Chi2 is a measure of the average deviation of the experimental data to form the fitted curve. Lower chi2 values indicate a better fit, but assessment of chi2 must be taken in the context of the binding level of the system. Basically the further away the fit is from your original data, the higher the Chi2 value will be and vice versa. As the Chi2 value is derived from the experimental data it’s hard to advise an exact number, but as a general rule of thumb ≤10% Rmax.
So using the data in the table above we have an average Rmax of 23.5 RU and therefore, we should ‘accept’ a Chi2 of less than 2.35. Out actual Chi2 is 0.00389 so we are much lower than that, so that gives us confidence in the fit.
One of my favourite values for confidence in data is T-values. These provide nice and simple feedback on the individual fit parameters. To determine these, you need the standard error (SE), which is generated for you by the BiacoreTM software. For SE you’re looking for a small a number as possible because a small SE indicates that changes in the parameter’s value would have a significant effect on the fitting (i.e., the confidence value is high).
To calculate T-values you simply divide the parameter’s value by the SE. The larger the number the better for T-values. My rule of thumb is:
- 10 = good
- 100 = great
- 1,000 = happy days
So the T-values of 251.3, 705.0 and 1,023.1 for T(ka), T(kd) and T(Rmax) give me increased confidence in the data.