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.
Authors
- Oleg Kovalevskiy (ORCID: https://orcid.org/0000-0003-1106-3966)
- Charles Ballard
- Ronan M. Keegan (ORCID: https://orcid.org/0000-0002-9495-0431)
- Eugene Krissinel
- Maria Fando (ORCID: https://orcid.org/0000-0001-6683-812X)
- Ville Uski (ORCID: https://orcid.org/0000-0002-1615-4798)
- Jamison Wills
- Andrey Lebedev
Institutions
- Science and Technology Facilities Council (GB)
- Research Complex at Harwell (GB)
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
Funders
- Directorate for Biological Sciences
- Biotechnology and Biological Sciences Research Council
- Science and Technology Facilities Council