Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Quantifying macromolecular interactions in living cells using FRET two-hybrid assays

Nature Protocols volume 11, pages 2470–2498 (2016)Cite this article

Subjects

This article has been updated

Abstract

Förster resonance energy transfer (FRET) is a versatile method for analyzing protein–protein interactions within living cells. This protocol describes a nondestructive live-cell FRET assay for robust quantification of relative binding affinities for protein–protein interactions. Unlike other approaches, our method correlates the measured FRET efficiencies to relative concentration of interacting proteins to determine binding isotherms while including collisional FRET corrections. We detail how to assemble and calibrate the equipment using experimental and theoretical procedures. A step-by-step protocol is given for sample preparation, data acquisition and analysis. The method uses relatively inexpensive and widely available equipment and can be performed with minimal training. Implementation of the imaging setup requires up to 1 week, and sample preparation takes ∼1–3 d. An individual FRET experiment, including control measurements, can be completed within 4–6 h, with data analysis requiring an additional 1–3 h.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Equipment setup for FRET imaging.
Figure 2: Schematic of optical components of the FRET setup for a given filter cube and fluorophore.
Figure 3: Spectral properties of FRET.
Figure 4: Components of the fluorescence signal at 535-nm emission.
Figure 5: Calibration experiments using cells expressing CFP–YFP dimers of high, intermediate and low FRET efficiency.
Figure 6: Conceptual graph of FRET binding curves.
Figure 7: Overview of the calibration phase of the FRET rig.
Figure 8: Overview of a FRET two-hybrid binding experiment.
Figure 9: Stability of spectral correction factors and amplification factors.
Figure 10: Determination of the G factor and the excitation ratio using dimers.
Figure 11: Examples of spurious FRET and FRET two-hybrid binding curves.

Similar content being viewed by others

Change history

  • 01 December 2016

    In the original version that appeared online, equal contribution of authors was incorrectly attributed; this has been corrected to indicate that Elisabeth Butz and Manu Ben Johny contributed equally to the work. The error has been corrected for the print, PDF and HTML versions of this article.

References

  1. Clegg, R.M. Fluorescence resonance energy transfer and nucleic acids. Methods Enzymol. 211, 353–388 (1992).

    Article  CAS  PubMed  Google Scholar 

  2. Jares-Erijman, E.A. & Jovin, T.M. FRET imaging. Nat. Biotechnol. 21, 1387–1395 (2003).

    Article  CAS  PubMed  Google Scholar 

  3. Shaner, N.C., Steinbach, P.A. & Tsien, R.Y. A guide to choosing fluorescent proteins. Nat. Methods 2, 905–909 (2005).

    Article  CAS  PubMed  Google Scholar 

  4. Berney, C. & Danuser, G. FRET or no FRET: a quantitative comparison. Biophys. J. 84, 3992–4010 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Gordon, G.W., Berry, G., Liang, X.H., Levine, B. & Herman, B. Quantitative fluorescence resonance energy transfer measurements using fluorescence microscopy. Biophys. J. 74, 2702–2713 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Xia, Z. & Liu, Y. Reliable and global measurement of fluorescence resonance energy transfer using fluorescence microscopes. Biophys. J. 81, 2395–2402 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Youvan, D.C. & Marrs, B.L. Molecular genetics and the light reactions of photosynthesis. Cell 39, 1–3 (1984).

    Article  CAS  PubMed  Google Scholar 

  8. Zal, T. & Gascoigne, N.R. Photobleaching-corrected FRET efficiency imaging of live cells. Biophys. J. 86, 3923–3939 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Erickson, M.G., Alseikhan, B.A., Peterson, B.Z. & Yue, D.T. Preassociation of calmodulin with voltage-gated Ca(2+) channels revealed by FRET in single living cells. Neuron 31, 973–985 (2001).

