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

Abstract

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.

Evidence for Homodimerization of the c-Fos Transcription Factor in Live Cells Revealed by Fluorescence Microscopy and Computer Modeling

“…Other groups have used microscopic FRET to determine K d s in cells by utilizing prior estimates of protein copy numbers per cell (53) or applying in vitro concentration calibration with purified proteins (54). Here we present a method to calculate K d values based on FRET titrations after concentration calibration by FCS, where the whole procedure was carried out with live cells.…”

Quantitative analysis of FRET assay in biology—new developments in protein interaction affinity and protease kinetics determinations in the SUMOylation cascade

“…Mehta et al (2009) compared different methods of characterizing FRET efficiency and FRET index by a self-developed Monte Carlo simulation algorithm and a surface FRET system with controlled amounts of donor and acceptor fluorophores and controlled distances between them. They obtained conclusions of optimized donor-to-acceptor ratios of higher energy transfer efficiencies.…”

A new trend to determine biochemical parameters by quantitative FRET assays

“…Subsequently, several studies have been conducted to improve the process of imaging-based FRET assay for K d determination [17][18][19] . These improvements mainly focus on the mathematic algorithms, and all of them require that the FRET efficiency is first determined.…”

“…For example, Chen et al directly measured the FRET efficiency by photobleaching imaging, which did not require recombinant proteins to obtain standard curves [18] . Mehta et al developed a computational imaging method to eliminate imaging noise and include endogenous unlabeled proteins in the process of K d determination by FRET imaging [19] . However, the accurate determination of FRET efficiency is challenging because the outcome may depend on the method used to determine the FRET efficiency.…”