期刊
JOURNAL OF APPLIED CRYSTALLOGRAPHY
卷 48, 期 -, 页码 1102-1113出版社
INT UNION CRYSTALLOGRAPHY
DOI: 10.1107/S1600576715010420
关键词
small-angle X-ray scattering; size-exclusion chromatography; singular value decomposition; linear combination; Guinier optimization
资金
- NIH [GM056324, DK060564]
- NSF [MCB1121942]
- DOE Office of Science by Argonne National Laboratory [DE-AC02-06CH11357]
- National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health [9 P41 GM103622]
- NIGMS [1S10OD018090-01]
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [P41GM103622, R01GM056324] Funding Source: NIH RePORTER
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.
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