Fit competition curves with laboratory inputs and outputs. Estimate IC50, Ki, top, bottom, and cooperativity. Turn raw displacement readings into defensible analytical conclusions faster.
Paste concentration values in the first column and one or more responses after them. Each line may be comma-separated or tab-separated.
| Concentration (nM) | Replicate 1 | Replicate 2 | Replicate 3 | Mean Signal |
|---|---|---|---|---|
| 0.1 | 98.4 | 99.1 | 97.8 | 98.43 |
| 0.3 | 96.1 | 95.5 | 96.8 | 96.13 |
| 1 | 91.7 | 90.8 | 91.3 | 91.27 |
| 3 | 80.1 | 79.2 | 80.8 | 80.03 |
| 10 | 58.7 | 60.1 | 59.5 | 59.43 |
| 30 | 34.6 | 33.2 | 35.1 | 34.30 |
| 100 | 17.8 | 18.5 | 17.3 | 17.87 |
| 300 | 10.2 | 9.7 | 10.6 | 10.17 |
This calculator fits a descending four-parameter logistic displacement curve. It first estimates IC50 and Hill slope, then optimizes them by minimizing the weighted residual sum of squares. Top and bottom can be auto-fitted or locked manually.
Where Y is the predicted signal, C is competitor concentration, Top is signal without displacement, Bottom is residual signal at saturation, and Hill describes the steepness of the transition.
The Ki estimate uses the Cheng–Prusoff correction, where [L] is tracer concentration and Kd is tracer equilibrium dissociation constant. pIC50 and pKi are reported in molar units.
It fits a descending displacement or competition binding curve using a four-parameter logistic model. The tool estimates top, bottom, IC50, Hill slope, residuals, and optionally Ki.
Calculate Ki when you know tracer concentration and tracer Kd. The calculator applies the Cheng–Prusoff correction, which converts observed IC50 into an affinity estimate under competitive conditions.
Locking parameters helps when biological knowledge or assay controls define realistic limits. This can stabilize the fit, especially when your data do not fully reach upper or lower plateaus.
The Hill slope controls curve steepness. Values near one often indicate a standard competitive transition, while larger or smaller values can reflect cooperativity, assay artifacts, or data compression.
Use inverse variance weighting when replicate spread differs strongly between concentrations. It reduces the influence of noisy points and gives more emphasis to measurements with lower uncertainty.
This version assumes a descending bound-signal curve. If your assay reports inhibition or displacement percent as an increasing response, transform the signal before fitting or adapt the equation orientation.
Four points are the minimum for calculation, but eight or more concentrations are usually better. Good spacing across the full transition improves IC50, Hill slope, and plateau estimates.
R² measures how much response variation the fitted curve explains. RMSE shows the average prediction error in your response units, making it useful for judging practical fit quality.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.