Project automation in <scp>CCP4</scp> Cloud: Enabling customization and high‐throughput efficiency

The rapid advancement of automatic structure solution methods, driven by the availability of high-quality predicted structures from AlphaFold and the growing adoption of multi-crystal and serial experiments, has created a pressing need for streamlining routine operations, automating structure solution projects, and efficiently handling large volumes of data. Modern software solutions must be both robust and user-friendly, supporting manual workflows while enabling high-throughput operations to keep pace with the high data collection rates of modern beamlines. Here, we present new developments in CCP4 Cloud that address these challenges by providing predefined and customizable automatic workflows, which can be seamlessly integrated with experimental facilities, offering a powerful solution for modern macromolecular crystallography. CCP4 Cloud is available as a public service at https://cloud.ccp4.ac.uk.

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Publication Details

Journal
Protein Science
Published
2025-05-24
DOI
https://doi.org/10.1002/pro.70176
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52

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Project automation in CCP4 Cloud: Enabling customization and high‐throughput efficiency

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Project automation in CCP4 Cloud: Enabling customization and high‐throughput efficiency

Oleg Kovalevskiy, Charles Ballard, Ronan M. Keegan, Eugene Krissinel, Maria Fando, Ville Uski, Jamison Wills, Andrey Lebedev
article en
1 citations

Abstract

The rapid advancement of automatic structure solution methods, driven by the availability of high-quality predicted structures from AlphaFold and the growing adoption of multi-crystal and serial experiments, has created a pressing need for streamlining routine operations, automating structure solution projects, and efficiently handling large volumes of data. Modern software solutions must be both robust and user-friendly, supporting manual workflows while enabling high-throughput operations to keep pace with the high data collection rates of modern beamlines. Here, we present new developments in CCP4 Cloud that address these challenges by providing predefined and customizable automatic workflows, which can be seamlessly integrated with experimental facilities, offering a powerful solution for modern macromolecular crystallography. CCP4 Cloud is available as a public service at https://cloud.ccp4.ac.uk.

Protein ScienceVol. 34(6)
Science and Technology Facilities Council (GB), Research Complex at Harwell (GB)
Directorate for Biological Sciences, Biotechnology and Biological Sciences Research Council, Science and Technology Facilities Council
Industry, innovation and infrastructure
Openalex Percentile: Top 38%
Machine Learning in Materials Science
0.52
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