    Article  CAS  PubMed  Google Scholar 

  10. Erickson, M.G., Liang, H., Mori, M.X. & Yue, D.T. FRET two-hybrid mapping reveals function and location of L-type Ca2+ channel CaM preassociation. Neuron 39, 97–107 (2003).

    Article  CAS  PubMed  Google Scholar 

  11. Erickson, M.G., Moon, D.L. & Yue, D.T. DsRed as a potential FRET partner with CFP and GFP. Biophys. J. 85, 599–611 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Miyawaki, A. et al. Fluorescent indicators for Ca2+ based on green fluorescent proteins and calmodulin. Nature 388, 882–887 (1997).

    Article  CAS  PubMed  Google Scholar 

  13. Chen, H., Puhl, H.L. III, Koushik, S.V., Vogel, S.S. & Ikeda, S.R. Measurement of FRET efficiency and ratio of donor to acceptor concentration in living cells. Biophys. J. 91, L39–L41 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Yang, P.S., Johny, M.B. & Yue, D.T. Allostery in Ca(2)(+) channel modulation by calcium-binding proteins. Nat. Chem. Biol. 10, 231–238 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Broussard, J.A., Rappaz, B., Webb, D.J. & Brown, C.M. Fluorescence resonance energy transfer microscopy as demonstrated by measuring the activation of the serine/threonine kinase Akt. Nat. Protoc. 8, 265–281 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Ormo, M. et al. Crystal structure of the Aequorea victoria green fluorescent protein. Science 273, 1392–1395 (1996).

    Article  CAS  PubMed  Google Scholar 

  17. Lohse, M.J., Nuber, S. & Hoffmann, C. Fluorescence/bioluminescence resonance energy transfer techniques to study G-protein-coupled receptor activation and signaling. Pharmacol. Rev. 64, 299–336 (2012).

    Article  CAS  PubMed  Google Scholar 

  18. Zhang, J., Ma, Y., Taylor, S.S. & Tsien, R.Y. Genetically encoded reporters of protein kinase A activity reveal impact of substrate tethering. Proc. Natl. Acad. Sci. USA 98, 14997–15002 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Takao, K. et al. Visualization of synaptic Ca2+/calmodulin-dependent protein kinase II activity in living neurons. J. Neurosci. 25, 3107–3112 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bazzazi, H., Ben Johny, M., Adams, P.J., Soong, T.W. & Yue, D.T. Continuously tunable Ca(2+) regulation of RNA-edited CaV1.3 channels. Cell Rep. 5, 367–377 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ben Johny, M., Yang, P.S., Bazzazi, H. & Yue, D.T. Dynamic switching of calmodulin interactions underlies Ca2+ regulation of CaV1.3 channels. Nat. Commun. 4, 1717 (2013).

    Article  PubMed  CAS  Google Scholar 

  22. Takahashi, S.X., Miriyala, J., Tay, L.H., Yue, D.T. & Colecraft, H.M. A CaVbeta SH3/guanylate kinase domain interaction regulates multiple properties of voltage-gated Ca2+ channels. J. Gen. Physiol. 126, 365–377 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Agler, H.L. et al. G protein-gated inhibitory module of N-type (ca(v)2.2) ca2+ channels. Neuron 46, 891–904 (2005).

    Article  CAS  PubMed  Google Scholar 

  24. Karpenko, I.A. et al. Selective nonpeptidic fluorescent ligands for oxytocin receptor: design, synthesis, and application to time-resolved FRET binding assay. J. Med. Chem. 58, 2547–2552 (2015).

    Article  CAS  PubMed  Google Scholar 

  25. Chang, D.D. & Colecraft, H.M. Rad and Rem are non-canonical G-proteins with respect to the regulatory role of guanine nucleotide binding in Ca 1.2 regulation. J. Physiol. 593, 5075–5090 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yang, T., Xu, X., Kernan, T., Wu, V. & Colecraft, H.M. Rem, a member of the RGK GTPases, inhibits recombinant CaV1.2 channels using multiple mechanisms that require distinct conformations of the GTPase. J. Physiol. 588, 1665–1681 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Yamashita, M., Somasundaram, A. & Prakriya, M. Competitive modulation of Ca2+ release-activated Ca2+ channel gating by STIM1 and 2-aminoethyldiphenyl borate. J. Biol. Chem. 286, 9429–9442 (2011).

