Methods for analysis of size‐exclusion chromatography–small‐angle X‐ray scattering and reconstruction of protein scattering

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.