in its helical domain, switch regions, and effector-binding site as key yet differential contributors to p115RhoGEF and PDZRhoGEF recruitment. Experimental validation with BRET confirmed that changes in many Gα 13 residues impact p115RhoGEF more substantially than PDZRhoGEF, underscoring the specificity of Gα 13 interactions with p115RhoGEF. Investigation of the p115RhoGEFs identified critical residues that contribute to interactions with Gα 13 and Gα 12 . Our findings highlight residue-level differences in the molecular interactions of Gα 13 with p115RhoGEF and PDZRhoGEF, providing insights into the specificity and regulation of Gα 13 -mediated signaling pathways. The resulting residue-level maps lay the groundwork for development of selective therapeutic strategies targeting Gα 13 -RhoGEF interactions.
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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) | 0.001 | 0.000 |
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".