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.
Highlights
In macromolecular crystallography (MX), for data collected using the rotation method, a data set is typically composed of a sequence of X-ray diffraction images (Arndt & Wonacott, 1977); each image covers a fixed oscillation and, as the crystal is rotated, individual reflections enter and subsequently exit the diffracting condition
We describe a new algorithm for modelling the X-ray diffraction background in the presence of ice rings
In order to evaluate the effect on the quality of processed data when there are prominent ice rings in the X-ray background, some data sets were selected from the Joint Centre for Structural Genomics (JCSG; Gabanyi et al, 2011)
Summary
In macromolecular crystallography (MX), for data collected using the rotation method, a data set is typically composed of a sequence of X-ray diffraction images (Arndt & Wonacott, 1977); each image covers a fixed oscillation and, as the crystal is rotated, individual reflections enter and subsequently exit the diffracting condition. This is the case at higher resolution where ice rings may not be immediately visible on single detector images Scaling programs such as AIMLESS (Evans & Murshudov, 2013) have outlier-handling routines that exclude intensity measurements that are not consistent between symmetry-equivalent reflections; a resolution range can be set to exclude reflections from the scaling. Subtracting the background prior to the integration will result in the data no longer being Poisson-distributed; some pixels may contain negative counts and others may contain a noninteger number of counts This will render assumptions about the statistical properties of the data in the integration program invalid and will have an impact on the estimation of the errors in the intensities. 628 Parkhurst et al Modelling of data in the presence of ice rings should be modelled explicitly during the reflection-integration step
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