Deciphering the Molecular Association of Human CRIP1a with an Agonist-Bound Cannabinoid Receptor 1
Bibliographic record
Abstract
Cannabinoid receptor 1 (CB1) is a class A G-protein-coupled receptor that plays important roles in several physiological and pathophysiological processes. Therefore, targeted regulation of CB1 activity is a potential therapeutic strategy for several diseases, including neurological disorders. Apart from cannabinoid ligands, CB1 signaling can also be regulated by different CB1-associated proteins. In particular, the cannabinoid receptor interacting protein 1a (CRIP1a) associates with an activated CB1 receptor and alters the G-protein selectivity, thereby reducing the agonist-mediated signal transduction of the CB1 receptor. Experimental evidence suggests that two peptides corresponding to the distal and central C-terminal segments of CB1 could interact with CRIP1a. However, our knowledge of the molecular basis of CB1-CRIP1a recognition is still limited. In this work, we use an extensive combination of computational methods to build the first comprehensive atomistic model human CB1-CRIP1a complex. Our model provides novel structural insights into the interactions of CRIP1a with a membrane-embedded, complete, agonist-bound CB1 receptor in humans. Our results highlight the key residues that stabilize the CB1-CRIP1a complex, which will be useful to guide in vitro mutagenesis experiments. Furthermore, our human CB1-CRIP1a complex presents a model system for structure-based drug design to target this physiologically important complex for modulating CB1 activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".