Biophys J, February 2002, p. 1096-1111, Vol. 82, No. 2

Size-Distribution Analysis of Proteins by Analytical Ultracentrifugation: Strategies and Application to Model Systems

Peter Schuck,^* Matthew A. Perugini, Noreen R. Gonzales, Geoffrey J. Howlett, and Dieter Schubert^§

 ^*Division of Bioengineering and Physical Science, Office of Research Services, and  Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, 20892 USA,  Department of Biochemistry and Molecular Biology, The University of Melbourne, Parkville, Australia, and  ^§Institut für Biophysik, Johann Wolfgang Goethe-Universität, Frankfurt am Main, Germany

Strategies for the deconvolution of diffusion in the determination of size-distributions from sedimentation velocity experiments were examined and developed. On the basis of four different model systems, we studied the differential apparent sedimentation coefficient distributions by the time-derivative method, g(s*), and by least-squares direct boundary modeling, ls-g*(s), the integral sedimentation coefficient distribution by the van Holde-Weischet method, G(s), and the previously introduced differential distribution of Lamm equation solutions, c(s). It is shown that the least-squares approach ls-g*(s) can be extrapolated to infinite time by considering area divisions analogous to boundary divisions in the van Holde-Weischet method, thus allowing the transformation of interference optical data into an integral sedimentation coefficient distribution G(s). However, despite the model-free approach of G(s), for the systems considered, the direct boundary modeling with a distribution of Lamm equation solutions c(s) exhibited the highest resolution and sensitivity. The c(s) approach requires an estimate for the size-dependent diffusion coefficients D(s), which is usually incorporated in the form of a weight-average frictional ratio of all species, or in the form of prior knowledge of the molar mass of the main species. We studied the influence of the weight-average frictional ratio on the quality of the fit, and found that it is well-determined by the data. As a direct boundary model, the calculated c(s) distribution can be combined with a nonlinear regression to optimize distribution parameters, such as the exact meniscus position, and the weight-average frictional ratio. Although c(s) is computationally the most complex, it has the potential for the highest resolution and sensitivity of the methods described.

Biophys J, February 2002, p. 1096-1111, Vol. 82, No. 2
© 2002 by the Biophysical Society   0006-3495/02/02/1096/16  $2.00

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