Pharmacogenomics of G Protein-Coupled Receptor
Bibliographic record
Abstract
The identification and characterization of the processes of G protein-coupled receptor (GPCR) activation and inactivation have refined not only the study of the GPCRs but also the genomics of many accessory proteins necessary for these processes. This has accelerated progress in understanding the fundamental mechanisms involved in GPCR structure and function, including receptor transport to the membrane, ligand binding, activation and inactivation by GRK-mediated (and other) phosphorylation. The catalog of G(s)alpha and Gbeta subunit polymorphisms that result in complex phenotypes has complemented the effort to catalog the GPCRs and their variants. The study of the genomics of GPCR accessory proteins has also provided insight into pathways of disease, such as the contributions of regulator of G protein signaling (RGS) protein to hypertension and activator of G protein signaling (AGS) proteins to the response to hypoxia. In the case of the G protein-coupled receptor kinases (GRKs), identified originally in the retinal tissues that converge on rhodopsin, proteins such as GRK4 have been identified that have been subsequently associated with hypertension. Here, we review the structure and function of GPCR and associated proteins in the context of the gene families that encode them and the genetic disorders associated with their altered function. An understanding of the pharmacogenomics of GPCR signaling provides the basis for examining the GPCRs disrupted in monogenic disease and the pharmacogenetics of a given receptor system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".