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Use of fluorescence-detected sedimentation velocity to study high-affinity protein interactions

Nature Protocols volume 12, pages 1777–1791 (2017)Cite this article

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Abstract

Sedimentation velocity (SV) analytical ultracentrifugation (AUC) is a classic technique for the real-time observation of free macromolecular migration in solution driven by centrifugal force. This enables the analysis of macromolecular mass, shape, size distribution, and interactions. Although traditionally limited to determination of the sedimentation coefficient and binding affinity of proteins in the micromolar range, the implementation of modern detection and data analysis techniques has resulted in marked improvements in detection sensitivity and size resolution during the past decades. Fluorescence optical detection now permits the detection of recombinant proteins with fluorescence excitation at 488 or 561 nm at low picomolar concentrations, allowing for the study of high-affinity protein self-association and hetero-association. Compared with other popular techniques for measuring high-affinity protein–protein interactions, such as biosensing or calorimetry, the high size resolution of complexes at picomolar concentrations obtained with SV offers a distinct advantage in sensitivity and flexibility of the application. Here, we present a basic protocol for carrying out fluorescence-detected SV experiments and the determination of the size distribution and affinity of protein–antibody complexes with picomolar KD values. Using an EGFP–nanobody interaction as a model, this protocol describes sample preparation, ultracentrifugation, data acquisition, and data analysis. A variation of the protocol applying traditional absorbance or an interference optical system can be used for protein–protein interactions in the micromolar KD value range. Sedimentation experiments typically take ∼3 h of preparation and 6–12 h of run time, followed by data analysis (typically taking 1–3 h).

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Figure 1: Recorded sedimentation boundaries and fit.
Figure 2: GUSSI overlay of sedimentation coefficient distributions showing the EGFP control (black) and mixtures with 0.1, 0.3, and 20 nM nanobody.
Figure 3: Screenshot of the isotherm text file created using Windows Notepad.
Figure 4: SEDPHAT screenshots showing experimental and global parameters.
Figure 5: GUSSI display of the signal-average sedimentation coefficients.

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References

  1. Havugimana, P.C. et al. A census of human soluble protein complexes. Cell 150, 1068–1081 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Gavin, A.C. et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141–147 (2002).

    Article  CAS  PubMed  Google Scholar 

  3. Schuck, P. Analytical ultracentrifugation as a tool for studying protein interactions. Biophys. Rev. 5, 159–171 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Schuck, P., Zhao, H., Brautigam, C.A. & Ghirlando, R. Basic Principles of Analytical Ultracentrifugation (CRC Press, 2015).

  5. Svedberg, T. The ultracentrifuge. Nobel lecture. Available at: http://www.nobelprize.org/nobel_prizes/chemistry/laureates/1926/svedberg-lecture.pdf (1926).

  6. Svedberg, T. & Pedersen, K.O. The Ultracentrifuge (Oxford University Press, 1940).

  7. Schuck, P. Sedimentation Velocity Analytical Ultracentrifugation: Discrete Species and Size-Distributions of Macromolecules and Particles (CRC Press, 2016).

  8. LaBar, F.E. & Baldwin, R.L. The sedimentation coefficient of sucrose. J. Am. Chem. Soc. 85, 3105–3108 (1963).

    Article  CAS  Google Scholar 

  9. Pavlov, G.M., Korneeva, E.V., Smolina, N.A. & Schubert, U.S. Hydrodynamic properties of cyclodextrin molecules in dilute solutions. Eur. Biophys. J. 39, 371–379 (2010).

    Article  CAS  PubMed  Google Scholar 

  10. Zhao, H. et al. Accounting for solvent signal offsets in the analysis of interferometric sedimentation velocity data. Macromol. Biosci. 10, 736–745 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Trachtenberg, S., Schuck, P., Phillips, T.M., Andrews, S.B. & Leapman, R.D. A structural framework for a near-minimal form of life: mass and compositional analysis of the helical mollicute Spiroplasma melliferum BC3. PLoS One 9, e87921 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Balbo, A. et al. Studying multi-protein complexes by multi-signal sedimentation velocity analytical ultracentrifugation. Proc. Natl. Acad. Sci. USA 102, 81–86 (2005).

    Article  CAS  PubMed  Google Scholar 

  13. MacGregor, I.K., Anderson, A.L. & Laue, T.M. Fluorescence detection for the XLI analytical ultracentrifuge. Biophys. Chem. 108, 165–185 (2004).

