1.

Introduction

Förster resonance energy transfer (FRET) is a physical process in which a donor fluorophore molecule in the excited state transfers energy nonradiatively to an acceptor molecule (often also a fluorophore) in the ground state.^{1, 2} FRET occurs over distances of molecular scale $\left(1\text{to}10\mathrm{nm}\right)$ and can thus be used to evaluate protein-protein interaction in living cells. The recent development of spectrally diverse fluorescent proteins (FP) that serve as genetically encoded donor and acceptor fluorophore tags has greatly facilitated FRET measurements in living cells.^{3, 4}

FRET is most appropriately quantified using the FRET efficiency, an instrument-independent index defined as the proportion of donor molecules that transfer excitation state energy to acceptor molecules.⁴ In living cells expressing proteins fused with FPs, FRET efficiency can be accurately determined using donor fluorescence lifetime or sensitized acceptor emission intensity.⁵ However, equating FRET efficiency with protein-protein interaction presents a challenge, as FRET efficiency is a function of multiple variables. The variables include the intracellular concentrations of donor and acceptor fluorophores (usually proteins fused to FPs), the affinity (i.e., the equilibrium dissociation constant $K_d$ ) of the interacting proteins, and the intrinsic FRET efficiency $\left(E_{\max }\right)$ of the donor∕acceptor pair. Moreover, FRET efficiency is not linearly related to these variables with the exception of $E_{\max }$ . Therefore, protein pairs with disparate $K_d$ values may display indistinguishable measured FRET efficiencies, while protein pairs with identical $K_d$ values may display different measured FRET efficiencies.⁴ Hence, the measured FRET efficiency per se is not an appropriate estimator of the protein-protein binding affinity.

Estimation of the binding affinity is an important goal of protein-protein interaction assays. Erickson, Alseikhan, and Peterson, and Erickson ^{6, 7} were the first to demonstrate the feasibility of using quantitative “three-cube” FRET measurements^{8, 9} to estimate the relative $K_d$ of two proteins tagged with genetically encoded fluorophores expressed in living cells. However, the methodology required determination of several parameters measured from purified donor and acceptor fluorescent proteins, and detailed knowledge of the optical filter characteristics (e.g., the average molar extinction coefficient of donor and acceptor fluorophores over the bandwidth of the FRET cube excitation filter).^{6, 7} Moreover, the equivalence of the fluorescent protein spectra determined in vitro and in vivo (i.e., a cellular context) was assumed.

Here, we describe a method to estimate the relative $K_d$ of two FP-tagged interacting proteins, which avoids these requirements and is performed using standard fluorescence microscopy and quantitative imaging techniques. The method was validated using an inducible FRET pair consisting of the FPs Cerulean and Venus fused to FKBP12 (a rapamycin-binding protein) and the Frb domain of FRAP1∕mTOR, respectively. Upon addition of rapamycin, a complex with defined structure, stoichiometry, and affinity forms.¹⁰

2.

Methods

In plasmid constructs used in this study, transcription was driven by the human cytomegalovirus promoter and the start codon was under the optimal context for translational initiation.¹¹ The constructs encoding monomeric (A206K mutation) Cerulean and Venus, variants of cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP); respectively, have been previously described.^{12, 13, 14, 15} The open reading frames (ORFs) encoding the rapamycin-binding protein FKBP12 (GenBank accession number U69485) and the Frb domain of FRAP1∕mTOR (GenBank accession number NM̱019906; residues 2018-2114) were amplified using the polymerase chain reaction from rat whole brain cDNA (Clontech, Mountain View, California), and cloned into mammalian expression vectors encoding Cerulean and Venus, respectively. The linker sequence, KLRILQSTVPRARDPPVAT, in FKBP12-Cerulean and Frb-Venus fusion constructs was identical. The W2101F mutation was introduced into the Frb domain using the QuikChange® method (Stratagene, La Jolla, California). ${\mathrm{Frb}}_{\mathrm{W}2101\mathrm{F}}$ was termed ${\mathrm{Frb}}_{\mathrm{KTF}}$ in the original description of the mutation.¹⁶

