Molecular basis for activation and biased signaling at the thrombin-activated GPCR proteinase activated receptor-4 (PAR4)
Bibliographic record
Abstract
Proteinase-activated receptor (PAR)-4 is a member of the proteolytically-activated PAR family of G-protein-coupled receptors (GPCR) that represents an important target in the development of anti-platelet therapeutics.PARs are activated by proteolytic cleavage of their receptor N terminus by enzymes such as thrombin, trypsin, and cathepsin-G.This reveals the receptor-activating motif, termed the tethered ligand that binds intramolecularly to the receptor and triggers signaling.However, PARs are also activated by exogenous application of synthetic peptides derived from the tethered-ligand sequence.To better understand the molecular basis for PAR4-dependent signaling, we examined PAR4-signaling responses to a peptide library derived from the canonical PAR4-agonist peptide, AYPGKF-NH 2 , and we monitored activation of the G␣ q/11 -coupled calcium-signaling pathway, -arrestin recruitment, and mitogen-activated protein kinase (MAPK) pathway activation.We identified peptides that are poor activators of PAR4-dependent calcium signaling but were fully competent in recruiting -arrestin-1 and -2.Peptides that were unable to stimulate PAR4-dependent calcium signaling could not trigger MAPK activation.Using in silico docking and site-directed mutagenesis, we identified Asp 230 in the extracellular loop-2 as being critical for PAR4 activation by both agonist peptide and the tethered ligand.Probing the consequence of biased signaling on platelet activation, we found that a peptide that cannot activate calcium signaling fails to cause platelet aggregation, whereas a peptide that is able to stimulate calcium signaling and is more potent for -arrestin recruitment triggered greater levels of platelet aggregation compared with the canonical PAR4 agonist peptide.These findings uncover molecular determinants critical for agonist binding and biased signaling through PAR4.
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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) | 0.003 | 0.001 |
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".