Rotate the model, follow the molecular switch, then change the conditions. Watch how receptor occupancy, G-protein activation, and cellular signals differ.
β₂-adrenoceptor · Gs
Camera controls (keyboard accessible)
The cycle illustrates one molecular event; readouts and curves represent a receptor population under the selected conditions.
Concentration–response
Predicted steady stateSignal over exposure time
Play records the transition from the no-ligand baseline toward the predicted level. Time and kinetic constants are illustrative. The trace retains its history when conditions change.
What every GPCR cycle has in common
A seven-transmembrane receptor changes conformation and acts as a guanine nucleotide exchange factor. GDP leaves Gα; a new GTP molecule binds. Gα–GTP and Gβγ can regulate effectors. Gα hydrolyses GTP to GDP + Pi, favouring reassembly.
Remember: GDP is exchanged, not phosphorylated on Gα. Gβ and Gγ stay together. “Inhibitory” Gi describes selected molecular targets, not every physiological effect of a receptor.
Compare coupling families and receptor examples
| Family | Canonical pathway | Examples | Teaching distinction |
|---|---|---|---|
| Gs | Adenylyl cyclase ↑ → cAMP ↑ → PKA | β₁, β₂, D₁, H₂, V₂ | Response depends on the cell: cardiac stimulation and airway relaxation can both involve cAMP. |
| Gi/o | Giα → AC inhibition; Gβγ → ion-channel regulation | M₂, D₂, α₂, μ-opioid | Giα inhibits susceptible AC isoforms. Go and Gβγ prominently regulate channels; not every Gi/o protein has identical effectors. |
| Gq/11 | PLCβ → PIP₂ cleavage → IP₃ + DAG → Ca²⁺ / PKC | M₁/M₃/M₅, α₁, H₁, V₁ | IP₃ releases ER calcium. DAG remains in the membrane; conventional PKC uses DAG and Ca²⁺. |
| G12/13 | RhoGEF → RhoA–GTP → ROCK | LPA receptors, PAR₁ (among others) | Controls cytoskeletal and contractile responses. These receptors may also couple to other families. |
Predict first. Change one factor. Explain the result.
1 · Can more agonist overcome a competitive antagonist?
Load the scenario. Increase the agonist from 1 to 1,000 × Kd. Compare the two concentration–response curves.
2 · Why is an inverse agonist different from a neutral antagonist?
Load the inverse-agonist scenario with constitutive activity. Switch the test ligand to neutral antagonist, keeping concentration fixed.
3 · Both toxins can raise cAMP. Are their mechanisms the same?
Compare the cholera and pertussis scenarios. Watch what happens to GDP–GTP exchange and signal termination.
4 · Same agonist, weaker signal over time. Why?
Load prolonged exposure and play for 20–30 teaching time units. Wash out ligands, keep playing, then reapply the agonist by unchecking “No test ligand”.
5 · Does Gq produce calcium from PIP₂?
Load Gq and step through effector activation. Identify where IP₃ travels and where DAG stays.
How to interpret this model
This is an offline, mechanistic teaching simulation for third-year PharmD students. The rotatable scene uses three-dimensional coordinates and perspective projection. It is a stylized class-A-like receptor, not a crystallographic structure or a molecular dynamics simulation. The binding pocket and TM6 opening illustrate principles; individual receptors differ.
- Families here refer to G-protein coupling, not GPCR structural classes A, B, C, etc. The examples emphasize canonical coupling; real receptors may engage multiple transducers.
- The representative molecule illustrates a possible event. Low occupancy reduces the population signal, not the size of an individual ligand. Neutral and inverse ligands do not run an agonist activation sequence. Basal receptor activity can still produce a population signal.
- Gα–GTP and Gβγ are drawn separated for clarity. Activation can also involve rearrangement without complete physical dissociation. Their membrane association is retained in the scene.
- β-arrestin can scaffold signalling as well as reduce G-protein coupling. Biased agonism, compartmental signalling, ion-channel currents, receptor internalization/recycling, calcium oscillations and drug-specific kinetics are not quantitatively simulated.
- No real drug affinity, clinical dose, tissue concentration, or patient response is predicted. Clinical examples provide context only. Ligand efficacy is a generic experimental setting, not an assigned efficacy for every drug in the example text.
Transparent equations
Let A = test-ligand concentration / Kd; B = competing neutral antagonist concentration / Ki; b = constitutive receptor activity; e = test-ligand efficacy (full = 1, partial = 0.4, neutral = b, inverse = 0). The model uses a single reversible competitive binding site.
Receptor drive R = b × (1 − θA) + e × θA
Coupling input u = R × (surface capacity / 100) × S
Active G fraction g = u / (u + 0.25 × h)
S is coupling competence (initially 1). h is 1 by default, or the RGS factor for Gi/o and Gq/11. With desensitization on: dS/dt = 0.025(1 − S) − 0.10 × max(0, R − b) × S. Turning it off restores S to 1. “Reset exposure” also restores S and clears elapsed time. Washout sets A = B = 0 while preserving S.
Gi cAMP = (1 − 0.8g) / (1 − PDE inhibition fraction)
Gq cytosolic Ca²⁺ proxy = 1 + 4g
G12/13 RhoA activity proxy = 100g (%)
cAMP is relative to basal AC production / uninhibited PDE clearance (1×); this is not necessarily the no-ligand value when constitutive receptor activity is present. The Ca²⁺ baseline is 1×. Gq and RhoA outputs are illustrative monotonic proxies, not mechanistic calcium or Rho cycling equations. Kd is not assumed equal to EC₅₀; the saturating transduction step permits receptor reserve.
Cholera toxin sets h = 0.015 for Gs and adds a receptor-independent activation floor of 0.03 to u, representing an established toxin effect. It remains after ligand washout. Pertussis toxin sets g = 0 for the susceptible Gi/o pathway. Toxins selected on other families have no effect in this model. RGS controls are restricted to the Gi/o and Gq branches. PDE inhibition only alters the cAMP branches.
Controls show immediate steady-state predictions. The time trace starts from the no-ligand baseline for the selected cellular factors, then approaches that prediction using a first-order lag of 2 teaching time units (longer with PDE inhibition). Cycle animation, lag, toxin constants and desensitization rates are chosen for teaching; none are fitted experimental parameters. The comparator uses the same cell state and test ligand, with B = 0. Family or scenario changes clear the trace to avoid mixing different quantities.
Scientific anchors & further reading
- Rasmussen et al. (2011). Crystal structure of the β₂-adrenergic receptor–Gs protein complex. Nature; PDB 3SN6. Structural basis for activation, nucleotide-free coupling and cytoplasmic TM6 movement. The scene is not generated from these atomic coordinates.
- Lohse et al. (1990). β-Arrestin: a protein that regulates β-adrenergic receptor function. Science. Foundational experimental work on receptor desensitization.
- Gavard & Gutkind (2008). Gα12/13, Gαq/11 and Rho-dependent endothelial responses. Journal of Biological Chemistry. Experimental example of overlapping GPCR coupling and cytoskeletal pathways.
- IUPHAR/BPS Guide to Pharmacology. Receptor-specific nomenclature and pharmacology; access may require login.
Built September 2026. All calculations and graphics run locally. No external libraries, accounts, API keys or internet connection are needed; opening reference links requires internet access.