Agonist efficacy at the β2AR is driven by the faster association rate of the Gs protein

Introduction: The β₂-adrenoceptor (β₂AR) is a class A G protein-coupled receptor (GPCR). It is therapeutically relevant in asthma and chronic obstructive pulmonary disease (COPD), where β₂AR agonists relieve bronchoconstriction. The β₂AR is a prototypical GPCR for structural and biophysical studies. However, the molecular basis of agonist efficacy at the β₂AR is not understood. We hypothesised that the kinetics of GPCR–G protein interactions could play a role in determining ligand efficacy. By studying a range of agonists with varying efficacy, we examined the relationship between ligand-induced mini-G_s binding to the β₂AR and ligand efficacy, along with the ability of individual ligands to activate the G protein in cells.

Methods: We used NanoBRET technology to measure ligand-induced binding of purified Venus-mini-G_s to β₂AR-nLuc in membrane preparations under both equilibrium and kinetic conditions. In addition, we examined the ability of these β₂AR agonists to activate the heterotrimeric G_s protein, measured using the G_s-CASE protein biosensor in living cells. This assay detects a reduction in NanoBRET between the nano-luciferase (nLuc) donor on the Gα subunit and Venus acceptor on the Gγ upon G_s protein activation.

Results: The 12 β₂AR agonists under study revealed a broad range of ligand potency and efficacy values in the cellular G_s-CASE assays. Kinetic characterisation of mini-G_s binding to the agonist β₂AR complex revealed a strong correlation between ligand efficacy values (E_max) and mini-G_s affinity (K_d) and its association rate (k_on). In contrast, there was no correlation between ligand efficacy and reported ligand dissociation rates (or residence times).

Conclusion: The association rate (k_on) of the G protein to the agonist β₂AR complex is directly correlated with ligand efficacy. These data support a model in which higher-efficacy agonists induce the β₂AR to adopt a conformation that is more likely to recruit G protein. Conversely, these data did not support the role of agonist binding kinetics in determining the molecular basis of efficacy.

Introduction

G protein-coupled receptors (GPCRs) are the largest family of membrane proteins in the human genome and are responsible for modulating a broad range of hormonal, neurological, and immune responses. GPCR-directed therapeutics currently target over 100 diverse receptors and represent 34% of all US Food and Drug Administration (FDA)-approved drugs, making them the most widely targeted receptors (Hauser et al., 2017). Despite their therapeutic importance, the molecular basis of ligand efficacy—the ability of a drug to affect GPCR signal transduction—is not fully understood. It is hoped that a deeper understanding of the molecular basis of efficacy will aid in more rational drug design.

The process of GPCR activation involves agonist binding, a ligand-induced conformational change in the receptor and the subsequent recruitment and activation of a G protein. Several studies have implicated ligand residence time in the molecular basis of efficacy at GPCRs. For example, a positive correlation has been observed between the efficacy of seven agonists at the muscarinic M3 receptor and 10 agonists at the adenosine A_2A receptor (A_2AR) with their ligand residence time (Sykes et al., 2009b; Guo et al., 2012). Conversely, no correlation between efficacy and residency time was found for ligands at the adenosine A₁ receptor (Louvel et al., 2014).

Biophysical studies have shown that agonists shift the receptor conformational landscape in favour of a unique active conformation, compared to the unliganded state (Deupi and Kobilka, 2010; Mary et al., 2012; Nygaard et al., 2013), but how conformational differences in a population translate to greater or lesser signalling responses remains to be fully elucidated. Structural studies have found little differences in GPCR conformations adopted by ligand-bound GPCR–G-protein complexes (Masureel et al., 2018; Zhang et al., 2020). However, using nuclear magnetic resonance (NMR), Liu et al. (2012) showed efficacy-dependent differences in the conformational state of β₂AR bound to different agonists prior to G protein binding. Similar results have been observed for the β₁AR (Grahl et al., 2020; Jones et al., 2024) and A_2AR (Ye et al., 2016). Alternatively, some studies (Nikolaev et al., 2006; Gregorio et al., 2017) show correlations between ligand efficacy and the rate of GPCR and G protein activation, suggesting a key role for G protein binding kinetics in dictating pharmacological efficacy.

Consequentially, we aimed to delineate the roles of ligand binding and receptor–G protein binding kinetics in agonist efficacy. We focused on the β₂-adrenoceptor (β₂AR), a prototypical class A GPCR, which is one of the most structurally, functionally, and therapeutically well-characterised GPCRs. The β₂AR is also an essential target in the treatment of asthma and COPD, and as a result, a wide range of clinically used agonists of varying efficacies have been developed to target the β₂AR, which could be utilised in this study.

G proteins are heterotrimeric, consisting of α, β, and γ subunits The Gα subunit comprises of a helical and GTPase domain. Full-length heterotrimeric G proteins are dynamic complexes that are difficult to isolate. To overcome this, we chose to utilise mini-G proteins (Carpenter and Tate, 2016) as tools to study the dynamics of β₂AR activation. The mini-G_s protein is the isolated GTPase domain of the Gα subunit, which has been engineered with several thermostabilising mutations that make it a rigid protein, locked in its active state, as shown in the agonist-bound A_2AR-mini-G_s structure (Carpenter et al., 2016; Carpenter and Tate, 2017). These mini-G proteins have also been converted into convenient probes that report the active state of a GPCR (Wan et al., 2018).

