Computational design of allosteric pathways reprograms ligand-selective GPCR signaling
Bibliographic record
Abstract
G-protein-coupled receptors (GPCRs) constitute the largest family of signaling receptors and drug targets. However, understanding how variations in receptor sequence, ligand chemical structure, and binding impact signaling functions remains a challenge, hindering drug discovery. Here, we developed a computational protein structure and dynamics approach to infer and design GPCR responses to multiple ligands. We created 32 dopamine D1 and D2 receptor variants with widely reprogrammed agonist-induced signal transductions. Subtle natural and designed receptor sequence variations, predicted to alter specific structural and dynamic mechanisms of ligand responses, profoundly impacted ligand potency and efficacy in agreement with our calculations. Our study provides a rational blueprint for computing the effect of sequence polymorphisms on ligand-selective protein signaling and paves the way for advancements in pharmacogenomics, drug selectivity, and the design of signaling receptors from first principles.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".