Allosteric and Biased G Protein-Coupled Receptor Signaling Regulation: Potentials for New Therapeutics
Bibliographic record
Abstract
G protein-coupled receptors (GPCRs) are seven-transmembrane proteins that participate in many aspects of the endocrine function and are important targets for drug development. They transduce signals mainly, but not exclusively, via hetero-trimeric G proteins, leading to a diversity of intracellular signaling cascades. Ligands binding at the hormone orthosteric sites of receptors have been classified as agonists, antagonists, and/or inverse agonists based on their ability to mainly modulate G protein signaling. Accumulating evidence also indicates that such ligands, alone or in combination with other ones such as those acting outside the orthosteric hormone binding sites (e.g., allosteric modulators), have the ability to selectively engage subsets of signaling responses as compared to the natural endogenous ligands. Such modes of functioning have been variously referred to as "functional selectivity" or "ligand-biased signaling." In this review, we provide an overview of the current knowledge regarding GPCR-biased signaling and their functional regulation with a focus on the evolving concept that receptor domains can also be targeted to allosterically bias signaling, and discuss the usefulness of such modes of regulation for the design of more efficient therapeutics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".