Multiple intramolecular triggers converge to preferential G protein coupling in the CB2R
Bibliographic record
Abstract
G protein-coupled receptors (GPCRs) are important therapeutic drug targets for a wide range of diseases. Upon activation, GPCRs can initiate several signaling pathways, each with unique therapeutic implications. Therefore, understanding how drugs selectively engage specific signaling pathways becomes paramount. However, achieving this selectivity remains highly challenging. To unravel the underlying multifaceted mechanisms, we integrate systematic mutagenesis of the CB2R, comprehensive profiling of Gαi2 and β-arrestin1 engagements and computer simulations to track the effects of mutations on receptor dynamics. Our research reveals multiple triggers within a complex allosteric communication network (ACN) that converge to preferential CB2R coupling by modulating evolutionarily conserved motifs. Utilizing network path analysis, we find that potent triggers are typically highly connected nodes and are located near regions of high information transmission within the ACN. Our insights highlight the complexity of GPCR signaling and provide a framework for the rational design of drug candidates tailored to evoke specific functional responses, ultimately enhancing the precision and efficacy of therapeutic interventions. Nonselective engagement of GPCR signaling pathways by GPCR-targeting drugs can reduce treatment efficacy and cause side effects. The authors show that signaling selectivity in CB2R can be tuned by reshaping allosteric networks, offering insights for more precise therapies.
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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".