Abstract
Contamination with diffraction from ice crystals can negatively affect, or even impede, macromolecular structure determination, and therefore detecting the resulting artefacts in diffraction data is crucial. However, once the data have been processed it can be very difficult to automatically recognize this problem. To address this, a set of convolutional neural networks named Helcaraxe has been developed which can detect ice-diffraction artefacts in processed diffraction data from macromolecular crystals. The networks outperform previous algorithms and will be available as part of the AUSPEX web server and the CCP4-distributed software.
Highlights
Crystals of biological macromolecules are routinely cryocooled to a temperature of 100 K before exposure to X-rays to reduce radiation damage during the diffraction experiment (Garman & Weik, 2019)
While antifreeze agents and flash-cooling are commonly employed to minimize this, rings from the diffraction of small ice crystals are frequently found in diffraction images from cryocooled macromolecular samples (Chapman & Somasundaram, 2010; Fig. 1)
1827 integrated, scaled and merged diffraction data sets indicated to have been measured at 100 K were used to generate training and validation sets. These diffraction data were randomly selected from the Coronavirus Structural Task Force repository (Croll et al, 2021; 396 diffraction data sets), the Integrated Resource for Reproducibility in Macromolecular Crystallography (Grabowski et al, 2016; 280 diffraction data sets) and the Protein Data Bank (Berman et al, 2000; 1151 diffraction data sets) without duplicates
Summary
Crystals of biological macromolecules are routinely cryocooled to a temperature of 100 K before exposure to X-rays to reduce radiation damage during the diffraction experiment (Garman & Weik, 2019). While antifreeze agents and flash-cooling are commonly employed to minimize this, rings from the diffraction of small ice crystals are frequently found in diffraction images from cryocooled macromolecular samples (Chapman & Somasundaram, 2010; Fig. 1). Identifying whether a structure, or more exactly the integrated, scaled and merged diffraction data set, available in the worldwide Protein Data Bank (wwPDB; Berman et al, 2000) is affected by ice-ring contamination is even more difficult. If an integrated and merged data set is affected by ice diffraction, one can assume that subsequent model refinement will be affected. It has been demonstrated that removing ice rings from the data during integration improves the R values by as much as 4.8% (Parkhurst et al, 2017). The correct identification of ice rings in data sets is an important step in assessing and improving data quality
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