HOMOGENEITY TESTS FOR MICHAELIS–MENTEN CURVES WITH APPLICATION TO FLUORESCENCE RESONANCE ENERGY TRANSFER DATA

Abstract

Resonance energy transfer methods are widely used for evaluating protein–protein interactions and protein conformational changes. Sensitized emission fluorescence resonance energy transfer (FRET) measures energy transfer as a function of the acceptor-to-donor ratio, generating FRET saturation curves. To reduce sampling variability effects, several replications (statistical samples) of the saturation curve are generated in the same biological conditions. Here we study procedures to determine whether these statistical samples are homogeneous, in the sense that they are extracted from the same underlying regression model (Michaelis–Menten kinetics). We used three methods to test the homogeneity of the samples: two hypothesis testing procedures (an F-test and bootstrap resampling) and model selection. The performance of the three methods was compared in a Monte Carlo study and through analysis in living cells of FRET saturation curves for dimeric CXCR4 complexes. This analysis shows that the F-test, the bootstrap procedure and the model selection method lead in general to similar conclusions, although the latter gave the best results when sample sizes were small, whereas the F-test and the bootstrap method were more appropriate for large samples. In practice, all three methods are easy to use simultaneously and show consistency.