Ice in biomolecular cryocrystallography

Abstract

Diffraction data acquired from cryocooled protein crystals often include diffraction from ice. Analysis of ice diffraction from crystals of three proteins shows that the ice formed within solvent cavities during rapid cooling is comprised of a stacking-disordered mixture of hexagonal and cubic planes, with the cubic plane fraction increasing with increasing cryoprotectant concentration and increasing cooling rate. Building on the work of Thorn and coworkers [Thorn et al. (2017), Acta Cryst. D73, 729–727], a revised metric is defined for detecting ice from deposited protein structure-factor data, and this metric is validated using full-frame diffraction data from the Integrated Resource for Reproducibility in Macromolecular Crystallography. Using this revised metric and improved algorithms, an analysis of structure-factor data from a random sample of 89 827 PDB entries collected at cryogenic temperatures indicates that roughly 16% show evidence of ice contamination, and that this fraction increases with increasing solvent content and maximum solvent-cavity size. By examining the ice diffraction-peak positions at which structure-factor perturbations are observed, it is found that roughly 25% of crystals exhibit ice with primarily hexagonal character, indicating that inadequate cooling rates and/or cryoprotectant concentrations were used, while the remaining 75% show ice with a stacking-disordered or cubic character.

Integrated sample-handling and mounting system for fixed-target serial synchrotron crystallography

“…(viii) Crystal cryoprotection is simplified and made more effective. Cryoprotectant-containing solutions (or oils) can be deposited on crystals on the sample support and then withdrawn using suction through the holes in the support; repeating this deposition and removal two or more times can efficiently remove all solvent initially present on the crystal surface, thus eliminating the primary source of ice diffraction in cryocrystallography (Parkhurst et al, 2017;Moreau et al, 2021) and minimizing background scatter from excess cryoprotectant.…”

Detecting ice artefacts in processed macromolecular diffraction data with machine learning

“…The identification of ice diffraction artefacts in integrated, scaled and merged data has been an ongoing problem in macromolecular crystallography, even with modern cryo-cooling 16 techniques (Moreau et al, 2021) and new background estimation algorithms (Parkhurst et al, 2017). To aid identification in automatic pipelines as well as by users, a set of neural networks named Helcaraxe was developed to identify whether a scaled and merged X-ray diffraction dataset contains ice diffraction contamination of the reflection data from a macromolecular crystal.…”

“…Performance of both F obs and I obs networks against the test set was compared to other ice ring detection algorithms, namely phenix.xtriage (Adams et al, 2010), CTRUNCATE (Winn et al, 2011), the AUSPEX icefinder score and the recent p ice algorithm (Moreau et al, 2021).…”

“…Thus, the correct identification of ice rings in data sets is an important step in assessing and ultimately, improving data quality. In addition to statistical identification in CTRUNCATE (Winn et al, 2011) and phenix.xtriage (Adams et al, 2010), the AUSPEX icefinder score, recently improved by Moreau and colleagues (Moreau et al, 2021), is one of the most reliable statistical tools to detect ice crystal artefacts in integrated, merged and scaled diffraction data sets. While statistical identification can identify stronger ice diffraction in processed data automatically, less distinct ice rings can go unnoticed.…”

Detecting ice artefacts in processed macromolecular diffraction data with machine learning

“…In addition to statistical identification in CTRUNCATE (Winn et al, 2011) and phenix.xtriage (Adams et al, 2010), the AUSPEX Icefinder score, recently improved by Moreau et al (2021), is one of the most reliable statistical tools to detect icecrystal artefacts in integrated, merged and scaled diffraction data sets. While statistical identification can identify stronger ice diffraction in processed data automatically, less distinct ice rings can go unnoticed.…”