Evolutionary Alternatives on the Road to a New Specificity in a G Protein-Coupled Receptor
Bibliographic record
Abstract
Cells sense change in their external environment and react appropriately through the action of signaling pathways. This process is initiated by receptor proteins with high degrees of specificity for a particular stimulus. G protein-coupled receptors form the largest family of membrane protein receptors and their ability to sense a broad variety of ligands is unparalleled despite their common ancestral origin. How GPCRs evolved their unique specificities is unknown, although ligand binding affinity is often given a central role. The goal of this thesis was to assess the possible contributions of secondary mechanisms of specificity, namely ligand efficacy and downstream signaling regulation, to changes in ligand recognition. Through directed evolution, we generated a yeast pheromone receptor with an altered specificity in two steps. First, promiscuous receptors were obtained through either improved binding affinity or weaker molecular interaction with a negative regulator of signaling. Second, a ligand-discriminating receptor was obtained from a promiscuous variant solely by reducing the efficacy of the native pheromone. These findings demonstrate the importance of assessing GPCRsâ pharmacological profiles in their native context, where signaling trumps binding affinity in significance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".