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Methods for the Design and Analysis of Sedimentation Velocity and Sedimentation Equilibrium Experiments with Proteins

Borries Demeler1,2

1Department of Biochemistry, The University of Texas Health Science Center at San Antonio, San Antonio, Texas
2Department of Computer Science, The University of Texas at San Antonio, San Antonio, Texas


Unit Number: 
Unit 7.13
DOI: 
10.1002/0471140864.ps0713s60
Online Posting Date: 
April, 2010
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Abstract

Analytical ultracentrifugation experiments play an integral role in the solution phase characterization of recombinant proteins and other biological macromolecules. This unit discusses the design of sedimentation velocity and sedimentation equilibrium experiments performed with a Beckman Optima XL-A or XL-I analytical ultracentrifuge. Optimal instrument settings and experimental design considerations are explained, and strategies for the analysis of experimental data with the UltraScan data analysis software package are presented. Special attention is paid to the strengths and weaknesses of the available detectors, and guidance is provided on how to extract maximum information from analytical ultracentrifugation experiments. Curr. Protoc. Protein Sci. 60:7.13.1-7.13.24. © 2010 by John Wiley & Sons, Inc.

Keywords: analytical ultracentrifugation; solution studies; sedimentation velocity; sedimentation equilibrium; UltraScan; 2-dimensional spectrum analysis; absorbance optics; intensity measurements

     
 
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Table of Contents

  • Introduction
  • Background
  • Experimental Design
  • Tips for Data Analysis
  • Data Management
  • Checklists
  • Experimental Analysis Flowchart
  • Conclusion
  • Acknowledgements
  • Literature Cited
  • Figures
  • Tables
     
 
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Figures

  • Figure 7.13.1
    Extinction profile of common reductants in the ultraviolet range. TCEP is an ideal reductant due to the low extinction at 280 nm where most proteins can be measured. DTT and ethanedithiol change extinction drastically with oxidation and are not recommended for AU experiments, this is not observed for TCEP.

  • Figure 7.13.2
    Emission intensity profile of the Xenon flash lamp. A well-tuned instrument will produce an intensity maximum at 230 nm of 15,000 counts or higher.

  • Figure 7.13.3
    Data quality comparison between absorbance (A, C, E) and intensity (B, D, F) data: A sedimentation experiment of a solution containing 0.1 OD ovalbumin was scanned at 280 nm in absorbance mode (panel A) and fitted by two-dimensional spectrum analysis with time- and radially invariant noise removal. The residuals of this fit are shown in panel C, and the noise corrected data are shown in panel E. After the experiment, the solution was shaken up and rescanned in intensity mode under identical run conditions, producing corresponding experimental scans in panel B, residuals in panel D and noise-subtracted data in panel F. The increased noise that can be seen in the absorbance experiment is due to the convolution of two stochastic measurements (reference and sample), which amplifies the stochastic noise approximately by a factor of Ö(2). In this case, the RMSD of the absorbance experiment was 3.5543 × 10–3 and the RMSD of the intensity experiment was 2.3705 × 10–3. The additional noise seen in panel B arises from time-invariant noise components contributed by optical components other than cell windows, which are subtracted out in the absorbance experiment. Regions in the residuals where deviations exceed the average are due to narrow, high-amplitude time invariant noise signals (most likely scratches on the cell windows) that are poorly reproduced by the scanning optics due to lack of precision in the radial scanning system.

  • Figure 7.13.4
    Time invariant noise contributions in the absorbance experiment (A) and the intensity experiment (B) shown in Figure 7.13.3. For clarity, the intensity-derived time-invariant noise vector is transposed by –0.2 absorbance units. While the amplitude for the low frequency noise is larger in the intensity measurement, the amplitude of the high frequency noise from the absorbance measurement is larger due to the convolution of the two noise vectors, one from each channel.

  • Figure 7.13.5
    Global extinction fit (thin black lines) of 15 wavelength scans (gray circles) at five different loading concentrations. Each loading concentration is scanned three times with 1-nm increments and fitted globally to the same sum of Gaussian terms to represent the intrinsic extinction profile with the UltraScan “Global Extinction Fit” module. Each loading concentration is represented by the same Gaussian sum, but with a different amplitude. The intrinsic extinction profile (heavy black line) of the protein is normalized by a known extinction coefficient (typically at 280 nm).

  • Figure 7.13.6
    Meniscus fit from RMSD values obtained in multiple iterations of 2DSA analysis fits. Such iterations always result in well-conditioned error surfaces that are easily fit by second- or third-order polynomials as shown here. The minimum occurs at the position where the first derivative of the polynomial is equal to zero.