    Article  CAS  PubMed  Google Scholar 

  28. Muik, M. et al. Dynamic coupling of the putative coiled-coil domain of ORAI1 with STIM1 mediates ORAI1 channel activation. J. Biol. Chem. 283, 8014–8022 (2008).

    Article  CAS  PubMed  Google Scholar 

  29. Murphy, J.G. et al. AKAP-anchored PKA maintains neuronal L-type calcium channel activity and NFAT transcriptional signaling. Cell Rep. 7, 1577–1588 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ruiz-Velasco, V. & Ikeda, S.R. Functional expression and FRET analysis of green fluorescent proteins fused to G-protein subunits in rat sympathetic neurons. J. Physiol. 537, 679–692 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kern, A., Albarran-Zeckler, R., Walsh, H.E. & Smith, R.G. Apo-ghrelin receptor forms heteromers with DRD2 in hypothalamic neurons and is essential for anorexigenic effects of DRD2 agonism. Neuron 73, 317–332 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Albizu, L. et al. Time-resolved FRET between GPCR ligands reveals oligomers in native tissues. Nat. Chem. Biol. 6, 587–594 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Bazzazi, H. et al. Novel fluorescence resonance energy transfer-based reporter reveals differential calcineurin activation in neonatal and adult cardiomyocytes. J. Physiol. 593, 3865–3884 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lee, S.R., Sang, L. & Yue, D.T. Uncovering aberrant mutant PKA function with flow cytometric FRET. Cell Rep. 14, 3019–3029 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Tadross, M.R., Park, S.A., Veeramani, B. & Yue, D.T. Robust approaches to quantitative ratiometric FRET imaging of CFP/YFP fluorophores under confocal microscopy. J. Microsc. 233, 192–204 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Shrestha, D., Jenei, A., Nagy, P., Vereb, G. & Szollosi, J. Understanding FRET as a research tool for cellular studies. Int. J. Mol. Sci. 16, 6718–6756 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ben-Johny, M., Yue, D.N. & Yue, D.T. A novel FRET-based assay reveals 1:1 stoichiometry of apocalmodulin binding across voltage-gated Ca and Na ion channels. Biophys. J. 102, 125a–126a (2012).

    Article  Google Scholar 

  38. Koushik, S.V., Chen, H., Thaler, C., Puhl, H.L. III & Vogel, S.S. Cerulean, venus, and venusY67C FRET reference standards. Biophys. J. 91, L99–L101 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sun, Y., Day, R.N. & Periasamy, A. Investigating protein-protein interactions in living cells using fluorescence lifetime imaging microscopy. Nat. Protoc. 6, 1324–1340 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Yasuda, R. Imaging spatiotemporal dynamics of neuronal signaling using fluorescence resonance energy transfer and fluorescence lifetime imaging microscopy. Curr. Opin. Neurobiol. 16, 551–561 (2006).

    Article  CAS  PubMed  Google Scholar 

  41. Thaler, C., Koushik, S.V., Blank, P.S. & Vogel, S.S. Quantitative multiphoton spectral imaging and its use for measuring resonance energy transfer. Biophys. J. 89, 2736–2749 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Zeug, A., Woehler, A., Neher, E. & Ponimaskin, E.G. Quantitative intensity-based FRET approaches—a comparative snapshot. Biophys. J. 103, 1821–1827 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Leavesley, S.J., Britain, A.L., Cichon, L.K., Nikolaev, V.O. & Rich, T.C. Assessing FRET using spectral techniques. Cytometry A 83, 898–912 (2013).