    Article  CAS  PubMed  Google Scholar 

  14. Zhao, H., Mayer, M.L. & Schuck, P. Analysis of protein interactions with picomolar binding affinity by fluorescence-detected sedimentation velocity. Anal. Chem. 18, 3181–3187 (2014).

    Article  CAS  Google Scholar 

  15. Zhao, H. et al. Monochromatic multicomponent fluorescence sedimentation velocity for the study of high-affinity protein interactions. Elife 5, e17812 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Schuck, P. Use of surface plasmon resonance to probe the equilibrium and dynamic aspects of interactions between biological macromolecules. Ann. Rev. Biophys. Biomol. Struct. 26, 541–566 (1997).

    Article  CAS  Google Scholar 

  17. Velázquez-Campoy, A. & Freire, E. Isothermal titration calorimetry to determine association constants for high-affinity ligands. Nat. Protoc. 1, 186–191 (2006).

    Article  PubMed  CAS  Google Scholar 

  18. Brautigam, C.A., Zhao, H., Vargas, C., Keller, S. & Schuck, P. Integration and global analysis of isothermal titration calorimetry data for studying macromolecular interactions. Nat. Protoc. 11, 882–894 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kroe, R.R. & Laue, T.M. NUTS and BOLTS: applications of fluorescence-detected sedimentation. Anal. Biochem. 390, 1–13 (2009).

    Article  CAS  PubMed  Google Scholar 

  20. Zhao, H. et al. Analysis of high-affinity assembly for AMPA receptor amino-terminal domains. J. Gen. Physiol. 139, 371–388 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zhao, H. et al. Recorded scan times can limit the accuracy of sedimentation coefficients in analytical ultracentrifugation. Anal. Biochem. 437, 104–108 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zhao, H. et al. Analysis of high-affinity assembly for AMPA receptor amino-terminal domains. J. Gen. Physiol. 141, 747–749 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhao, H., Casillas, E., Shroff, H., Patterson, G.H. & Schuck, P. Tools for the quantitative analysis of sedimentation boundaries detected by fluorescence optical analytical ultracentrifugation. PLoS One 8, e77245 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bailey, M.F., Angley, L.M. & Perugini, M.A. Methods for sample labeling and meniscus determination in the fluorescence-detected analytical ultracentrifuge. Anal. Biochem. 390, 218–220 (2009).

    Article  CAS  PubMed  Google Scholar 

  25. Lyons, D.F., Lary, J.W., Husain, B., Correia, J.J. & Cole, J.L. Are fluorescence-detected sedimentation velocity data reliable? Anal. Biochem. 437, 133–137 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Le Roy, A. et al. AUC and small-angle scattering for membrane proteins. Methods Enzymol. 562, 257–286 (2015).

    Article  CAS  PubMed  Google Scholar 

  27. Ryan, T.M., Howlett, G.J. & Bailey, M.F. Fluorescence detection of a lipid-induced tetrameric intermediate in amyloid fibril formation by apolipoprotein C-II. J. Biol. Chem. 283, 35118–35128 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kingsbury, J.S. et al. The modulation of transthyretin tetramer stability by cysteine 10 adducts and the drug diflunisal. Direct analysis by fluorescence-detected analytical ultracentrifugation. J. Biol. Chem. 283, 11887–11896 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Burgess, B.R. et al. Structure and evolution of a novel dimeric enzyme from a clinically important bacterial pathogen. J. Biol. Chem. 283, 27598–27603 (2008).

    Article  CAS  PubMed  Google Scholar 

  30. Rossmann, M. et al. Subunit-selective N-terminal domain associations organize the formation of AMPA receptor heteromers. EMBO J. 30, 959–971 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhu, T., Bailey, M.F., Angley, L.M., Cooper, T.F. & Dobson, R.C.J. The quaternary structure of pyruvate kinase type 1 from Escherichia coli at low nanomolar concentrations. Biochimie 92, 116–120 (2010).

    Article  CAS  PubMed  Google Scholar 

  32. Marzahn, M.R. et al. Higher-order oligomerization promotes localization of SPOP to liquid nuclear speckles. EMBO J. 35, 1–22 (2016).

