Explore when the familiar hyperbolic binding curve is a good shortcut, and when ligand depletion makes the exact quadratic model matter.
Set the true affinity, protein concentration, and ligand range. Concentrations are shown in actual µM.
Kd, protein, and ligand sliders move on a log10 scale. Concentrations are entered and plotted in µM.
When protein concentration is tiny compared with Kd, free ligand is nearly the same as total ligand, so the shortcut behaves well.
Ligand point spacing controls where simulated concentrations are sampled between 0 and ligand [B]o. Simulated data are generated only from the exact quadratic binding equation.
Use presets to jump between ligand-excess, moderate-depletion, and tight-binding scenarios.
Compare the exact quadratic model with the simple hyperbolic approximation.
Individual points show every replicate. Mean mode summarizes replicates at each ligand concentration and can show SD or SEM.
Both fits use the same simulated data. The comparison shows how model choice can shift the apparent Kd.
These controls change how simulated data are displayed on the fit plot. The nonlinear fit still uses every replicate.
| Model | Kd ± SE | Ymax ± SE | SSE | R2 | Score |
|---|---|---|---|---|---|
| True value | 0.100 | 1.000 | - | - | 100 |
| Quadratic fit | - | - | - | - | - |
| Hyperbolic fit | - | - | - | - | - |
Switch to simulated data mode to generate noisy measurements and fit them. SSE means sum of squared errors; smaller SSE means the fitted curve stays closer to the simulated data points overall.