Multiple triggers converge to preferential effector coupling in the CB2R through a complex allosteric communication network
Bibliographic record
Abstract
G protein-coupled receptors are important therapeutic drug targets for a wide range of diseases. Their ability to preferentially engage specific signaling pathways over others can be exploited to design drugs that target only disease-associated pathways leading to an improved safety profile. However, the underlying molecular mechanisms for preferential pathway engagement are complex and remain largely elusive. To elucidate the multifaceted actions at the receptor level that lead to preferential coupling, we employ a combination of techniques. Our approach integrates systematic mutagenesis of the CB2R and comprehensive profiling of Gαi2 and β-arrestin1 engagements with computer simulations to track mutant-induced impacts on receptor dynamics. Most importantly, our research discloses multiple triggers on a complex allosteric communication network (ACN) that converge to preferential CB2R coupling by modulating evolutionary conserved motifs (e.g., CWxP, NPxxY, sodium binding site). Potent triggers for a preferential Gαi2 response exhibit high levels of connectivity and are located in proximity to connections with high information transmission. Our insights highlight the complexity of GPCR signaling and can guide the rational design of drug candidates tailored to evoke specific functional responses that can enhance the precision and efficacy of therapeutic interventions.
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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 | 0.000 |
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".