Curve fitting

Curve fitting

After generating a nice sensorgram, the fitting can be done. The first and only model you can use is the 1:1 Langmuir interaction describing the single exponential of your data. Start by filling in the initial fitting values and press “fit” to see the result. If your sensorgram consist of nice curves the fit will be done in seconds, follow your curves and have unique values.

Fitting curves
1:1 interaction fitting
Fitted values
Fitted values

More realistic, you will have to optimize initial values and adjust the fitting ranges. This is normal and it is a good idea to fit the sensorgram with several initial values to make sure that the answer gives unique values.

Next step is to compare critically the fitting with the measured curves. Does the fitting follow the curves? Are the calculated buffer jump values in line with the curves? Are the values of the parameters (Rmax, ka, kd) possible?

Large RI jumps
1:1 fitting with large RI jumps

As you can see in the figure, the fit is not following the curves during the dissociation. By adding a larger buffer jump the program attempts to make the fit better. One other thing immediately apparent is that the overall response is too high. A way to solve this is to immobilize less ligand and repeat the experiment.

Other small problems, like small buffer jumps or low baseline drift can be solved with subtracting reference channels and blank (buffer only) injections (double referencing).

To validate further the interaction, several more steps are necessary. For instance, reverse the ligand and analyte and perform the experiments again. If you used the kinetic protocol, try an equilibrium experiment. Repeat the experiment with different batches of the same reagents to identify batch-to-batch variation. In all cases, a same value for kinetic constants is expected.