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Resonance energy transfer methods are in wide use for evaluatingprotein-protein interactions and protein conformational changes in livingcells. Fluorescence resonance energy transfer (FRET) measures energy transferas a function of the acceptor:donor ratio, generating FRET saturation curves.Modeling these curves by Michaelis-Menten kinetics allows characterization bytwo parameters, which serve to evaluate apparent affinity between two proteinsand to compare this affinity in different experimental conditions. To reducethe effect of sampling variability, several statistical samples of thesaturation curve are generated in the same biological conditions. Here we studythree procedures to determine whether statistical samples in a collection arehomogeneous, in the sense that they are extracted from the same regressionmodel. From the hypothesis testing viewpoint, we considered an F test and aprocedure based on bootstrap resampling. The third method analyzed the problemfrom the model selection viewpoint, and used the Akaike information criterion(AIC). Although we only considered the Michaelis-Menten model, all statisticalprocedures would be applicable to any other nonlinear regression model. Wecompared the performance of the homogeneity testing methods in a Monte Carlostudy and through analysis in living cells of FRET saturation curves fordimeric complexes of CXCR4, a seven-transmembrane receptor of the Gprotein-coupled receptor family. We show that the F test, the bootstrapprocedure and the model selection method lead in general to similarconclusions, although AIC gave the best results when sample sizes were small,whereas the F test and the bootstrap method were more appropriate for largesamples. In practice, all three methods are easy to use simultaneously and showconsistency, facilitating conclusions on sample homogeneity.

Homogeneity tests for Michaelis-Menten curves with application to fluorescence resonance energy transfer data