A computational approach to inferring cellular protein-binding affinities from quantitative fluorescence resonance energy transfer imaging.

Fluorescence resonance energy transfer (FRET) microscopy can measure the spatial distribution of protein interactions inside live cells. Such experiments give rise to complex data sets with many images of single cells, motivating data reduction and abstraction. In particular, determination of the value of the equilibrium dissociation constant (K(d)) will provide a quantitative measure of protein-protein interactions, which is essential to reconstructing cellular signaling networks. Here, we investigate the feasibility of using quantitative FRET imaging of live cells to estimate the local value of K(d) for two interacting labeled molecules. An algorithm is developed to infer the values of K(d) using the intensity of individual voxels of 3-D FRET microscopy images. The performance of our algorithm is investigated using synthetic test data, both in the absence and in the presence of endogenous (unlabeled) proteins. The influence of optical blurring caused by the microscope (confocal or wide field) and detection noise on the accuracy of K(d) inference is studied. We show that deconvolution of images followed by analysis of intensity data at local level can improve the estimate of K(d). Finally, the performance of this algorithm using cellular data on the interaction between yellow fluorescent protein-Rac and cyan fluorescent protein-PBD in mammalian cells is shown.