We investigated the binding kinetics and affinity of fluorescently labelled (Venus-fused) mini-G_s proteins for the β₂AR in complex with a set of agonists of varying efficacy, from partial to full agonists. In addition, we correlated ligand binding affinities, residence times, and efficacy at the level of heterotrimeric G_s protein activation for these agonists.

Materials, instruments and software

Materials

The T-REx™-293 Cell Line was obtained from Invitrogen (CA, United States). T75 and T175 mammalian cell culture flasks were purchased from Fisher Scientific (Loughborough, United Kingdom). All cell culture reagents, including Hank’s balanced salt solution (HBSS), phosphate-buffered saline (PBS), and foetal calf serum (FCS), were purchased from Sigma Aldrich (Gillingham, United Kingdom), except for blasticidin, which was obtained from Gibco™ (MA, United States), and Zeocin™. Polyethylenimine (PEI) (25 kDa) was obtained from Polysciences Inc. (PA, United States), and the culture plates were obtained from Greiner Bio-One (code 655098 Kremsmünster, Austria).

HisTrap FF crude 5-mL columns were obtained from GE Healthcare (IL, United States). Vivaspin protein concentrators were obtained from Sartorius (Göttingen, Germany). Slide-A-Lyzer Dialysis Cassettes, NuPAGE LDS Sample Buffer, NuPAGE 4%–12% Bis-Tris 15 × 1.0 mm well gels, NuPAGE MOPS SDS Running Buffer, PageRuler Prestained Protein Ladder, were all obtained from Thermo Fisher (MA, United States).

Salmeterol was obtained from Tocris (Bristol, U.K). Formoterol hemifumarate was obtained from APExBIO (TX, United States), and BI-167-107 was obtained from Boehringer Ingelheim (Ingelheim, Germany). Compound 26 was a gift from Novartis. (±)-Epinephrine hydrochloride, noradrenaline, salbutamol hemisulfate, and isoprenaline hydrochloride were purchased from Sigma-Aldrich (Gillingham, United Kingdom). Dobutamine hydrochloride was obtained from Merck Life Sciences, UK. Isoxsuprine hydrochloride, ritodrine hydrochloride, and tulobuterol were obtained from CliniSciences Limited. Nano-Glo luciferase substrate was obtained from Promega (WI, United States). All other chemicals were purchased from Sigma-Aldrich (Gillingham, United Kingdom).

Instruments and software

BMG PHERAstar FSX plate reader (BMG Labtech, Offenburg, Germany), fitted with BRET1 plus optic module (ex. 475/30 nm, em. 535/30 nm) and MARS software, was purchased from BMG Labtech (Offenburg, Germany). GraphPad Prism 9 was purchased from GraphPad Software (San Diego, United States). Microsoft Excel™ XP was purchased from Microsoft (Washington, United States).

Methods

Molecular biology

The construct pcDNA4TO-TwinStrep (TS)-SNAP-β₂AR was generated through the amplification of the SNAP and β₂AR sequences from the pSNAPf-ADRB2 plasmid (NEB) and inserted into pcDNA4TO-TS using Gibson assembly (Heydenreich et al., 2017). pcDNA4TO-TS-SNAP-β₂AR-nLuc was generated by Dr. Brad Hoare through the amplification of pcDNA4TO-TS-SNAP-β₂AR and nanoLuc, with the insertion of nanoLuc into pcDNA4TO-TS-SNAP-β₂AR via Gibson assembly. Both constructs used a signal peptide based on the 5HT_3A receptor to increase protein folding and expression. The CASE G_s (or G_s-CASE) protein constructs were designed and optimised by the Schulte Lab (Schihada et al., 2021) and were obtained from Addgene. Mammalian Venus-fused mini-G_s constructs were a kind gift from Nevin Lambert (Wan et al., 2018). For the bacterial expression of Venus-mini-G_s and mini-G_s, protein encoding DNA sequences were amplified from the corresponding mammalian constructs and inserted into the pJ411 vector containing MKK-HIS10-TEV N-terminal tag (Sun et al., 2015) via Gibson assembly, yielding the constructs MKK-HIS10-TEV-mini-G_s and MKK-HIS10-TEV-Venus-mini-G_s.

Transfection and mammalian cell culture

pcDNA4TO-TS-SNAP-β₂AR or pcDNA4TO-TS-SNAP-β₂AR-nLuc was stably transfected into T-REx™-293 cells (Invitrogen) using PEI. A stable mixed population was selected by resistance to 5 μg/mL blasticidin and 20 μg/mL zeocin. Stable cell lines were maintained in high-glucose DMEM (Sigma D6429) with 10% FBS, 5 μg/μL blasticidin, and 20 μg/μL zeocin at 37°C in a humidified atmosphere of 5% CO_2. When ∼70% confluent, TS-SNAP-β₂AR or TS-SNAP-β₂AR-nLuc expression was induced with 1 μg/mL tetracycline. Cells were left to express for 50 h before harvesting for assays. The T-REx™-293 pcDNA4TO-TS-SNAP-β₂AR-CASE G_s stable cell line was generated by stably transfecting the CASE G_s constructs into the T-REx™-293 pcDNA4TO-TS-SNAP-β₂AR using PEI. A mixed population stable cell line was generated by selection with 500 μg/mL G418, and then a single colony population was generated via FACS.

