The directed evolution of ligand specificity in a GPCR and the unequal contributions of efficacy and affinity
Bibliographic record
Abstract
G protein-coupled receptors (GPCRs) must discriminate between hundreds of related signal molecules. In order to better understand how GPCR specificity can arise from a common promiscuous ancestor, we used laboratory evolution to invert the specificity of the Saccharomyces cerevisiae mating receptor Ste2. This GPCR normally responds weakly to the pheromone of the related species Kluyveromyces lactis, though we previously showed that mutation N216S is sufficient to make this receptor promiscuous. Here, we found that three additional substitutions, A265T, Y266F and P290Q, can act together to confer a novel specificity for K. lactis pheromone. Unlike wild-type Ste2, this new variant does not rely on differences in binding affinity to discriminate against its non-preferred ligand. Instead, the mutation P290Q is critical for suppressing the efficacy of the native pheromone. These two alternative methods of ligand discrimination were mapped to specific amino acid positions on the peptide pheromones. Our work demonstrates that changes in ligand efficacy can drive changes in GPCR specificity, thus obviating the need for extensive binding pocket re-modeling.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".