Minimum Mean Squared Error Estimation of the Radius of Gyration in Small-Angle X-Ray Scattering Experiments

被引:4
作者
Alsaker, Cody [1 ]
Breidt, F. Jay [1 ]
van der Woerd, Mark J. [2 ]
机构
[1] Colorado State Univ, Dept Stat, 1231 E Dr, Ft Collins, CO 80523 USA
[2] Colorado State Univ, Dept Biochem & Mol Biol, Ft Collins, CO 80523 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
Autoregressive process; Generalized least squares; Guinier approximation; Local polynomial regression; Time series regression; BIOLOGICAL MACROMOLECULES; CONFORMATIONS; PROTEINS;
D O I
10.1080/01621459.2017.1408467
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Small-angle X-ray scattering (SAXS) is a technique that yields low-resolution structural information of biological macromolecules by exposing a large ensemble of molecules in solution to a powerful X-ray beam. The beam interacts with the molecules and the intensity of the scattered beam is recorded on a detector plate. The radius of gyration for a molecule, which is a measure of the spread of its mass, can be estimated from the lowest scattering angles of SAXS data. This estimation method requires specification of a window of scattering angles. Under a local polynomial model with autoregressive errors, we develop methodology and supporting asymptotic theory for selection of an optimal window, minimum mean square error estimation of the radius of gyration, and estimation of its variance. Simulation studies confirm the quality of our asymptotic approximations and the superior performance of the proposed methodology relative to the accepted standard. Our semi-automated methodology makes it feasible to estimate the radius of gyration many times, from replicated SAXS data under various experimental conditions, in an objective and reproducible manner. This in turn allows for secondary analyses of the dataset of estimates, as we demonstrate with a split-split plot analysis for 357 SAXS intensity curves. Supplementary materials for this article are available online.
引用
收藏
页码:39 / 47
页数:9
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