Membrane preparations of TS-SNAP-β₂AR-nLuc

For membrane preparation, all steps were conducted at 4°C to avoid tissue degradation. Cell pellets were thawed and re-suspended using ice-cold buffer containing 10 mM HEPES and 10 mM EDTA (pH 7.4). The suspension was homogenised using an electrical homogeniser (ULTRA-TURRAX, IKA-Werke GmbH, Germany) and subsequently centrifuged at 1,200 × g for 5 min. The pellet obtained, containing cell nucleus and other heavy organelles, was discarded, and the supernatant was centrifuged for 30 min at 48,000 × g at 4°C (Beckman Avanti J-251 Ultra-centrifuge; Beckman Coulter). The supernatant was discarded, and the pellet was re-suspended in the same buffer (10 mM HEPES and 10 mM EDTA; pH 7.4) and centrifuged again for 30 min as described above. Finally, the supernatant was discarded, and the pellet was re-suspended in ice-cold 10 mM HEPES and 0.1 mM EDTA (pH 7.4). Protein concentration determination was carried out using the bicinchoninic acid assay kit (Sigma-Aldrich) with BSA as the standard. The final membrane suspension was aliquoted and maintained at −80°C until required for the assays.

Solubilisation of the TS-SNAP-β₂AR or TS-SNAP-β₂AR-nLuc

TS-SNAP-β₂AR or TS-SNAP-β₂AR-nLuc was solubilised from stably transfected T-REx^TM-293 cell membranes, as described previously (Harwood et al., 2024). Solubilisation was carried out using 1% DDM (w/v) in 20 mM HEPES, 5% (v/v) glycerol, and 150 mM NaCl, pH 8, at 4°C for 2–3 h. Samples were clarified by ultracentrifugation at 4°C for 1 h at 100,000 × g.

Production of mini-G_s

His-TEV-Venus-mini-G_s and His-TEV-mini-G_s were expressed in NiCo21(DE3) E. coli, cultured in Terrific Broth (Gibco). 1L cultures were induced with 1 mM isopropyl β-D-1-thiogalactopyranoside (IPTG) at OD = 0.6 and incubated for a further 20 h at 20°C and 225 RPM. Pellets from 1L cultures were thawed on ice, and re-suspended in 50 mL lysis buffer (20 mM HEPES, pH 7.5, 500 mM NaCl, 40 mM imidazole, 10% glycerol, 8 mM β-mercaptoethanol (BME), 1 μM guanosine diphosphate (GDP), complete protease inhibitors (Roche), DNase I, and lysozyme) using a Dounce homogeniser. Lysis occurred on ice via sonication, using a Vibra-Cell probe sonicator with 5 × 10-s pulses, 30 s apart. The lysate was loaded onto the HisTrap FF crude 5-mL column, using ÄKTA™ start protein purification system at a flow rate of 5 mL/min. The system and column had been equilibrated with 10 column volumes (CV) of buffer A (20 mM HEPES, 500 mM NaCl, 40 mM imidazole, 10% glycerol, 8 mM BME, and 1 μM GDP). Unbound protein was washed out with 10 CV of buffer A. Bound protein was then eluted over an 8 CV gradient of 0% to 100% buffer B at a flow rate of 5 mL/min (Buffer B = 20 mM HEPES, 500 mM NaCl, 400 mM imidazole, 10% glycerol, 8 mM BME, and 1 μM GDP). The presence of His-TEV-Venus-mini-G_s and His-TEV-mini-G_s was confirmed by SDS-PAGE analysis and InstantBlue staining for protein. Pooled elution fractions were then concentrated using 10,000 or 30,000 molecular weight cutoff (MWCO) Vivaspin protein concentrators by centrifugation at 3000 × g and 4°C for 15-min intervals over 2–3 h. Protein was exchanged into assay buffer using Slide-A-Lyzer 10,000 or 30,000 MWCO dialysis cassettes for untagged and Venus-tagged mini-G_s protein samples, respectively. Dialysis occurred overnight at 4°C under constant stirring. The assay buffer consisted of 20 mM HEPES, 150 mM NaCl, 10% glycerol, 8 mM BME, and 1 μM GDP. The purified mini-G_s protein was flash-frozen using liquid nitrogen and stored at −80°C.