HeLa cells (ATCC, Manassas, Virginia) suspended in minimal essential medium containing 10% bovine calf serum were plated onto glass-bottom tissue culture dishes (MatTek, Ashland, Massachusetts) and stored in a humidified atmosphere containing 5% $\mathrm{C}{\mathrm{O}}_2$ in air at $37°\mathrm{C}$ . HeLa cells were transfected with plasmids using Lipofectamine 2000 (Invitrogen, Carlsbad, California) for approximately $24\mathrm{h}$ . Prior to image acquisition, culture medium was replaced with phosphate buffered saline (PBS) containing $1\mathrm{mM}$ $\mathrm{Ca}{\mathrm{Cl}}_2$ and $\mathrm{Mg}{\mathrm{Cl}}_2$ . To induce heterodimerization, rapamycin (Biomol, Plymouth Meeting, Pennsylvania) was added to the PBS ( $1\mu \mathrm{M}$ final concentration) at least $40\min$ prior to imaging.

The imaging procedure has been described.¹⁷ Briefly, an Olympus IX-71 inverted microscope equipped with a $75\text{‐}\mathrm{W}$ xenon arc lamp (Cairn Research, Kent, United Kingdom), a UNIBLITZ mechanical shutter (Vincent Associates, Rochester, New York), a $60\times$ oil immersion objective (NA 1.4), a CFP filter set ( $I_{DD}$ cube, the donor channel; excitation: $436\pm 10\mathrm{nm}$ , emitter: $480\pm 20\mathrm{nm}$ , dichroic: 455LP), a YFP filter set ( $I_{AA}$ cube, the acceptor channel; excitation: $500\pm 10\mathrm{nm}$ , emitter: $540\pm 15\mathrm{nm}$ , dichroic: 520LP), and a FRET filter set ( $I_{DA}$ cube, the FRET channel; excitation: $436\pm 10\mathrm{nm}$ , emitter: $540\pm 15\mathrm{nm}$ , dichroic: 455LP). Using a $12\text{‐}\text{bit}$ cooled charge-coupled device (CCD) camera (Retiga Exi, Qimaging, Burnaby, British Columbia, Canada) and a custom IGOR Pro (version 5, WaveMetrics, Lake Oswego, Oregon) based program, three fluorescence intensity images, i.e., $I_{DD}$ , $I_{DA}$ , and $I_{AA}$ images of HeLa cells coexpressing Venus (or Venus-tagged protein) and Cerulean (or Cerulean-tagged protein) were acquired using the $I_{DD}$ , $I_{DA}$ , and $I_{AA}$ filter cubes. Background fluorescence measured from untransfected cells was subtracted from all images. Images were acquired at room temperature $(22\text{to}23°\mathrm{C})$ with exposure times of $0.1\text{to}1.5\mathrm{s}$ , analog gain was set to 1, and binning set to $2\times 2$ . Fluorescence intensities were normalized to an exposure duration of $1\mathrm{s}$ . Due to the overlap of the excitation and emission spectra of Cerulean and Venus, the fluorescence intensity of the $I_{DD}$ , $I_{DA}$ , and $I_{AA}$ images had three potential sources: the donor fluorescence $\left(I_{dd}\right)$ , the sensitized acceptor emission $\left(F_c\right)$ due to FRET, and the direct acceptor emission $\left(I_{aa}\right)$ . To isolate these components in each pixel of intensity images, standard procedures and formulae have been employed.^{9, 18, 19} $I_{DD}$ , $I_{DA}$ , and $I_{AA}$ images of HeLa cells expressing only Venus were acquired, and two cross talk parameters were determined: $a=I_{DA}∕I_{AA}$ and $b=I_{DD}∕I_{AA}$ . In these experiments, $a=0.0335\pm 0.0003$ $(\mathrm{n}=11)$ and $b=0.0004\pm 0.0001$ $(\mathrm{n}=11)$ . $I_{DD}$ , $I_{DA}$ , and $I_{AA}$ images of HeLa cells expressing only Cerulean were acquired, and two cross talk parameters were determined: $d=I_{DA}∕I_{DD}$ and $c=I_{AA}∕I_{DD}$ . In these experiments, $c=0.0013\pm 0.0001$ $(\mathrm{n}=12)$ and $d=0.3769\pm 0.0005$ $(\mathrm{n}=12)$ . The pixel intensity of $I_{DD}$ , $I_{DA}$ , and $I_{AA}$ images in cells coexpressing Venus (or Venus-tagged protein) and Cerulean (or Cerulean-tagged protein) were characterized with the following equations:

