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$X-ray Diffraction of Biological Samples$ 01:10

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According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are scattered by the electron clouds around the sample atoms. The X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...

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Related Experiment Video

Updated: Oct 5, 2025

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Published on: May 29, 2021

Detecting ice artefacts in processed macromolecular diffraction data with machine learning.

Kristopher Nolte¹, Yunyun Gao¹, Sabrina Stäb¹

¹Institute for Nanostructure and Solid State Physics, University of Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany.

Acta Crystallographica. Section D, Structural Biology

|February 1, 2022

Summary

Detecting ice crystal diffraction artefacts in macromolecular crystallography data is vital. A new deep learning tool, Helcaraxe, accurately identifies these problematic signals in processed diffraction data.

Keywords:

AUSPEX convolutional neural networks ice rings machine learning macromolecular crystallography

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Last Updated: Oct 5, 2025

Single Particle Cryo-Electron Microscopy: From Sample to Structure

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Published on: May 29, 2021

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

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Published on: January 16, 2021

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Published on: March 22, 2019

Area of Science:

Crystallography
Structural Biology
Artificial Intelligence

Background:

Ice crystal diffraction contaminates macromolecular crystallography data.
This contamination hinders accurate structure determination.
Automated detection of these artefacts in processed data is challenging.

Purpose of the Study:

To develop an automated method for detecting ice-diffraction artefacts.
To improve the reliability of macromolecular structure determination.

Main Methods:

Development of a convolutional neural network (CNN) suite named Helcaraxe.
Training the CNNs on processed diffraction data from macromolecular crystals.
Evaluating Helcaraxe's performance against existing algorithms.

Main Results:

Helcaraxe successfully detects ice-diffraction artefacts in processed diffraction data.
The developed neural networks demonstrate superior performance compared to previous methods.
The tool is integrated into the AUSPEX web server and CCP4 software.

Conclusions:

Helcaraxe provides an effective automated solution for identifying ice-diffraction artefacts.
This advancement aids in the accurate determination of macromolecular structures.
The accessibility of Helcaraxe will benefit the structural biology community.