Membrane-based TS-SNAP-β₂AR-Venus-mini-G_s NanoBRET binding assays

The assay buffer, consisting of HBSS (Sigma H8264) containing 10 mM HEPES, 0.1% BSA, and 0.1% ascorbic acid, pH 7.4, was used in all NanoBRET assays. For recruitment assays, varying concentrations of β₂AR agonists were used to recruit Venus-mini-G_s to the TS-SNAP-β₂AR. Assays were run in 50 μL volumes in white 384-well OptiPlate (Revvity). Receptor, ligand, 0.3 μM mini-G_s proteins, and 10 μM furimazine were added to the plate and incubated for 60 min at room temperature before reading on PHERAstar FSX using the BRET1 module. For kinetic assays, in which the affinity of Venus-mini-G_s for the agonist-bound TS-SNAP-β₂AR-nLuc receptors was measured over time, assays were run in 50 μL volumes in white 384-well OptiPlate. Varying concentrations (10–300 nM) of Venus-mini-G_s were added to assay plates. TS-SNAP-β₂AR membranes were pre-incubated with saturating concentrations (100x EC₅₀) of selected β₂AR agonists and furimazine for 15 min prior to addition to the plate. TS-SNAP-β₂AR membranes were added to the plate offline and mixed with the Venus-mini-G_s on a plate shaker (MixMate, Eppendorf) at 600 RPM for 10 s. The mixture was then immediately read on PHERAstar FSX as described above, with readings taken over a period of 240 min.

G_s-CASE activation assays

For G_s-CASE activation assays, a single population of T-REx™-293 stably expressing pcDNA4TO-TS-SNAP-β₂AR and CASE G_s was plated at 50,000 cells/well in 96-well plates, in a volume of 100 μL, and induced for 48 h with 1 μg/mL tetracycline at 37°C and 5% CO₂. Plates were washed once with 100 μL/well assay buffer (HBSS containing 10 mM HEPES, 0.1% BSA, and 0.1% ascorbic acid) prior to the addition of 90 μL/well of assay buffer containing 10 μM furimazine, diluted in assay buffer, to achieve a final concentration of 8 μM. The plates were incubated at 37°C and 5% CO₂ for 20 min. A white back seal was placed on the underside of the plate, and luminescence was read on a PHERAstar FSX using the BRET1 module for 3 min to establish a baseline BRET signal. The plate reader was then paused, and 10 μL of ×10 ligand dilutions were added accordingly. Readings were taken over a period of 30 min.

Mathematical modelling

The previously described ordinary differential model (ODE) of the cubic ternary complex model (Weiss et al., 1996), with additional reactions to simulate the G protein activation cycle, was used (Woodroffe et al., 2009; Bridge et al., 2018). The model, encoded in COPASI (Hoops et al., 2006), includes ligand binding, receptor activation, G protein binding, and the G protein cycle, whereby the model output is activated G protein Gα_GTP and receptor occupancy (Bridge et al., 2018). Prior to the addition of the ligand, we first compute the system for 10⁶ s. To enable the simulation of the data, the cooperativity factor β (see Supplementary Figure 7; Supplementary Table 3) was varied, and simulations were performed. Steady state was reached after 5 min, and outputs are shown after 10 min.

Data analysis

All non-linear regression and statistical analyses were performed using GraphPad Prism 9. Multiple replicates were combined, such as TR-FRET equilibrium binding curves and mini-G_s equilibrium recruitment curves, as shown in Supplementary Material. Data points for each replicate were normalised to the maximum value obtained for each ligand in each experiment. Competition ligand-binding data were fitted to a one-site model (Equation 1).

$Y=\frac{Bottom+\left(Top-Bottom\right)}{\left(1+{10}^{\left(x-{LogIC}_{50}\right)}\right)},(1)$

where Y is the binding of tracer, x = Log [ligand], IC₅₀ is the concentration of the competing ligand that displaces 50% of radioligand-specific binding.

CASE G_s activation data from individual experiments were fitted to sigmoidal (variable slope) curves using a “four-parameter logistic equation” (Equation 2):

$Y=Bottom+\frac{\left(Top-Bottom\right)}{1+{10}^{\left(logEC50-X\right)\ast Hillslope}},(2)$

where Bottom is the plateaus of the agonist concentration response curve and Top is the basal response (fixed to 1). LogEC₅₀ is the concentration of the agonist that produces a half-maximal effect, and the Hillslope is the unitless slope factor or Hillslope, which was fixed to −1.

Mini-G_s association data were fitted to a global fitting model (Equation 3) using GraphPad Prism 9.2 to simultaneously calculate k_on and k_off using the following equations, where k_obs equals the observed rate of association and L is the concentration of mini-G_s.

$K_{\mathrm{d}}=\frac{k_{\text{off}}}{k_{\text{on}}},$

$\mathrm{L}=\text{Hotnm}*1\mathrm{e}-9,$

$K_{\text{ob}}=k_{\text{on}}*\mathrm{L}+k_{\text{off}},$

$\text{Occupancy}=\mathrm{L}/\left(\mathrm{L}+K_{\mathrm{d}}\right),$

${\mathrm{Y}}_{\max }=\text{Occupancy}*{\mathrm{B}}_{\max },$

$\text{drift}={\mathrm{B}}_{\max }*\exp \left(-\text{drift}*\mathrm{X}\right),$

$\mathrm{Y}=\left({\mathrm{Y}}_{\max }*\left(1-\exp \left(-1*k_{\text{ob}}*\mathrm{X}\right)\right)\right)*\text{drift}.(3)$

Saturation binding curves for Venus-mini-G_s binding to the agonist TS-SNAP-β₂AR-nLuc were fitted to a one-site specific binding model according to Equation 4. The final K_d values were taken as an average of K_d values from individual specific curve fits.

