Background modelling of diffraction data in the presence of ice rings

DOI: 10.1107/S2052252517010259

Authors: James M. Parkhurst (Diamond Light Source; MRC Laboratory of Molecular Biology) , Andrea Thorn (Diamond Light Source; MRC Laboratory of Molecular Biology; Universität Hamburg) , Melanie Vollmar (Diamond Light Source) , Graeme Winter (Diamond Light Source) , David G. Waterman (Diamond Light Source) , Luis Fuentes-Montero (Diamond Light Source) , Richard J. Gildea (Diamond Light Source) , Garib N. Murshudov (MRC Laboratory of Molecular Biology) , Gwyndaf Evans (Diamond Light Source)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Iucrj , VOL 4

State: Published (Approved)
Published: September 2017

Abstract: An algorithm for modelling the background for each Bragg reflection in a series of X-ray diffraction images containing Debye–Scherrer diffraction from ice in the sample is presented. The method involves the use of a global background model which is generated from the complete X-ray diffraction data set. Fitting of this model to the background pixels is then performed for each reflection independently. The algorithm uses a static background model that does not vary over the course of the scan. The greatest improvement can be expected for data where ice rings are present throughout the data set and the local background shape at the size of a spot on the detector does not exhibit large time-dependent variation. However, the algorithm has been applied to data sets whose background showed large pixel variations (variance/mean > 2) and has been shown to improve the results of processing for these data sets. It is shown that the use of a simple flat-background model as in traditional integration programs causes systematic bias in the background determination at ice-ring resolutions, resulting in an overestimation of reflection intensities at the peaks of the ice rings and an underestimation of reflection intensities either side of the ice ring. The new global background-model algorithm presented here corrects for this bias, resulting in a noticeable improvement in R factors following refinement.

Journal Keywords: protein structure; refinement; X-ray crystallography; ice rings; data processing; data analysis; X-ray diffraction; data quality; AUSPEX; DIALS

Subject Areas: Mathematics, Biology and Bio-materials, Information and Communication Technology


Technical Areas:

Added On: 11/08/2017 10:24

Documents:
hi5646.pdf

Discipline Tags:

Information & Communication Technologies Structural biology Data processing Life Sciences & Biotech Mathematics

Technical Tags: