A High-Throughput Method for Screening Peptide Activators of G-Protein-Coupled Receptors
Bibliographic record
Abstract
Here, we describe an innovative and efficient method for screening peptide activators of G-protein-coupled receptors (GPCRs) utilizing a protein-protein interaction (PPI) approach. We designed a library of 92,918 peptides fused with transmembrane domains of glycosylphosphatidylinositol-anchored proteins (GPI-APs). We employed a pooled lentiviral system to promote the expression of these proteins at the cellular membrane and evaluate their ability to activate GPCRs. We then used fluorescence-activated cell sorting (FACS) to screen the GPI-AP-peptide library and identify novel peptide activators of the glucagon-like peptide-1 receptor (GLP-1R). We discovered one peptide PepA3 derived from the Frizzled-like (FZ) domain of human Carboxypeptidase Z (CPZ), a regulated secreted metallocarboxypeptidase. Notably, PepA3 and its two related variants, PepA and PepA2, activated the GLP-1R receptor with less potency but comparable efficacy to that of GLP-1. We then hypothesized that all of these peptides will bind differently to the GLP-1R than the normal ligand. Our technology could identify novel GPCR-activating peptides for structure-function or drug discovery research.
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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".