Previously we have demonstrated that expressing two donor-acceptor fusion FPs with differing FRET efficiencies allows us to determine: 1. the ratio of sensitized acceptor emission to donor fluorescence quenching ( $G$ factor), and 2. the ratio of donor∕acceptor fluorescence intensity for equimolar concentrations in the absence of FRET ( $k$ factor).¹⁷ $G$ and $k$ factors are constant for given donor∕acceptor pairs and a particular imaging setup. Following determination of the $G$ and $k$ factors for the FPs Cerulean and Venus, the FRET efficiency $\left(E\right)$ and the concentration ratio $D∕A$ of donor (Cerulean or Cerulean-tagged protein) and acceptor (Venus or Venus-tagged protein) fluorophore can be determined:^{17, 18}

3.

Theory

Assuming a bimolecular interaction between donor and acceptor molecules, a formalism derived from receptor-ligand binding studies can be adopted, in which we equate the donor and acceptor molecules to receptor and ligand, respectively. In this study, the acceptor was not expressed in great excess relative to the donor, thus we did not assume that “free” acceptor concentration was approximated by total acceptor concentration (i.e., $A$ ). The situation is equivalent to binding experiments with ligand depletion:²⁰

We can determine $D$ , $A$ , and $E$ for cells expressing varying concentrations and ratios of interacting proteins fused to Cerulean and Venus [see Eqs. 7, 9, 10]. Treating $D$ and $A$ as independent variables, and $E$ as the dependent variable, the free parameters $K_d$ and $E_{\max }$ are adjusted to minimize the difference between the experimental and predicted results. Based on maximum likelihood analysis, the best estimates of $K_d$ and $E_{\max }$ result from using the sum of the squared residual error (SSE) as the figure-of-merit function:^{22, 23}

The remaining task was to determine a method for adjusting the parameters $K_d$ and $E_{\max }$ to find the minimum SSE $\left({\mathrm{SSE}}_{\min }\right)$ . Although various iterative algorithms exist for curve-fitting multivariate functions, we chose the simple, but computationally intense, approach of computing the SSE for each dataset from a large matrix of hypothetical $K_d$ and $E_{\max }$ values. The approach is feasible, because meaningful values of $K_d$ and $E_{\max }$ are constrained. For example, $E_{\max }$ must lie in the range 0 to 1 to be interpretable. Moreover, given the crystal structure of fluorescent proteins, we can restrict this range even more, as the beta barrel encapsulating the fluorophore residues prevents close apposition of the donor and acceptor fluorophores. Similarly, $K_d$ cannot be negative and the practical upper limit (that can be determined) is influenced by other factors (e.g., collisional FRET, see next). Computing the ${\mathrm{SSE}}_{\min }$ in this manner provides several advantages. First, since the error for a large parameter space is computed, the method is not susceptible to finding local minimums or dependent on initial parameter values. Second, the topology of the error space provides a visual clue as to parameter correlation. Third, the method facilitates generation of a “confidence interval contour” based on a critical value of SSE determined from the $F$ distribution.²² Projection of this elliptical contour provides estimates of parameter confidence intervals.

4.

Results

We tested our method of $K_d$ estimation using a well-characterized inducible protein-protein heterodimerization system.^{10, 16, 24} As shown schematically in Fig. 1a , the protein FKBP12 has a high affinity $(K_d=0.2\mathrm{nM})$ for the cell permeable immunosuppressant, rapamycin. Subsequently, the FKBP12-rapamycin complex can bind the Frb domain of the FRAP1∕mTOR protein with high affinity $(K_d=12\mathrm{nM})$ , thus bringing the C-termini of FKBP12 and the Frb domains into close apposition $(\sim 15\mathrm{\AA })$ . When expressed in HeLa cells, fluorescence of the fusion proteins FKBP12-Cerulean and Frb-Venus appeared distributed throughout the cytosolic and nuclear compartments (data not shown). As shown in Figs. 1b and 1c (left panels), $E$ was negligible ( $0.008\pm 0.002$ , $\mathrm{n}=120$ ) in HeLa cells coexpressing FKBP12-cerulean and Frb-Venus, consistent with the idea that FKBP12 does not bind the Frb domain in the absence of rapamycin.¹⁰ In a few cells expressing a high concentration of Frb-Venus ( $>5000$ arbitrary fluorescence units, AFU), however, $E$ was about 0.05. This was likely due to “collisional” FRET, as it also occurred when similar concentrations of Cerulean and Venus were expressed (data not shown). To avoid this issue, cells with Frb-Venus concentrations $>5000$ AFUs were excluded from further analysis. In addition, it has been reported that three-cube FRET measurements were prone to error when $D∕A$ fell outside the range 0.1 to 10; therefore only cells with $D∕A$ between 0.2 to 5 were chosen for further analysis.²⁵