    PubMed  PubMed Central  Google Scholar 

  44. Mustafa, S., Hannagan, J., Rigby, P., Pfleger, K. & Corry, B. Quantitative Forster resonance energy transfer efficiency measurements using simultaneous spectral unmixing of excitation and emission spectra. J. Biomed. Opt. 18, 26024 (2013).

    Article  PubMed  CAS  Google Scholar 

  45. Bykova, E.A., Zhang, X.D., Chen, T.Y. & Zheng, J. Large movement in the C terminus of CLC-0 chloride channel during slow gating. Nat. Struct. Mol. Biol. 13, 1115–1119 (2006).

    Article  CAS  PubMed  Google Scholar 

  46. Takanishi, C.L., Bykova, E.A., Cheng, W. & Zheng, J. GFP-based FRET analysis in live cells. Brain Res. 1091, 132–139 (2006).

    Article  CAS  PubMed  Google Scholar 

  47. Hoppe, A.D. & Swanson, J.A. Cdc42, Rac1, and Rac2 display distinct patterns of activation during phagocytosis. Mol. Biol. Cell 15, 3509–3519 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Chen, H., Puhl, H.L. III & Ikeda, S.R. Estimating protein-protein interaction affinity in living cells using quantitative Forster resonance energy transfer measurements. J. Biomed. Opt. 12, 054011 (2007).

    Article  PubMed  CAS  Google Scholar 

  49. Hoppe, A., Christensen, K. & Swanson, J.A. Fluorescence resonance energy transfer-based stoichiometry in living cells. Biophys. J. 83, 3652–3664 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Chen, Y., Mauldin, J.P., Day, R.N. & Periasamy, A. Characterization of spectral FRET imaging microscopy for monitoring nuclear protein interactions. J. Microsc. 228, 139–152 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Levy, S. et al. SpRET: highly sensitive and reliable spectral measurement of absolute FRET efficiency. Microsc. Microanal. 17, 176–190 (2011).

    Article  CAS  PubMed  Google Scholar 

  52. Kemmer, G. & Keller, S. Nonlinear least-squares data fitting in Excel spreadsheets. Nat. Protoc. 5, 267–281 (2010).

    Article  CAS  PubMed  Google Scholar 

  53. Findeisen, F. & Minor, D.L. Jr. Structural basis for the differential effects of CaBP1 and calmodulin on Ca(V)1.2 calcium-dependent inactivation. Structure 18, 1617–1631 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Martin, S.R. & Bayley, P.M. Calmodulin bridging of IQ motifs in myosin-V. FEBS Lett. 567, 166–170 (2004).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank R. Rötzer, A. Rößing, T. Kiwitt and W. Yang for excellent technical support. This work was supported by funding from the German Research Foundation (SFB/TRR 152 TP12 to M.B., SFB/TRR 152 TP06 to C.W.-S. and SFB 870 TP B05 to C.W.-S.) and the National Institute of Mental Health (R01 MH065531 to D.T.Y. and M.B.-J.). D.T.Y. passed away on December 23, 2014. He was a remarkable teacher, inspirational scientist and a generous colleague. He pioneered the development and the usage of FRET two-hybrid assays to elucidate the mechanistic underpinnings of voltage-gated Ca2+ channels. His scientific brilliance and personal charm are greatly missed in the scientific community.

Author information

Author notes

  1. Elisabeth S Butz and Manu Ben-Johny: These authors contributed equally to this work.

Authors and Affiliations

  1. Center for Integrated Protein Science CIPS-M and Zentrum für Pharmaforschung, Department Pharmazie; DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany

    Elisabeth S Butz, Martin Biel & Christian Wahl-Schott

  2. Department of Biomedical Engineering, Calcium Signals Laboratory, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Manu Ben-Johny, Michael Shen, Philemon S Yang, Lingjie Sang & David T Yue

Authors
  1. Elisabeth S Butz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manu Ben-Johny
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Philemon S Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lingjie Sang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Martin Biel
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. David T Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Christian Wahl-Schott
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

E.S.B., M.B.-J., D.T.Y. and C.W.-S. developed and/or designed the study. E.S.B. and M.B.-J. collected and analyzed the data. M.S. and L.S. furnished initial experimental data for dimer calibrations. M.B.-J. and D.T.Y. provided theoretical derivations. M.S., M.B.-J. and P.S.Y. constructed YFP- and CFP-tagged constructs and dimers. E.S.B., M.B.-J. and C.W.-S. wrote the manuscript and created the figures. M.B. discussed and commented on the manuscript.

