Computational design of orthogonal membrane receptor-effector switches for rewiring signaling pathways
Bibliographic record
Abstract
Membrane receptors regulate numerous intracellular functions. However, the molecular underpinnings remain poorly understood because most receptors initiate multiple signaling pathways through distinct interaction interfaces that are structurally uncharacterized. We present an integrated computational and experimental approach to model and rationally engineer membrane receptor-intracellular protein systems signaling with novel pathway selectivity. We targeted the dopamine D2 receptor (D2), a G-protein-coupled receptor (GPCR), which primarily signals through Gi, but triggers also the Gq and beta-arrestin pathways. Using this approach, we designed orthogonal D2-Gi complexes, which coupled with high specificity and triggered exclusively the Gi-dependent signaling pathway. We also engineered an orthogonal chimeric D2-Gs/i complex that rewired D2 signaling from a Gi-mediated inhibitory into a Gs-dependent activating pathway. Reinterpreting the evolutionary history of GPCRs in light of the designed proteins, we uncovered an unforeseen hierarchical code of GPCR-G-protein coupling selectivity determinants. The results demonstrate that membrane receptor-cytosolic protein systems can be rationally engineered to regulate mammalian cellular functions. The method should prove useful for creating orthogonal molecular switches that redirect signals at the cell surface for cell-engineering applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".