$\mathit{Y}=\frac{Bmax\ast X}{\left(K_d+X\right)},(4)$

where Y is the specific binding, K_d is the equilibrium dissociation constant of the labelled ligand (in this case, Venus-mini-G_s), and x represents [Venus-mini-G_s] in nM.

Statistical analysis

Pearson’s correlation coefficient was used to investigate correlations between mini-G_s recruitment, CASE-G_s activation, mini-G_s binding K_d, k_on and k_off values, and literature pK_i/d. Deming regression was applied to determine the line of best fit while accounting for errors in observations on both the x- and y-axes. All statistical analyses were performed in GraphPad Prism 9, and p < 0.05 was considered statistically significant.

Results

Characterisation of β₂AR agonist efficacy for G_s activation

To produce a suitable dataset for analysis, we chose 12 β₂AR agonists anticipated to have a diverse range of efficacies, affinities, and ligand binding kinetics. We first characterised the efficacy of these compounds in activating the heterotrimeric G_s protein using a NanoBRET-based biosensor (Schihada et al., 2021; Harwood et al., 2024). In this assay format, G_s protein activation results in a decrease in the NanoBRET signal as the nLuc-labelled α-subunit of the G_s protein dissociates from the Venus-labelled γ-subunit. These experiments are summarised in Figures 1A–C and Table 1.

Figure 1

Figure 1. T-REx^TM-293-SNAP-β₂AR G_s-CASE activation assay. (A) Concentration-response curves are shown for the full agonist formoterol and the partial agonist tulobuterol. The Gs-CASE baseline BRET signal was set to 1 for normalisation purposes. The response to each agonist is expressed as a fractional change relative to the basal response. Response data are representative of three or more experiments. (B) Gs-CASE pEC₅₀ and (C) E_max values are shown for the 12 agonists. Data are presented as the mean ± SEM of three or more experiments.

Table 1

Table 1. Summary of efficacy and potency values obtained for β₂AR agonists in the G_s-CASE activation assay.

The Gs-CASE assay functions as a non-amplified system, showing very distinct differences in measurable efficacy between full and partial agonists. The concentration-response curves for formoterol (full) and tulobuterol (partial agonist) are shown in Figure 1A. A broad range of potencies was observed for the 12 tested ligands, with pEC₅₀ values ranging from 6.49 ± 0.48 for dobutamine to 8.69 ± 0.18 for isoprenaline (see Figure 1B; Table 1). Figure 1C shows a range of efficacy values for each agonist, represented by E_max (maximal decrease in basal BRET) values, with the lowest efficacy agonists being tulobuterol and isoxsuprine and the highest being formoterol and Compound 26.

Validation of mini-G_s proteins as tools for probing G_s protein binding

In order to investigate the mechanism underlying the differences in efficacy, we expressed and purified fluorescently labelled mini-G_s proteins from E. coli (Supplementary Figure 1); our aim was to probe the affinity and binding kinetics of Venus-mini-G_s protein for the agonist-bound β₂AR–nLuc complex using NanoBRET. Figure 2A shows that all 12 agonists recruited Venus-mini-G_s protein to β₂AR-nLuc in HEK cell membranes in a concentration-dependent manner, with varying E_max and pEC_5o values (Table 2). Moreover, Figure 2B reveals a strong correlation (R² = 0.80, p = 0.0001) between E_max values for mini-G_s recruitment and E_max values for G_s-CASE activation, further validating these assays as effective tools for investigating β₂AR–G_s interactions.

Figure 2

Figure 2. β₂AR ligand efficacy determined in the Venus-mini-G_s recruitment assay. (A) Ligand-induced increases in BRET, following the recruitment of Venus-labelled mini-G_s to β₂AR-nLuc. The Venus-mini-G_s ligand-response amplitude of each agonist was compared to the maximal response of formoterol (1 μM). (B) Correlation of the Gs-CASE assay-response amplitude, expressed as the maximal Δ (E_max) in the baseline BRET signal, with E_max measured in the mini-Gs recruitment assay. Data are presented as the mean ± SEM of three experiments.

Table 2

Table 2. Summary of mini-Gs assay potency (pEC₅₀) and efficacy (E_max) values and literature pK_i values for the 12 agonists of varied efficacy under study.

Investigating the kinetics of mini-G_s protein binding to the β₂AR in complex with agonists of varying efficacies

We established a kinetic NanoBRET binding assay to measure Venus-mini-G_s protein recruitment to β₂AR-nLuc in membrane preparations. To achieve this, we pre-incubated receptor-containing membranes with a saturating concentration (×100 EC₅₀) of each β₂AR agonist, as characterised above. The pre-incubated membranes were then added to a plate containing various concentrations of Venus-mini-G_s protein, and we measured the association between these two proteins using NanoBRET (Figure 3; Table 3). Both association and dissociation rates (k_on and k_off) of Venus-mini-G_s for agonist β₂AR-nLuc could be obtained by analysing the observed association kinetics (Table 3). These studies showed that the full agonists, isoprenaline (k_on = 3.00 ± 0.1 × 10⁵ M⁻¹ min⁻¹) and adrenaline (k_on = 3.06 ± 0.15 × 10⁵ M⁻¹ min⁻¹), induce faster recruitment of the mini-G_s protein than the partial agonists, ritodrine (k_on = 6.13 ± 0.75 × 10⁴ M⁻¹ min⁻¹) and isoxsuprine (k_on = 4.97 ± 0.29 × 10⁴ M⁻¹ min⁻¹). k_off values were similar for all ligands, with all values within the range of 0.0070–0.0113 min⁻¹. We also conducted these mini-G_s kinetics studies on β₂AR-nLuc extracted into DDM detergent micelles, using 6 of the 12 ligands (Supplementary Figure 2; Supplementary Table 1) and observed similar results.