Fig. 1

Rapamycin-induced FRET. (a) Schematic diagram representing the binding events involved in the formation of FKBP12-rapamycin-Frb ternary complex. Adapted from Ref. 10. (b) Representative images illustrating FRET efficiencies (displayed using a color scale) in HeLa cells coexpressing FKBP12-Cerulean and Frb-Venus. FRET efficiencies were determined on a pixel-by-pixel basis in the absence (left panel) or presence (right panel) of $1\mu \mathrm{M}$ rapamycin. Scale bar, $30\mu \mathrm{m}$ . (c) Ensemble data illustrate FRET efficiencies measured in the absence (left) or presence (right) of $1\mu \mathrm{M}$ rapamycin in HeLa cells coexpressing FKBP12-Cerulean and Frb-Venus. The mean pixel FRET efficiencies from individual cells (open circles) are plotted versus the relative acceptor (Frb-Venus) concentration, in arbitrary fluorescence units (AFU). (Color online only.)

Application of rapamycin to cells coexpressing FKBP12 and Frb induces formation of the FKBP12-rapamycin-Frb complex.^{10, 16} Consistent with this idea, $40\min$ following application of a saturating concentration of rapamycin $\left(1\mu \mathrm{M}\right)$ , we detected robust FRET [ $E=0.153\pm 0.004$ , $\mathrm{n}=248$ , Fig. 1b and 1c, right panels] in HeLa cells coexpressing FKBP12-Cerulean and Frb-Venus. Under these conditions, all molecules of FKBP12-Cerulean should be bound to rapamycin. Thus, the trimolecular system reduces to a bimolecular system with the donor∕receptor being the rapamycin-FKBP12-Cerulean complex, and the acceptor∕ligand being Frb-Venus, thereby satisfying the conditions of Eqs. 15, 17 that assume a bimolecular interaction.

To estimate the effective binding affinity $\left(K_d\right)$ and the intrinsic FRET efficiency $\left(E_{\max }\right)$ of the rapamycin-FKBP12-Cerulean and Frb-Venus complex, HeLa cells were transfected with several different cDNA molar ratios, and an effort made to image a wide range of fluorescence intensities. $E$ and $D∕A$ for FKBP12-Cerulean and Frb-Venus were determined using the three-filter cube approach following application of rapamycin $\left(1\mu \mathrm{M}\right)$ . In the absence of rapamycin, $E$ varied between about $-3$ to 3% in cells with Frb-Venus intensity less than 5000 AFU [Fig. 1c, left). Therefore, to increase signal-to-noise ratio, seven cells with $E$ less than 3% in the presence of rapamycin were excluded from further analysis.

We then calculated the SSE $(\mathrm{n}=241)$ between the measured and predicted $E$ using Eqs. 7, 17, 18, custom Igor Pro-based computer program. A $448\times 448$ array with row and column indices represented by hypothetical $E_{\max }$ (0.1 to 0.4) and $K_d$ (0 to 2000 AFU) values, respectively, was filled with the computed SSE values for the dataset. In other words, each “pixel” in the array contained an “intensity” (i.e., $z$ value) comprised of the SSE for the dataset computed from the corresponding $K_d$ (ordinate) and $E_{\max }$ (abscissa). These data could be represented as an image with the SSE “intensity” coded by a color scale, as shown in Fig. 2a (color online only), allowing visualization of the error space topology. The band of similar colors extending diagonally across the plot indicates a “trough” in the SSE and thus parameter correlation. Expanding the color scale over a narrower range of SSE values, both the gradient [Fig. 2b] and convergence to the ${\mathrm{SSE}}_{\min }$ (0.125, marked with a cross symbol) at $K_d=148$ AFU and $E_{\max }=0.244$ become more apparent. A plot of the measured versus predicted $E$ , and the corresponding residuals, is shown in Fig. 2d. A histogram of the residuals is plotted in Fig. 2e, and the resulting distribution fit with a Gaussian equation (solid line, $p=0.12$ , chi-square test). This result supports an assumption of maximum likelihood analysis, on which the ${\mathrm{SSE}}_{\min }$ as best estimator of $K_d$ and $E_{\max }$ was based.²²