Corresponding authors

Correspondence to Manu Ben-Johny or Christian Wahl-Schott.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Linearity of fluorescence detection subsystem

(a) Fluorescence intensity measured through the YFP cube is linearly proportional to the concentration of Alexa Fluor 514 dye. This result illustrates the linearity of the PMT detector enabling robust estimation of number of acceptor molecules. (b) Fluorescence intensity measured through the CFP cube is also linearly proportional to the concentration of Proflavin dye.

Supplementary Figure 2 Main graphical user interface of Felix GX

(a) The tab “Delta Ram Analog” (right upper corner) is clicked to open the “Hardware Configuration” window (Supplementary Fig. 2a). (b) The setup tab is selected (left lower corner) to open the “Setup” window (Supplementary Fig. 2b). (c) The “Action” tab opens the “Macro” window from which the “Macro Editor” window is opened (Supplementary Fig. 4).

Supplementary Figure 3 The “Hardware Configuration” window and the “Setup” window.

(a) Settings for the “Hardware Configuration” window. (b) Settings for the “Setup” window. From the 10 tabs in the “Setup” window select “Acquisition Type” and click the option “Timebased”.

Supplementary Figure 4 Parameter settings specific for Timebased acquisition in the “Setup window”.

(a) Acquisition settings; (b) PMTs; (c) Backgrounds; (d) Traces tab.

Supplementary Figure 5 Setup of Macros using the “Macro” and the “Macro Editor” window.

(a) Open the “Macro” window by clicking the “Action“ tab in the main graphical user interface (Supplemental Fig. 1). Click the green “+” button to open the “Macro Editor” window (b and c). In this window, different actions (listed on the left hand side) can be added to the macro (list on the black panel on the right hand side). In this example, a pause step (b) and a protocol (Run Acquisition; c) is added to the macro by clicking the Add tab.

Supplementary Figure 6 Data input field of the Background sheet.

(a) Raw data pasted into the respective columns from measurements using CFP- FRET- and YFP-cubes at a distinct HV gain. (b) The excel sheet contains 9 individual sub sheets (labeled from left to right: Background, RD-value (CFP), RA-value (YFP), Dimer sheet, Dimer Analysis, Spurious FRET, Samples (FRET sample data), BC Analysis and HV lookup.

Supplementary Figure 7 Data input field of the RD-value (CFP) and the RA-value (YFP) sheets.

Raw data are pasted into the respective columns on the left hand side from measurements in (a) CFP-only or (b) YFP-only expressing cells using CFP- FRET- and YFP-cubes at a distinct HV gain. Background subtraction and RA value calculation is performed automatically. (c) The mean RD and RA value, resp., is calculated on the right hand side (gray panel). Statistical values (Standard deviation of the mean (SEM), Minimum (Min) and Maximum (Max)) provide a useful tool to assess the variability of the values. Note that only the mean RD1 and RA1 values are used for subsequent analysis.

Supplementary Figure 8 Data input field of the Dimer sheet.

(a) Raw data are pasted into the respective columns on the left hand side from measurements in CFP-YFP dimer expressing cells using CFP- FRET- and YFP-cubes at a distinct HV gain. Raw data for individual cells are collected separately for a given dimer as indicated. (b) Background subtracted values are shown on the right. RA1 and RD1 values are automatically picked from RD-value (CFP) and the RA-value (YFP) sheets. Statistics are shown on the right hand side (Mean, SEM).

