Size-distribution analysis of proteins by analytical ultracentrifugation: Strategies and application to model systems

被引:598
作者
Schuck, P
Perugini, MA
Gonzales, NR
Howlett, GJ
Schubert, D
机构
[1] NIDDKD, Div Bioengn & Phys Sci, Off Res Serv, NIH, Bethesda, MD 20892 USA
[2] NIDDKD, Mol Biol Lab, NIH, Bethesda, MD 20892 USA
[3] Univ Melbourne, Dept Biochem & Mol Biol, Parkville, Vic 3052, Australia
[4] Goethe Univ Frankfurt, Inst Biophys, D-6000 Frankfurt, Germany
关键词
D O I
10.1016/S0006-3495(02)75469-6
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
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.
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收藏
页码:1096 / 1111
页数:16
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