Figure 3

Figure 3. Kinetics of the association of Venus-mini-G_s to the agonist β₂AR–nLuc complex, as measured using nanoBRET. Recruitment of the mini-G_s protein by (A) formoterol, (B) salbutamol, (C) salmeterol, (D) Compound 26, (E) BI167107, (F) tulobuterol, (G) ritodrine, (H) isoxsuprine, (I) isoprenaline, (J) noradrenaline, (K) dobutamine, and (L) adrenaline. Data are presented as the mean ± SEM of three experiments.

Table 3

Table 3. Summary of mean k_off, k_on, and K_d values for purified Venus-mini-G_s recruitment to TS-SNAP-β₂AR-nLuc by various β₂AR agonists; NanoBRET between TS-SNAP-β₂AR-nLuc and Venus-mini-G_s read on PHERAstar FSX, at room temperature, using the BRET1 module.

To probe the binding affinity of the Venus mini-G_s protein to the agonist β₂AR–nLuc complex, we added ligands in excess (×100 reported pEC₅₀ determined in the mini-G_s recruitment assay, see above) and incubated with the membrane fraction expressing β₂AR-nLuc for 15 min prior to the addition of Venus-labelled mini-G_s (Figure 4). The resulting affinity (pK_d) values are summarised in Table 3, which ranged from 24 nM for the full agonist isoprenaline to 193 nM for the partial agonist isoxsuprine. These data also showed a difference in the maximum amount of mini-G_s protein (E_max) recruited over the limited concentration range studied (300–10 nM), with full agonists exhibiting higher recruitment compared to partial agonists.

Figure 4

Figure 4. Equilibrium binding of Venus-mini-G_s bound to β₂AR-nLuc at saturating concentrations of each agonist, as reflected by an increase in the BRET signal. The full agonist β₂AR complexes recruit Venus-mini-G_s with higher affinity compared to partial agonist β₂AR mG complexes. Data shown are presented as the mean ± SEM of three experiments.

Affinity and the rate of association of Venus-mini-Gs protein for β₂AR-nLuc correlated with agonist efficacy

Finally, we performed Pearson’s correlation analysis between both the association rates (k_on) and dissociation rates (k_off) and the affinity (pK_d) values for Venus-mini-Gs binding agonist β₂AR–nLuc complexes vs. agonist efficacy, comparing both G_s-CASE and mini-G_s assay E_max values (Figure 5). This analysis showed a strong correlation between ligand efficacy (E_max) measured in both assay formats and mini-G_s association rates (k_on) (R² = 0.78, p < 0.0001 and R²= 0.99, p < 0.0001 respectively; see Figures 5A, D) and between ligand efficacy (E_max) and mini-G_s affinity (pK_d) (R² 0.70, p = 0.0007 and R² = 0.93, p < 0.0001, respectively; see Figures 5C, F). This suggests that the differences in agonist efficacy can be explained by agonist β₂AR complexes’ ability to recruit the G_s protein. No correlation was observed between ligand efficacy (E_max) measured in either assay formats and mini-G_s dissociation rates (k_off) (R² = 0.06, p = 0.45 and R² = 0.16, p = 0.20, respectively; see Figures 5B, E).

Figure 5

Figure 5. Correlation plots of G_s-CASE activation E_max with (A) agonist β₂AR complex Venus-mini-G_s association rate (k_on), (B) β₂AR complex Venus-mini-G_s affinity (pK_d), and (C) β₂AR complex Venus-mini-G_s dissociation rate (k_off). The GS-CASE response to each agonist is expressed as a fractional change relative to the basal response. Correlation plots of mini-G_s assay E_max with (D) agonist β₂AR complex Venus-mini-G_s association rate (k_on), (E) β₂AR complex Venus-mini-G_s affinity (pK_d), and (F) β₂AR complex Venus-mini-G_s dissociation rate (k_off). The Venus-labelled mini-G_s ligand-response amplitude of each agonist in the mini-G_s assay was compared to the maximal response of formoterol (1 μM). Deming regression was applied to determine the line of best fit. Data are shown as the mean ± SEM of three experiments.

We also performed this same correlation analysis between these mini-G_s kinetics values obtained in detergent micelles and G_s efficacy data obtained in the Gs-CASE assay and found a similar trend (Supplementary Figure 4).

Efficacy of β₂AR agonists does not correlate with ligand binding kinetics

Previous studies have suggested that for some GPCRs, there is a relationship between ligand efficacy and the dissociation rates of ligand binding (Sykes et al., 2009a; Guo et al., 2016). To investigate the correlations between ligand residence time and efficacy, we analyzed existing kinetic data. This analysis revealed a broad range of measured k_off values, with adrenaline exhibiting the fastest dissociation rate and Compound 26 showing the slowest.

