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首页> 美国卫生研究院文献>Journal of Applied Crystallography >Methods for analysis of size-exclusion chromatography–small-angle X-ray scattering and reconstruction of protein scattering
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Methods for analysis of size-exclusion chromatography–small-angle X-ray scattering and reconstruction of protein scattering

机译：体积排阻色谱分析方法-小角X射线散射和蛋白质散射重建

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Size-exclusion chromatography in line with small-angle X-ray scattering (SEC–SAXS) has emerged as an important method for investigation of heterogeneous and self-associating systems, but presents specific challenges for data processing including buffer subtraction and analysis of overlapping peaks. This paper presents novel methods based on singular value decomposition (SVD) and Guinier-optimized linear combination (LC) to facilitate analysis of SEC–SAXS data sets and high-quality reconstruction of protein scattering directly from peak regions. It is shown that Guinier-optimized buffer subtraction can reduce common subtraction artifacts and that Guinier-optimized linear combination of significant SVD basis components improves signal-to-noise and allows reconstruction of protein scattering, even in the absence of matching buffer regions. In test cases with conventional SAXS data sets for cytochrome c and SEC–SAXS data sets for the small GTPase Arf6 and the Arf GTPase exchange factors Grp1 and cytohesin-1, SVD–LC consistently provided higher quality reconstruction of protein scattering than either direct or Guinier-optimized buffer subtraction. These methods have been implemented in the context of a Python-extensible Mac OS X application known as Data Evaluation and Likelihood Analysis (DELA), which provides convenient tools for data-set selection, beam intensity normalization, SVD, and other relevant processing and analytical procedures, as well as automated Python scripts for common SAXS analyses and Guinier-optimized reconstruction of protein scattering.
机译：符合小角度X射线散射（SEC-SAXS）的尺寸排阻色谱法已成为研究异质和自缔合系统的重要方法，但对数据处理提出了特殊挑战，包括缓冲液减法和重叠峰分析。本文提出了基于奇异值分解（SVD）和Guinier优化线性组合（LC）的新颖方法，以帮助分析SEC–SAXS数据集和直接从峰区域进行高质量的蛋白质散射重建。结果表明，经过Guinier优化的缓冲液减法可以减少常见的减影伪影，并且即使没有匹配的缓冲区，经过Guinier优化的重要SVD基础组分的线性组合也可以改善信噪比并允许重建蛋白质散射。在具有传统SAXS数据集和小GTPase Arf6和Arf GTPase交换因子Grp1和cytohesin-1的SEC-SAXS数据集的测试案例中，SVD-LC始终比直接或Guinier提供更高质量的蛋白质散射重建-优化的缓冲区减法。这些方法已在称为数据评估和似然分析（DELA）的Python可扩展Mac OS X应用程序的上下文中实现，该应用程序提供了方便的工具来进行数据集选择，束强度归一化，SVD以及其他相关处理和分析程序，以及用于常见SAXS分析和Guinier优化的蛋白质散射重建的自动Python脚本。

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