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The Phenix-AlphaFold webservice: Enabling AlphaFold predictions for use in Phenix
Billy K Poon1, Thomas C Terwilliger2,3, Paul D Adams1,4
1Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Protein Science : a Publication of the Protein Society
|April 22, 2024
Summary
Machine learning now predicts protein structures accurately for crystallography and cryo-electron microscopy. A new Phenix-AlphaFold webservice makes these advanced protein structure predictions accessible to all Phenix users.
Area of Science:
- Structural biology
- Computational biology
- Biophysics
Background:
- Machine learning advances enable accurate protein structure prediction.
- Protein structure determination is crucial for understanding biological function.
- Existing tools require significant computational resources.
Purpose of the Study:
- Integrate AlphaFold predictions into the Phenix software suite.
- Develop a user-friendly webservice for remote AlphaFold predictions.
- Streamline the process of macromolecular structure determination.
Main Methods:
- Integrated AlphaFold predictions into an automated Phenix pipeline.
- Implemented a Phenix-AlphaFold webservice accessible via the Phenix GUI.
- Utilized amino acid sequences and experimental data for structure determination.
Main Results:
- Successfully created an automated pipeline for model-building and refinement.
- Enabled remote execution of AlphaFold predictions for Phenix users.
- Provided a solution to the technical requirements of running AlphaFold.
Conclusions:
- The Phenix-AlphaFold webservice democratizes access to advanced protein structure prediction.
- This integration facilitates more efficient macromolecular structure determination.
- Future improvements will be guided by user feedback and research directions.