  • Figure 7.13.7
    van Holde-Weischet integral distribution plots for the ovalbumin sample shown in Figure 7.13.3, measured at three different loading concentrations. Filled squares represent 0.1 OD at 280 nm, open circles represent 0.3 OD at 280 nm, and filled triangles represent 0.9 OD at 280 nm. The vertical shape of the s-value distribution indicates homogeneity. Identical results for all three concentrations indicate absence of reversible self-association in this concentration range (in contrast to the data shown in Fig. 7.13.8).

  • Figure 7.13.8
    van Holde-Weischet integral distribution plots for an ASTFEM simulated velocity experiment of a reversibly self-associating monomer-dimer system with monomer molecular weight of 20 kDa and a frictional ratio of 1.25, equilibrium constant of 1.0, and koff rate of 0.001/sec. Measured at three different loading concentrations covering a 100-fold concentration range, centered on the equilibrium constant [0.1 (filled triangle), 1.0 (open circles), and 10.0 (filled squares), arbitrary concentration units]. The off-vertical shape of the s-value distribution indicates heterogeneity, the shift in s-value with increasing concentration indicates that this system is reversibly self-associating (in contrast to the data shown in Fig. 7.13.7).

  • Figure 7.13.9
    UltraScan composition analysis of the data shown in Figure 7.13.8 (Relative concentrations: red: 0.1, blue: 1.0, green: 10.0). Solute signal is indicated by darkness of color in the 2-dimensional molecular weight/shape plane. (A) Monte Carlo analysis of the 2-dimensional spectrum analysis. (B) Parsimonious regularization by genetic algorithm analysis. (C) Monte Carlo analysis of genetic algorithm analysis shown in (B). The shift to higher molecular weight with increasing concentration is clearly apparent in all three methods. The spreading of peaks is dependent on stochastic noise in the data and is proportional to the confidence intervals of the parameters. Note that a noninteracting analysis does not reproduce the solutes (monomer and dimer) exactly, due to interactions between the oligomeric species. This results in measurements of the weight-average sedimentation coefficient and the gradient-average diffusion coefficient present at each point in the moving boundary. In the extreme of a very slowly interacting system, kinetic effects are minimal and the oligomeric species can be resolved reliably (Brookes et al., 2009).

Literature Cited

Literature Cited
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    Brookes, E. and Demeler, B. 2007. Parsimonious regularization using genetic algorithms applied to the analysis of analytical ultracentrifugation experiments. GECCO Proceedings ACM. pp. 361-368.
    Brookes, E. and Demeler, B. 2008. Parallel computational techniques for the analysis of sedimentation velocity experiments in UltraScan. Prog. Colloid Polym. Sci. 286:138-148.
    Brookes, E., Cao, W., and Demeler, B. 2009. A two-dimensional spectrum analysis for sedimentation velocity experiments of mixtures with heterogeneity in molecular weight and shape. Eur. Biophys. J. In press.
    Cao, W. and Demeler, B. 2005. Modeling analytical ultracentrifugation experiments with an adaptive space-time finite element solution of the Lamm equation. Biophys J. 89:1589-1602.
    Cao, W. and Demeler, B. 2008. Modeling analytical ultracentrifugation experiments with an adaptive space-time finite element solution for multi-component reacting systems. Biophys. J. 95:54-65.
    Cölfen, H., Laue, T.M., Wohlleben, W., Schilling, K., Karabudak, E., Langhorst, B.W., Brookes, E., Dubbs, B., Zollars, D., Rocco, M., and Demeler, B. 2009. The Open AUC Project. Eur. Biophys. J. In press.
    Demeler, B. 2005. "UltraScan-a comprehensive data analysis software package for analytical ultracentrifugation experiments". In Modern Analytical Ultracentrifugation: Techniques and Methods (D.J. Scott, S.E. Harding, and A.J. Rowe, eds.) pp. 210-229. Royal Society of Chemistry, London.
    Demeler, B. 2009. UltraScan Version 9.9 - A multi-platform analytical ultracentrifugation data analysis software package: http://www.ultrascan.uthscsa.edu.
    Demeler, B. and van Holde, K.E. 2004. Sedimentation velocity analysis of highly heterogeneous systems. Anal. Biochem. 335:279-288.
    Demeler, B. and Brookes, E. 2008. Monte Carlo analysis of sedimentation experiments. Prog. Colloid Polym. Sci. 286:129-137.
    Demeler, B., Saber, H., and Hansen, J.C. 1997. Identification and interpretation of complexity in sedimentation velocity boundaries. Biophys. J. 72:397-407.
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    Vistica, J., Dam, J., Balbo, A., Yikilmaz, E., Mariuzza, R.A., Rouault, T.A., and Schuck, P. 2004. Sedimentation equilibrium analysis of protein interactions with global implicit mass conservation constraints and systematic noise decomposition. Anal. Biochem. 326:234-256.
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