Molecular basis for activation and biased signaling at the thrombin-activated GPCR proteinase activated receptor-4 (PAR4)

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-NH2, and we monitored activation of the Gαq/11-coupled calcium-signaling pathway, β-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 β-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 Asp230 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 β-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.

Role of the C-terminal tail in regulating Proteinase Activated Receptor 2 (PAR2) signalling

“…Statistical significance of EC50 shifts and concentration-effect curve top (indicated as "Max.") were calculated using the extra sum of squares analysis and indicated in table 1 (*p < 0.05; Table 1) (26,66). Statistical significance for western blots was assessed using two-way analysis of variance (*p < 0.05).…”

“…To investigate the contribution of PAR2 stimulated G-protein and b-arrestin signalling on the phosphorylation of ERK (p-44/42), we employed pharmacological inhibition of Gαq/11 with the compound YM254890 and used a CRISPR/Cas9 barrestin-knockout HEK-293 cell (26). Both pathways were explored as ERK phosphorylation can occur downstream of Gαq/11-dependent and/or b-arrestindependent/G-protein-independent pathways.…”

“…Agonist-stimulated calcium signalling was recorded in PAR2-knockout HEK-293 cells as previously described (26,55,63). Cells were detached in enzyme-free cell dissociation buffer, centrifuged to pellet (1000 rpm, 5 minutes), and resuspended in Fluo-4 NW (no wash) calcium indicator dye (Thermo Fisher Scientific).…”

“…Agonist-stimulated mitogen-activated protein kinase (MAPK) signalling in PAR2knockout HEK-293 cells expressing PAR2-YFP or mutant PAR2-YFP was monitored by Western blot analysis as previously described (26,55,63). Cells expressing PAR2 receptor constructs were serum starved (2 hours) and stimulated with sub-maximal concentrations of SLIGRL-NH2 (3 µM) or trypsin (0.3 nM) for 10 minutes at 37℃.…”

Docking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement

“…Docking of larger peptides requires tools dedicated to flexible peptide docking, protein-protein docking, or modeling protocols tailored to particular GPCR-peptide systems. The examples include template-based modeling using Rosetta [12], manual docking guided by NMR data [13], CABS-dock docking followed by selection of plausible models [14], application of the hybrid molecular modeling protocol that integrates heterogeneous experimental data with force field-based calculations [15], application of ZDOCK and RDOCK tools for protein-protein docking [16] and GalaxyPepDock [17]. Nevertheless, the performance of neither of these methods was evaluated by prediction of a larger set of different GPCR-peptide complexes.…”