Malaby Andrew W, Chakravarthy Srinivas, Irving Thomas C, Kathuria Sagar V, Bilsel Osman, Lambright David G
Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01605, USA ; Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01655, USA.
The Biophysics Collaborative Access Team (BioCAT), Department of Biological Chemical and Physical Sciences, Illinois Institute of Technology, Chicago, IL 60616, USA.
J Appl Crystallogr. 2015 Jul 8;48(Pt 4):1102-1113. doi: 10.1107/S1600576715010420. eCollection 2015 Aug 1.
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 (), 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.
尺寸排阻色谱联用小角X射线散射（SEC-SAXS）已成为研究多分散和自缔合体系的一种重要方法，但在数据处理方面存在特定挑战，包括缓冲液扣除和重叠峰分析。本文提出了基于奇异值分解（SVD）和吉尼尔优化线性组合（LC）的新方法，以促进SEC-SAXS数据集的分析，并直接从峰区域高质量重建蛋白质散射。结果表明，吉尼尔优化的缓冲液扣除可减少常见的扣除伪影，并且对显著的SVD基元进行吉尼尔优化的线性组合可提高信噪比，即使在没有匹配缓冲区域的情况下也能重建蛋白质散射。在针对细胞色素c的传统SAXS数据集以及针对小GTP酶Arf6、Arf GTP酶交换因子Grp1和细胞黏附素-1的SEC-SAXS数据集的测试案例中，SVD-LC始终比直接或吉尼尔优化的缓冲液扣除提供更高质量的蛋白质散射重建。这些方法已在一个名为（）的可通过Python扩展的Mac OS X应用程序中实现，该应用程序提供了用于数据集选择、光束强度归一化、SVD以及其他相关处理和分析程序的便捷工具，以及用于常见SAXS分析和吉尼尔优化的蛋白质散射重建的自动化Python脚本。
