Using Ligand-Induced Conformational Change to Screen for Compounds Targeting G-Protein-Coupled Receptors
Bibliographic record
Abstract
The authors describe a novel drug strategy designed as a primary screen to discover either antagonist or agonist compounds targeting G-protein-coupled receptors (GPCRs). The incorporation of a nuclear localization sequence (NLS, a 5 amino acid substitution), in a location in helix 8 of the GPCR structure, resulted in ligand-independent receptor translocation from the cell surface to the nucleus. Blockade of the GPCR-NLS translocation from the cell surface was achieved by either antagonist or agonist treatments, each achieving their result in a sensitive concentration-dependent manner. GPCR-NLS translocation and blockade occurred regardless of the identity of the G-protein-coupling, and thus this assay is also ideally suited for identification of compounds targeting orphan GPCRs. The GPCR-NLS trafficking was visualized by fusion to fluorescent detectable proteins. Quantification of this effect was measured by determining the density of cell surface receptors, using enzyme fragment complementation in a manner suitable for high-throughput screening. Thus, the authors have developed a cellular assay for GPCRs suitable for compound screening without requiring prior identification of an agonist or knowledge of G-protein-coupling.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".