Problems associated with analysis and interpretation of small molecule/macromolecule binding data.
In the analysis of binding data, arbitrary transformations such as the Scatchard plot, may give misleading estimates of the binding parameters. The statistically correct approach is to determine values of K and n by non-linear regression of the actual dependent variable against the actual independent variable. In the case of the spectrophotometric titration method the dependent variable is the absorbance and the independent variable is the composition of the drug/macromolecule mixture. The method relies on an accurate estimate of the extinction coefficient of the bound drug and this is best treated as a parameter to be estimated in the regression analysis. In testing models by data fits alone it is emphasized that whilst a model may be rejected if it does not fit the data, a good fit does not ensure uniquieness and confirmatory, independent evidence must be sought.
Problems associated with analysis and interpretation of small molecule/macromolecule binding data. In the analysis of binding data, arbitrary transformations such as the Scatchard plot, may give misleading estimates of the binding parameters. The statistically correct approach is to determine values of K and n by non-linear regression of the actual dependent variable against the actual independent variable. In the case of the spectrophotometric titration method the dependent variable is the absorbance and the independent variable is the composition of the drug/macromolecule mixture. The method relies on an accurate estimate of the extinction coefficient of the bound drug and this is best treated as a parameter to be estimated in the regression analysis. In testing models by data fits alone it is emphasized that whilst a model may be rejected if it does not fit the data, a good fit does not ensure uniquieness and confirmatory, independent evidence must be sought.