Supplementary Figure 9 Dimer analysis sheet.

(a) Columns D-G list background subtracted raw data from the “Dimer” sheet. Data for individual cells are collected separately for a given dimer as indicated in Column B. RA1 and RD1 values are automatically transferred from “RD-value (CFP)” and the “RA-value (YFP)” sheets (grey panel on the top, left). Columns H-P comprise calculations as outlined in Procedure Step 97. (b) The graph (column K plotted against column L) depicts data points for individual cells. For each dimer, cells cluster together. In the table below, the mean values of EA and ED for each dimer as well as mean values of the x- and y- coordinates (from columns M and N) are indicated. (c) The latter can be plotted in a new graph and analyzed by a linear regression line. In this template, the regression line is automatically computed and the resulting equation displayed in the graph. Note, that the y-intercept and the slope of this line is copied to cell T56 and T57 respectively for further analysis.

Supplementary Figure 10 Data input field of the “Spurious FRET” sheet.

(a) Raw data from cells expressing untagged eCFP and eYFP are transferred to respective cells in Columns C-G. (b) Background-subtraction as well as subsequent calculation of Fc/(RA1*SYFP), CFPEST and EA (columns K-M) is automatically performed referring to the constants in the gray panel at the upper left. (c) The graph at the right hand side, illustrates CFPEST as a function of EA. The slope of the regression line is used for spurious FRET correction in the “BC analysis sheet”. The equation of the regression line is automatically displayed in the graph. The slope is subsequently entered into cell “P7” linked to the “BC analysis” sheet.

Supplementary Figure 11 Data input field of the binding curve analysis (“BC analysis”) sheet.

(a) Background subtracted raw data (columns D-H) are automatically adopted from the “Samples” sheet. The principal calculation steps required in order to solve binding curves are automatically performed in columns I-W for each (biological) cell according Steps 117-130 outlined in the Procedure section. (b) The table in the upper left of the worksheet contains all parameters (columns F and I) necessary to correct for spectral properties and spurious FRET. Thereby, all values except the excitation ratio ((ƐYFP(440)/ƐCFP(440)) are automatically transferred from previously analyzed sheets. Values for KdEff, EAmax and EDmax (Column K) are place holders that are adjusted during the fitting procedure. (c) Graphs shown displays data points for individual biological cells (blue dots) and the fitted binding isotherme (black lines). The graphs represent data for 33-FRET (Dfree plotted against EA) and E-FRET (Afree plotted against ED) and allow a control of the fitting procedure.

Supplementary Figure 12 Setting up Excel Solver for optimizing 33-FRET and E-FRET binding parameters.

(a) Least squares optimization of Cell K8 (Sum_Err_3Cube) by changing both KD_eff (KD,EFF, relative affinity) and EAmax (EA,max, maximum 33-FRET efficiency). In this scenario, two constraints are EAmax>0, and Kd_eff>0 in accordance with physical principles. (b) Least squares optimization of Cell K10 (Total Error) by changing both KD_eff (KD,EFF, relative affinity), EAmax (EA,max, maximum 33-FRET efficiency), and EDmax (ED,max, maximum E-FRET efficiency). Three constraints are imposed: EDmax>0, EAmax>0, and Kd_eff>0.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12 and Supplementary Notes 1–4 (PDF 4424 kb)

Supplementary Data 1

MS Excel analysis spreadsheet for theoretical calculation of MD and MA containing sample data. (XLSX 270 kb)

Supplementary Data 2

MATLAB import/export script source file (M). (XLSX 46 kb)

Supplementary Data 3

MS Excel analysis spreadsheet containing sample data. (XLSX 133 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Butz, E., Ben-Johny, M., Shen, M. et al. Quantifying macromolecular interactions in living cells using FRET two-hybrid assays. Nat Protoc 11, 2470–2498 (2016). https://doi.org/10.1038/nprot.2016.128

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2016.128

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing