Detecting ice artefacts in processed macromolecular diffraction data with machine learning. Issue 2 (24th January 2022)

• Record Type: Journal Article
• Authors: Nolte, Kristopher; Gao, Yunyun; Stäb, Sabrina; Kollmannsberger, Philip; Thorn, Andrea
• Is Part Of: Acta crystallographica. Volume 78:Issue 2(2022)
• Page Start: 187
• Page End: 195
• Publication Date: 2022-01-24
• Subjects: machine learning -- convolutional neural networks -- macromolecular crystallography -- ice rings -- AUSPEX; X-ray crystallography -- Periodicals; Crystallography -- Periodicals; Molecular biology -- Periodicals; Molecular structure -- Periodicals; Biomolecules -- Structure -- Periodicals; Cytology -- Periodicals; Biomolecules -- Structure; Crystallography; Cytology; Molecular biology; Molecular structure; X-ray crystallography; Periodicals; 548
• DOI: 10.1107/S205979832101202X
• Languages: English
• ISSNs: 2059-7983

Abstract

Abstract : A program utilizing artificial learning and convolutional neural networks, named Helcaraxe, has been developed which can detect ice‐crystal artefacts in processed macromolecular diffraction data with unprecedented accuracy. 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 CCP 4‐distributed software.