Fig. 2

Estimation of the binding affinity between rapamycin-FKBP12-Cerulean and Frb-Venus coexpressed in HeLa cells using the least square method. (a) The sum of squared errors (SSE) between the measured and predicted FRET efficiencies were generated for a matrix of $K_d$ and $E_{\max }$ values and plotted using a color scale. (b) Same plot as in (a) using an expanded color scale. The cross indicates the minimum of the SSE. (c) The ellipse (red) defines the 95% confidence contour of the estimated $K_d$ and $E_{\max }$ . Projections of the contour (black lines) define the estimated 95% confidence intervals of the $K_d$ (ordinate) and $E_{\max }$ (abscissa). (d) Plot of the measured versus predicted (based on the estimated $K_d$ and $E_{\max }$ ) FRET efficiency with residuals shown before. (e) Distribution of the residuals. A Gaussian equation was fit (red line) to the histogram data. (f) The measured FRET efficiency plotted versus estimated free acceptor (Frb-Venus) concentration (in AFU). A bimolecular binding equation was fit to the data (red line). (Color online only.)

To generate estimated confidence limits for $K_d$ and $E_{\max }$ , we defined a region within the SSE space that contained all combinations of $K_d$ and $E_{\max }$ that generated SSEs not significantly different, at an arbitrarily defined level (e.g., $P=0.05$ ), from the best-fit values. The critical value of the SSE for $P=0.05$ is described by:

The data can be plotted as $E$ versus free acceptor concentration $\left(A_{\text{free}}\right)$ , thereby emulating a typical saturation binding isotherm [Fig. 2f]. As mentioned earlier, $E$ is proportional to fractional donor occupancy. $A_{\text{free}}$ was calculated using the formula: $A_{\text{free}}=A-DA$ . The data points were compared with a theoretical line generated from the previously determined $E_{\max }$ and $K_d$ parameters according to: $E=E_{\max }\times A_{\text{free}}∕(K_d+A_{\text{free}})$ . The data points span much of the binding curve around the $K_d$ value—a prerequisite for accurate parameter estimation. It should be emphasized that the $K_d$ value cannot be derived from $E$ versus $A_{\text{free}}$ relationship, as this represents a circular dependency. The main purpose of Fig. 2f is to provide a visual data representation analogous to radioligand binding studies.

The ability to distinguish different affinities was tested by introducing a point mutation, W2101F, in the Frb domain. Previous studies indicated that the affinity between the rapamcyin-FKBP12 complex and ${\mathrm{Frb}}_{\mathrm{W}2101\mathrm{F}}$ was reduced.¹⁶ The experimental procedure used before was repeated using ${\mathrm{Frb}}_{\mathrm{W}2101\mathrm{F}}$ -Venus in place of the wild-type construct. As shown in Fig. 3a (upper ellipse), the effective $K_d$ and $E_{\max }$ were estimated to be 492 AFU (95% confidence interval 292 to 775 AFU) and 0.255 (95% confidence interval 0.229 to 0.287), respectively. As before, we plotted the $E$ measured from HeLa cells $(\mathrm{n}=261)$ coexpressing FKBP12-Cerulean and ${\mathrm{Frb}}_{\mathrm{W}2101\mathrm{F}}$ -Venus (in the presence of $1\mu \mathrm{M}$ rapamycin) against the $E_{\mathrm{pred}}$ [Fig. 3b]. The residuals were also calculated [Fig. 3b] and the distribution found to be adequately described by a Gaussian equation (data not shown). Expressing the data as a standard binding isotherm [i.e., $E$ versus free acceptor concentration, Fig. 3c] revealed a right-shifted sigmoid curve with data points spanning the estimated $K_d$ value. Based on these data, we conclude that the affinity between rapamycin-FKBP12-Cerulean complex and ${\mathrm{Frb}}_{\mathrm{W}2101\mathrm{F}}$ -Venus was significantly lower when compared with wild-type Frb-Venus. This conclusion is visually evident when the 95% confidence ellipse from Fig. 2c is superimposed on Fig. 3a (lower ellipse). Our conclusion is consistent with the results from a rapamycin-induced transcriptional switch assay performed in yeast cells using ${\mathrm{Frb}}_{\mathrm{W}2101\mathrm{F}}$ .¹⁶ In contrast to the $K_d$ , the estimated $E_{\max }$ for ${\mathrm{Frb}}_{\mathrm{W}2101\mathrm{F}}$ was not significantly different from the wild-type Frb. Although structural changes resulting from point mutations are difficult to predict, the result is consistent with the position of the W2101F mutation within the context of the wild-type Frb crystal structure. The mutated residue lies within the Frb-rapamycin binding pocket and thus, barring dramatic long-range alterations in conformation, would not be expected to alter the position of the Frb C-terminus where the fluorescent protein is attached, and hence the intrinsic FRET efficiency (i.e., $E_{\max }$ ).

