Activation processes in ligand‐activated G protein‐coupled receptors: A case study of the adenosine A<sub>2A</sub> receptor
Bibliographic record
Abstract
Here we review concepts related to an ensemble description of G‐protein‐coupled receptors (GPCRs). The ensemble is characterized by both inactive and active states, whose equilibrium populations and exchange rates depend sensitively on ligand, environment, and allosteric factors. This review focuses on the adenosine A2 receptor (A2AR), a prototypical class A GPCR. 19F Nuclear Magnetic Resonance (NMR) studies show that apo A2AR is characterized by a broad ensemble of conformers, spanning inactive to active states, and resembling states defined earlier for rhodopsin. In keeping with ideas associated with a conformational selection mechanism, addition of agonist serves to allosterically restrict the overall degrees of freedom at the G protein binding interface and bias both states and functional dynamics to facilitate G protein binding and subsequent activation. While the ligand does not necessarily “induce” activation, it does bias sampling of states, increase the cooperativity of the activation process and thus, the lifetimes of functional activation intermediates, while restricting conformational dynamics to that needed for activation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".