Abstract
The recent developments at microdiffraction X-ray beamlines are making microcrystals of macromolecules appealing subjects for routine structural analysis. Microcrystal diffraction data collected at synchrotron microdiffraction beamlines may be radiation damaged with incomplete data per microcrystal and with unit-cell variations. A multi-stage data assembly method has previously been designed for microcrystal synchrotron crystallography. Here the strategy has been implemented as a Python program for microcrystal data assembly (PyMDA). PyMDA optimizes microcrystal data quality including weak anomalous signals through iterative crystal and frame rejections. Beyond microcrystals, PyMDA may be applicable for assembling data sets from larger crystals for improved data quality.
Highlights
Biomolecular X-ray crystallography has enabled the understanding of biological complexity at the atomic and molecular level
The optimization of crystals to suitable sizes is a bottleneck in biomolecular crystallography
Compared with X-ray free-electron lasers (XFELs) which produce only one diffraction pattern for every microcrystal, synchrotron microdiffraction beamlines are optimized for collection of a small wedge of rotation data from each microcrystal, greatly improving data quality from microcrystals (Smith et al, 2012; Fuchs et al, 2016; Yamamoto et al, 2017)
Summary
Biomolecular X-ray crystallography has enabled the understanding of biological complexity at the atomic and molecular level. The problem of how to assemble data from radiation-damaged and incomplete data sets is not a trivial one To rationally treat unit-cell variations, radiation damage and incomplete data in microcrystals, we and others have developed data assembly workflows (Guo et al, 2018, 2019; Yamashita et al, 2018; Basu et al, 2019; Cianci et al, 2019). PyMDA allows for processing individual microcrystal data sets as progressive wedges to address radiation damage and allows for robust extraction of diffraction signals including weak anomalous signals through the implementation of unitcell-based classification and an iterative outlier rejection strategy. PyMDA may be used routinely to process microcrystal data sets to produce one or more assembled data sets for structural analysis
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