<scp>GPCR</scp> heterodimers: asymmetries in ligand binding and signalling output offer new targets for drug discovery
Bibliographic record
Abstract
UNLABELLED: Dimers of GPCRs have held the imagination of researchers for almost 20 years. However, only recently has their value as potentially novel drug targets been increased significantly, and primarily, in the context of GPCR heterodimers. The view of receptor heterodimers as allosteric machines has transformed the way we understand structural and functional asymmetries inherent in their organization. These asymmetries alter both signalling output and how they might be targeted pharmacologically. The paper in this issue of BJP by Siddiquee and colleagues () highlights our growing understanding of such asymmetries and their implications. They show that heterodimers of the angiotensin II AT1 receptor and the apelin receptor recognize and respond to their respective ligands in distinct ways from the parent receptors expressed alone. Further, they demonstrate asymmetric allosteric effects in the context of the heterodimer that may have significant implications for our understanding of such receptor complexes. LINKED ARTICLE: This article is a commentary on the research paper by Siddiquee et al., pp. 1104-1117 of this issue. To view this paper visit http://dx.doi.org/10.1111/j.1476-5381.2012.02192.x.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".