    Article  CAS  Google Scholar 

  33. Montecinos-Franjola, F., Schuck, P. & Sackett, D.L. Tubulin dimer reversible dissociation: affinity, kinetics, and demonstration of a stable monomer. J. Biol. Chem. 291, 9281–9294 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Husain, B., Mukerji, I. & Cole, J.L. Analysis of high-affinity binding of protein kinase R to double-stranded RNA. Biochemistry 51, 8764–8770 (2012).

    Article  CAS  PubMed  Google Scholar 

  35. Foote, J. & Eisen, H.N. Kinetic and affinity limits on antibodies produced during immune responses. Proc. Natl. Acad. Sci. USA 92, 1254–1256 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Chen, J., Callis, P.R. & King, J.A. Mechanism of the very efficient quenching of tryptophan fluorescence in human gamma D- and gamma S-crystallins: the gamma-crystallin fold may have evolved to protect tryptophan residues from ultraviolet photodamage. Biochemistry 48, 3708–3716 (2009).

    Article  CAS  PubMed  Google Scholar 

  37. Goncalvez, A.P. et al. Humanized monoclonal antibodies derived from chimpanzee Fabs protect against Japanese encephalitis virus in vitro and in vivo. J. Virol. 82, 7009–7021 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Brekke, O.H. & Sandlie, I. Therapeutic antibodies for human diseases at the dawn of the twenty-first century. Nat. Rev. Drug Discov. 2, 52–62 (2003).

    Article  CAS  PubMed  Google Scholar 

  39. Hanes, J., Schaffitzel, C., Knappik, A. & Plückthun, A. Picomolar affinity antibodies from a fully synthetic naive library selected and evolved by ribosome display. Nat. Biotechnol. 18, 1287–1292 (2000).

    Article  CAS  PubMed  Google Scholar 

  40. Maynard, J.A. et al. Protection against anthrax toxin by recombinant antibody fragments correlates with antigen affinity. Nat. Biotechnol. 20, 597–601 (2002).

    Article  CAS  PubMed  Google Scholar 

  41. Fridy, P.C. et al. A robust pipeline for rapid production of versatile nanobody repertoires. Nat. Methods 11, 1253–1260 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Desai, A., Krynitsky, J., Pohida, T.J., Zhao, H. & Schuck, P. 3D-printing for analytical ultracentrifugation. PLoS One 11, e0155201 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Zhao, H., Lomash, S., Glasser, C., Mayer, M.L. & Schuck, P. Analysis of high affinity self-association by fluorescence optical sedimentation velocity analytical ultracentrifugation of labeled proteins: opportunities and limitations. PLoS One 8, e83439 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Patterson, G.H., Davidson, M., Manley, S. & Lippincott-Schwartz, J. Superresolution imaging using single-molecule localization. Annu. Rev. Phys. Chem. 61, 345–367 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zhao, H. et al. Accounting for photophysical processes and specific signal intensity changes in fluorescence-detected sedimentation velocity. Anal. Chem. 86, 9286–9292 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Balbo, A., Zhao, H., Brown, P.H. & Schuck, P. Assembly, loading, and alignment of an analytical ultracentrifuge sample cell. J. Vis. Exp. http://dx.doi.org/10.3791/1530 (2009).

  47. Ghirlando, R. et al. Improving the thermal, radial, and temporal accuracy of the analytical ultracentrifuge through external references. Anal. Biochem. 440, 81–95 (2013).

    Article  CAS  PubMed  Google Scholar 

  48. Zhao, H. et al. A multilaboratory comparison of calibration accuracy and the performance of external references in analytical ultracentrifugation. PLoS One 10, e0126420 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Lakowicz, J.R. Principles of Fluorescence Spectroscopy (Kluwer Academic/Plenum, 1999).

  50. Kirchhofer, A. et al. Modulation of protein properties in living cells using nanobodies. Nat. Struct. Mol. Biol. 17, 133–138 (2010).

    Article  CAS  PubMed  Google Scholar 

  51. Chaturvedi, S.K., Zhao, H. & Schuck, P. Sedimentation of reversibly interacting macromolecules with changes in fluorescence quantum yield. Biophys J. 112, 1374–1382 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Schuck, P. Size-distribution analysis of macromolecules by sedimentation velocity ultracentrifugation and Lamm equation modeling. Biophys. J. 78, 1606–1619 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Schuck, P. Sedimentation patterns of rapidly reversible protein interactions. Biophys. J. 98, 2005–2013 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Schuck, P. On the analysis of protein self-association by sedimentation velocity analytical ultracentrifugation. Anal. Biochem. 320, 104–124 (2003).