The relationships between agonist efficacy, as determined by E_max values obtained from the Gs-CASE and mini-G_s recruitment assays, and literature ligand binding association (k_on) and dissociation rates (k_off) were determined using Pearson’s correlation analysis (see Figure 6). This analysis showed no statistically significant correlation between ligand k_off values and the efficacy values determined for 6 of the 12 β₂AR agonists. Moreover, we also conducted kinetic TR-FRET-based ligand binding studies on 6 of the 12 β₂AR agonists in detergent micelles (Supplementary Figure 5) and found no statistically significant correlation (R² = 0.26, p = 0.29) between relative ligand residence times (IC₅₀ 1 min/IC₅₀ equilibrium) and their efficacy (Supplementary Figure 6; Supplementary Table 2).

Figure 6

Figure 6. Correlation plots of ligand efficacy with ligand binding parameters. Plot of (A) ligand dissociation rates (k_off) with G_s-CASE activation E_max and (B) ligand association rate (k_on) with G_s-CASE activation E_max. The Gs-CASE assay response amplitude is expressed as the maximal Δ (E_max) in the baseline BRET signal, which was set to 1.0. Plot of (C) ligand dissociation rates (k_off) with mini-G_s assay E_max and (D) ligand association rate (k_on) with mini-G_s assay E_max. The Venus-labelled mini-G_s ligand-response amplitude of each agonist in the mini-G_s assay was compared to the maximal response of formoterol (1 μM). Deming regression was applied to determine the line of best fit. Data are shown as the mean ± SEM of three experiments. Ligand association and dissociation rates were taken from Sykes et al., 2014, Sykes et al., 2012 and Rosethorne et al., 2016.

Discussion

In this study, we aimed to investigate the molecular basis for ligand efficacy. The first hypothesis was that the ligand binding kinetics, or ligand residence time, may influence efficacy. The second hypothesis was that the kinetics of G protein recruitment to the receptor–agonist complex may be correlated to ligand efficacy.

We found no correlations between the ligand binding kinetics and its efficacy. Whilst some studies suggested a role for ligand dissociation kinetics (Guo et al., 2012; Sykes et al., 2009) for adenosine A2_A and muscarinic M₃ receptors, our data are congruent with the previously reported observation that it was not the case for β₂AR (Sykes and Charlton, 2012).

Alternatively, we observed a linear correlation between ligand-induced differences in mini-G_s protein binding kinetics (k_on) and affinity (pK_d) for the agonist-bound β₂AR and agonist efficacy, the ability of a ligand to activate the heterotrimeric G_s protein. In contrast, our data showed minimal difference in the dissociation rate (k_off) or corresponding residence time (1/k_off) of the Venus-mini-G_s when binding to different agonist-β₂AR complexes. Since the affinity of mini-G_s is a ratio of k_on and k_off and mini-G_s k_off appears relatively constant, it is expected that mini-G_s k_on and its affinity correlate.

Our hypothesis is that agonist binding to the β₂AR increases the propensity for G protein recruitment, which underlies the molecular basis of ligand efficacy at the β₂AR (Figure 7A). To support our hypothesis, we applied a previously validated mathematical model of the cubic ternary complex model (BioModels ID:2306220001) to investigate the effect of increasing the forward rate of G protein binding to the activated receptor, on both G protein activation and agonist–receptor occupancy at the β₂AR (Figures 7B, C). As indicated, increasing the on-rate for G protein recruitment increases the efficacy and potency of G protein activation by the ligand, without changing agonist–receptor occupancy (Figure 7B). This, therefore, supports our hypothesis that an increase in G protein recruitment propensity underlies the molecular basis of ligand efficacy at the β₂AR.

Figure 7

Figure 7. Conformational model of efficacy proposed by this study: (A) agonists of higher efficacy induce a conformation of β₂AR that is more likely to recruit a mini-G_s protein, but once bound, there is no difference in the β₂AR conformation within the agonist β₂AR-mini–G_s complex. (B) Use of the cubic ternary complex model to investigate the effect of increasing the rate of G protein recruitment on the potency of the agonist–receptor complex to activate the G protein. Arrow indicates increases in apparent ligand EC₅₀ values for the formation of GαGTP. (C) Use of the cubic ternary complex model to investigate the effect of increasing the rate of G protein recruitment on agonist affinity for the GPCR. Dotted line indicates log (K_d) of ligand-receptor occupancy. As shown in the figure, the association rate of Gα to the receptor does not affect ligand binding affinity; hence, the yellow and blue curves lie directly on top of each other.

These differences in the rate of mini-G_s recruitment and the resulting differences in mini-G_s affinity suggest that subtle differences in agonist β₂AR complex conformations result in differences in agonist efficacy due of differences in the ability of these conformations to affect the recruitment of Venus-mini-G_s. As the dissociation rates of the mini-G_s protein are very similar for all ligands, the structure of the GPCR–G protein complex is likely similar for all ligands. This hypothesis aligns with recent observations made by NMR (Jones et al., 2024), where the full agonist isoprenaline induced a different conformational state of the β₁ adrenergic receptor (β₁AR) compared to the partial agonists xamoterol and salbutamol. However, the conformations were similar in the case of the ternary complex with mini-G_s. The authors also reported faster recruitment kinetics for the full agonist isoprenaline, a result that aligns well with our own observations for a wide range of partial and full agonists, as presented in this study.