Fig. 3

Estimation of the effective binding affinity between rapamycin-FKBP12-Cerulean and the mutant ${\mathrm{Frb}}_{\mathrm{W}2101\mathrm{F}}$ -Venus coexpressed in HeLa cells using the least-squares method. (a) The 95% confidence contours of the estimated $K_d$ and $E_{\max }$ for the mutant ${\mathrm{Frb}}_{\mathrm{W}2101\mathrm{F}}$ -Venus (upper ellipse) and wild-type Frb-Venus (lower ellipse) binding to rapamycin-FKBP12-Cerulean are displayed for visual comparison. The center of the cross indicates the point of best estimation. (b) The measured FRET efficiencies $\left(\mathrm{FRET}\text{‐}E\right)$ were plotted against the corresponding predicted $\mathrm{FRET}\text{‐}E$ based on the estimated $K_d$ and $E_{\max }$ . The corresponding residuals (i.e., the difference between the measured and predicted $\mathrm{FRET}\text{‐}E$ ) are displayed. (c) The measured $\mathrm{FRET}\text{‐}E$ are plotted versus the estimated relative concentration, in AFU, of free acceptor, ${\mathrm{Frb}}_{\mathrm{W}2101\mathrm{F}}$ -Venus. The data were fitted with a bimolecular binding equation.

An advantage of the FKBP12-rapamycin-Frb system, aside from being inducible, is that the absolute affinity $(K_d=12\mathrm{nM})$ has been well characterized.¹⁰ Assuming that the in-vivo and in-vitro binding affinities are comparable and the fluorescent protein tagging has a negligible effect on binding affinity, we can calibrate our system with the literature value for the wild-type Frb and estimate that the affinity between FKBP12-rapamycin and ${\mathrm{Frb}}_{\mathrm{W}2101\mathrm{F}}$ in living cells to be about $40\mathrm{nM}$ (95% confidence interval $24\text{to}63\mathrm{nM}$ ).

5.

Discussion

We have described a simple optical method for estimating the effective affinity between two protein domains in living mammalian cells. This method was extended from a previous study in which $E$ and $D∕A$ was determined in living cells using three-cube wide-field fluorescence microscopy.¹⁷ Assuming a simple bimolecular interaction, $E$ is a function of donor and acceptor concentrations (total, not free) and the parameters $K_d$ and $E_{\max }$ . We estimated $K_d$ and $E_{\max }$ by minimizing the SSE between the predicted and the measured $E$ . Minimization was accomplished by generating SSE values for a matrix of hypothetical $K_d$ and $E_{\max }$ values. From the topology, a 95% confidence contour for $K_d$ and $E_{\max }$ was generated using a critical $F$ value. Projection of the contour allowed assignment of estimated confidence intervals to each parameter. The method was tested by expressing an inducible FRET fusion pair consisting of FKBP12-Cerulean and Frb-Venus (wild-type or mutant) in HeLa cells. We demonstrated that when coexpressed in HeLa cells and in the presence of the heterodimerization-inducer rapamycin at the saturating concentration, FKBP12-Cerulean has a higher affinity for Frb-Venus than to the mutant ${\mathrm{Frb}}_{\mathrm{W}2101\mathrm{F}}$ -Venus. This result was consistent with a report using a transcriptional switch assay based on the yeast two-hybrid system.¹⁶ Therefore, the described methodology is useful for comparing protein-protein interaction affinities based on FRET determinations in living cells.