Detecting ice artefacts in processed macromolecular diffraction data with machine learning

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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.
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Funding Information
  • Bundesministerium für Forschung, Technologie und Raumfahrt (05K19WWA)
  • Deutsche Forschungsgemeinschaft (TH2135/2-1)

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