Moreover, this conformational model (see Figure 7) is supported by data from hydrogen/deuterium exchange mass spectrometry (HDMS) and hydroxy radical foot printing mass spectrometry (HDX) (Du et al., 2019), where the conformational changes involved in β₂AR–G_s protein complex formation were investigated. Du et al. showed that the conformation of the initial β₂AR–G_s structure differs from that of the fully formed nucleotide free β₂AR–G_s complex. Furthermore, NMR studies (Nygaard et al., 2013; Manglik et al., 2015) show that the agonist BI-167-107 alone is not sufficient to fully stabilise the β₂AR in the active state and that nanobody 80 is required to fully stabilise the active state. These data support our findings that the conformation of the agonist β₂AR complex differs from that of the agonist β₂AR–mini-G_s complex. Moreover, Liu et al. (2012) investigated the conformational states of β₂AR bound to agonists of a range of efficacies and showed efficacy-dependent differences in the agonist β₂AR conformational state. Structural studies of the agonist-bound β₂AR or other class A GPCRs have only been possible in the presence of a G protein mimetics (Rasmussen et al., 2011b) and show only very small conformational differences that do not seem to explain differences in efficacy (Katritch et al., 2009). This further supports our finding that there was no difference in the agonist β₂AR–mini-G_s complex conformation.

We performed the majority of this study in membranes as we believe this environment is the most physiologically relevant for performing a kinetic analysis of ligand-induced mini-G_s binding. We also reproduced most of the experiments with receptor isolated in DDM detergent micelles (Supplementary Figures 2–6); this approach gives us confidence that our conclusions are relevant purely at the biophysical level, independent of the regulatory elements of the cell, while also establishing a baseline for future biophysical studies. However, the full applicability of our findings to the native cell environment remains to be fully elucidated. Interestingly, Sungkaworn et al. (2017) investigated the association rate (k_on) and dissociation rate (k_off) of Gα_I binding to the α2AR receptor in CHO cells in response to a range of agonists using single molecule microscopy. They showed that efficacy is at least partially correlated with k_on but not k_off of the Gα_I protein. Taken together with the evidence from the current study, this suggests that the conformational model of efficacy proposed may extend to the cellular environment. Future work will investigate whether this model of efficacy proposed is relevant to the β₂AR in its native cellular environment and whether this model can be generalized as a mechanism for agonist efficacy at other GPCRs.

Conclusion

In summary, these findings suggest that differences in initial agonist-GPCR conformations, where full agonists stabilise a state that readily recruits G protein, could be central to understanding the molecular basis of efficacy for the 12 β₂AR agonists studied. In contrast, we found no evidence linking ligand or G protein binding dissociation kinetics to the molecular basis of ligand efficacy at the β₂AR. We propose a conformational model of efficacy, in which agonists with higher efficacy stabilise a conformation of β₂AR that is more likely to recruit the G protein. The results from mini-G protein association experiments with ligand prebound to the receptor provide a convenient and direct measurement of ligand efficacy. Further studies incorporating a broader range of agonists with varying efficacies, along with measurements across different receptor types, would help determine whether this mechanism is a general feature of GPCR efficacy.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical approval was not required for the studies on humans in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used. Ethical approval was not required for the studies on animals in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used.

Author contributions

CH. DS: formal analysis and writing–review and editing. TR-N: investigation, methodology, resources, writing–original draft, and writing–review and editing. OU, CN, AK, EK, and GL: conceptualization, supervision, writing–original draft, and writing–review and editing. SB: conceptualization, funding acquisition, supervision, writing–original draft, and writing–review and editing. DV: conceptualization, funding acquisition, resources, supervision, writing–original draft, and writing–review and editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. CH was funded by a Medical Research Council (MRC) IMPACT PhD studentship. TR-N was funded by a UK Biotechnology and Biological Sciences Research Council iCase studentship (BB/V509334/1) co-funded with AstraZeneca. AK is funded by the BBSRC Doctorate Training Program at the University of Nottingham. GL is a Royal Society Industry Fellow (NF\R2\212001). EK, CN, DS and DV gratefully acknowledge funding by the Medical Research Council [grant number MR/Y003667/1] and Biotechnology and Biological Sciences Research Council [BB/Y51407X/1] and [BB/Z514500/1].

Acknowledgments

The authors would like to thank Dr. Bradley Hoare and Dr. Franziska M. Heydenreich for, respectively, generating the pcDNA4TO-TS-SNAP-β₂AR-nLuc and pcDNA4TO-TS-SNAP-β₂AR and Nevin Lambert for sharing the templates for Venus-mG plasmids used in this study.

Conflict of interest

DS and DV are founding directors of Z7 Biotech Ltd., an early-stage drug discovery company. OU is an employee of Z7 Biotech Ltd.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2025.1367991/full#supplementary-material

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