Philo John S
Alliance Protein Laboratories, Thousand Oaks, CA 91360, USA.
Anal Biochem. 2006 Jul 15;354(2):238-46. doi: 10.1016/j.ab.2006.04.053. Epub 2006 May 11.
Time-derivative approaches to analyzing sedimentation velocity data have proven to be highly successful and have now been used routinely for more than a decade. For samples containing a small number of noninteracting species, the sedimentation coefficient distribution function, g(s *), traditionally has been fitted by Gaussian functions to derive the concentration, sedimentation coefficient, and diffusion coefficient of each species. However, the accuracy obtained by that approach is limited, even for noise-free data, and becomes even more compromised as more scans are included in the analysis to improve the signal/noise ratio (because the time span of the data becomes too large). Two new methods are described to correct for the effects of long time spans: one approach that uses a Taylor series expansion to correct the theoretical function and a second approach that creates theoretical g(s *) curves from Lamm equation models of the boundaries. With this second approach, the accuracy of the fitted parameters is approximately 0.1% and becomes essentially independent of the time span; therefore, it is possible to obtain much higher signal/noise when needed. This second approach is also compared with other current methods of analyzing sedimentation velocity data.
分析沉降速度数据的时间导数方法已被证明非常成功，并且现在已经常规使用了十多年。对于包含少量非相互作用物种的样品，传统上沉降系数分布函数g(s *)是通过高斯函数拟合来推导每种物种的浓度、沉降系数和扩散系数的。然而，即使对于无噪声数据，该方法获得的精度也是有限的，并且随着分析中包含更多扫描以提高信噪比（因为数据的时间跨度变得太大），精度会进一步受损。本文描述了两种校正长时间跨度影响的新方法：一种方法是使用泰勒级数展开来校正理论函数，另一种方法是从边界的Lamm方程模型创建理论g(s *)曲线。使用第二种方法，拟合参数的精度约为0.1%，并且基本上与时间跨度无关；因此，在需要时可以获得更高的信噪比。还将第二种方法与当前其他分析沉降速度数据的方法进行了比较。
