Parallel Interrogation of β-Arrestin2 Recruitment for Ligand Screening on a GPCR-Wide Scale using PRESTO-Tango Assay
Bibliographic record
Abstract
As the largest and most versatile gene superfamily and mediators of a gamut of cellular signaling pathways, G-protein-coupled receptors (GPCRs) represent one of the most promising targets for the pharmaceutical industry. Ergo, the design, implementation, and optimization of GPCR ligand screening assays is crucial, as they represent remote-control tools for drug discovery and for manipulating GPCR pharmacology and outcomes. In the past, G-protein dependent assays typified this area of research, detecting ligand-induced events and quantifying the generation of secondary messengers. However, since the advent of functional selectivity, as well as an increased awareness of several other G protein-independent pathways and the limitations associated with G-protein dependent assays, there is a greater push towards the creation of alternative GPCR ligand screening assays. Towards this endeavor, we describe the application of one such resource, the PRESTO-Tango platform, a luciferase reporter-based system that enables the parallel and simultaneous interrogation of the human GPCR-ome, a feat which was previously considered technically and economically unfeasible. Based on a G-protein independent β-arrestin2 recruitment assay, the universality of β-arrestin2-mediated trafficking and signaling at GPCRs makes PRESTO-TANGO an apt tool for studying approximately 300 non-olfactory human GPCRs, including approximately 100 orphan receptors. PRESTO-Tango's sensitivity and robustness make it suitable for primary high-throughput screens using compound libraries, employed to uncover new GPCR targets for known drugs or to discover new ligands for orphan receptors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".