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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".