    Article  CAS  PubMed  Google Scholar 

  55. Zhao, H., Balbo, A., Brown, P.H. & Schuck, P. The boundary structure in the analysis of reversibly interacting systems by sedimentation velocity. Methods 54, 16–30 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Stafford, W.F. & Sherwood, P.J. Analysis of heterologous interacting systems by sedimentation velocity: curve fitting algorithms for estimation of sedimentation coefficients, equilibrium and kinetic constants. Biophys. Chem. 108, 231–243 (2004).

    Article  CAS  PubMed  Google Scholar 

  57. Dam, J., Velikovsky, C.A., Mariuzza, R.A., Urbanke, C. & Schuck, P. Sedimentation velocity analysis of heterogeneous protein-protein interactions: Lamm equation modeling and sedimentation coefficient distributions c(s). Biophys. J. 89, 619–634 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Correia, J.J. & Stafford, W.F. Extracting equilibrium constants from kinetically limited reacting systems. Methods Enzymol. 455, 419–446 (2009).

    Article  CAS  PubMed  Google Scholar 

  59. Brautigam, C.A. Using Lamm-equation modeling of sedimentation velocity data to determine the kinetic and thermodynamic properties of macromolecular interactions. Methods 54, 4–15 (2011).

    Article  CAS  PubMed  Google Scholar 

  60. Jameson, D.M. & Mocz, G. Fluorescence polarization/anisotropy approaches to study protein-ligand interactions: effects of errors and uncertainties. Methods Mol. Biol. 305, 301–322 (2005).

    CAS  PubMed  Google Scholar 

  61. Jameson, D.M. Introduction to Fluorescence (CRC Press, 2014).

  62. Kubala, M.H., Kovtun, O., Alexandrov, K. & Collins, B.M. Structural and thermodynamic analysis of the GFP:GFP-nanobody complex. Protein Sci. 19, 2389–2401 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Brautigam, C.A. Fitting two- and three-site binding models to isothermal titration calorimetric data. Methods 76, 124–136 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Schuck, P., Boyd, L.F. & Andersen, P.S. Measuring protein interactions by optical biosensors. Curr. Protoc. Cell Biol. 17, 17.6.1–17.6.22 (1999).

    Google Scholar 

  65. Schuck, P. & Zhao, H. The role of mass transport limitation and surface heterogeneity in the biophysical characterization of macromolecular binding processes by SPR biosensing. Methods Mol. Biol. 627, 15–54 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Zhao, H. & Schuck, P. Global multi-method analysis of affinities and cooperativity in complex systems of macromolecular interactions. Anal. Chem. 84, 9513–9519 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Zhao, H., Gorshkova, I., Fu, G.L. & Schuck, P. A comparison of binding surfaces for SPR biosensing using an antibody-antigen system and affinity distribution analysis. Methods 59, 328–335 (2013).

    Article  CAS  PubMed  Google Scholar 

  68. Svitel, J., Balbo, A., Mariuzza, R.A., Gonzales, N.R. & Schuck, P. Combined affinity and rate constant distributions of ligand populations from experimental surface-binding kinetics and equilibria. Biophys. J. 84, 4062–4077 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Nieba, L., Krebber, A. & Plückthun, A. Competition BIAcore for measuring true affinities: large differences from values determined from binding kinetics. Anal. Biochem. 234, 155–165 (1996).

    Article  CAS  PubMed  Google Scholar 

  70. Vorup-Jensen, T. in Nanomedicine (eds. Howard, K. A., Vorup-Jensen, T. & Peer, D.) 53–76 (Springer, 2016).

  71. Zhao, H., Piszczek, G. & Schuck, P. SEDPHAT – a platform for global ITC analysis and global multi-method analysis of molecular interactions. Methods 76, 137–148 (2015).

    Article  CAS  PubMed  Google Scholar 

  72. Melikishvili, M., Rodgers, D.W. & Fried, M.G. 6-Carboxyfluorescein and structurally similar molecules inhibit DNA binding and repair by O(6)-alkylguanine DNA alkyltransferase. DNA Repair 10, 1193–1202 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Brautigam, C.A. Calculations and publication-quality illustrations for analytical ultracentrifugation data. Methods Enzymol. 562, 109–133 (2015).

