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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".