    Article  CAS  PubMed  Google Scholar 

  74. Pace, C.N., Vajdos, F., Fee, L., Grimsley, G. & Gray, T. How to measure and predict the molar absorption coefficient of a protein. Protein Sci. 4, 2411–2423 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Unruh, J.R., Gokulrangan, G., Wilson, G.S. & Johnson, C.K. Fluorescence properties of fluorescein, tetramethylrhodamine and Texas red linked to a DNA aptamer. Photochem. Photobiol. 81, 682–690 (2005).

    Article  CAS  PubMed  Google Scholar 

  76. Arthur, K.K., Gabrielson, J.P., Kendrick, B.S. & Stoner, M.R. Detection of protein aggregates by sedimentation velocity analytical ultracentrifugation (SV-AUC): sources of variability and their relative importance. J. Pharm. Sci. 98, 3522–3539 (2009).

    Article  CAS  PubMed  Google Scholar 

  77. Schuck, P. Diffusion of the reaction boundary of rapidly interacting macromolecules in sedimentation velocity. Biophys. J. 98, 2741–2751 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Schachman, H.K. Ultracentrifugation in Biochemistry (Academic Press, 1959).

  79. Lamm, O. Die differentialgleichung der ultrazentrifugierung. Ark. Mat. Astr. Fys. 21B, 1–4 (1929).

    Google Scholar 

  80. Brown, P.H. & Schuck, P. A new adaptive grid-size algorithm for the simulation of sedimentation velocity profiles in analytical ultracentrifugation. Comput. Phys. Commun. 178, 105–120 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by the Intramural Research Program of the National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health.

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  1. Jia Ma

    Present address: Present address: Bindley Bioscience Center, Purdue University, West Lafayette, Indiana, USA.,

Authors and Affiliations

  1. Dynamics of Macromolecular Assembly Section, Laboratory of Cellular Imaging and Macromolecular Biophysics, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland, USA

    Sumit K Chaturvedi, Jia Ma, Huaying Zhao & Peter Schuck

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Contributions

S.K.C., J.M., H.Z., and P.S. collected the data and developed the protocol. S.K.C., H.Z., and P.S. analyzed the data. S.K.C., H.Z., and P.S. prepared the manuscript.

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Correspondence to Huaying Zhao or Peter Schuck.

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Integrated supplementary information

Supplementary Figure 1 Surface Plasmon Resonance Data

SPR experiments were conducted in a Biacore 3000 instrument (GE Healthcare, Piscataway, NJ), at a temperature of 25 °C). The nanobody was chemically coupled to the surface on a CM3 sensor chip associated with one of the flow cells by amine coupling using the standard procedures. Immobilization was carried out with nanobody solution of 1 μg/mL at pH 5.5. PBS buffer with 0.005% v/v surfactant P20 was used as the working buffer for all the binding experiments. At a flow rate of 5 μL/min, cycles of 1200 sec surface binding of 0.30, 1, 3, 10, 30, and 100 nM EGFP were each followed by 2300 sec dissociation. The binding surface was regenerated with 1 min injection of glycine/HCl at pH 1.50 after the dissociation in each cycle. Analysis of SPR binding data was carried out with the program EVILFIT, which fits the data with a distribution of sites at different KD and different koff. Shown in Panel A is the family of binding traces at the different EGFP concentrations (blue to green solid lines) and global best-fit traces (red lines). Appended below is an overlay of the residuals of the fit, which have a root-mean-square deviation of 0.36 RU. Panel B shows the best-fit surface site distribution. The major peak (red) resulted in an average KD of 23 pM and koff of 3.0×10-5 sec-1. The total signal contribution from this peak is 37 RU from a total estimated binding capacity of 81 RU.

Supplementary Figure 2 Final SEDPHAT Screenshot

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Supplementary Figures 1 and 2, and Supplementary Table 1. (PDF 549 kb)

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Chaturvedi, S., Ma, J., Zhao, H. et al. Use of fluorescence-detected sedimentation velocity to study high-affinity protein interactions. Nat Protoc 12, 1777–1791 (2017). https://doi.org/10.1038/nprot.2017.064

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