- Course overview
- Search within this course
- An introductory guide to AlphaFold’s strengths and limitations
- Validation and impact
- Inputs and outputs
- Accessing and predicting protein structures with AlphaFold2
- Choosing how to access AlphaFold2
- Accessing predicted protein structures in the AlphaFold Database
- Predicting protein structures with ColabFold and AlphaFold2 Colab
- Predicting protein structures using the AlphaFold2 open-source code
- Other ways to access predicted protein structures
- How to cite AlphaFold
- Advanced modelling and applications of predicted protein structures
- Classifying the effects of missense variants using AlphaMissense
- Future directions and summary
- Your feedback
- Glossary of terms
- References
- Acknowledgements
All materials are free cultural works licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, except where further licensing details are provided.
Acknowledgements
Bringing these materials to life wouldn’t have been possible without the fruitful partnership between Google Deepmind and EMBL-EBI. We extend our deepest gratitude to Google Deepmind’s Rosalind Onions, Jack Mason, Agata Laydon, Meera Last, Stephanie Booth and Juan Mateos-Garcia for their invaluable support, guiding every step from conception to completion. And from EMBL-EBI, Sameer Velankar and Mihaly Varadi’s insights and collaboration have significantly enriched the depth of the training materials. We would also like to thank Michael Marshall for his meticulous work in refining and enhancing the clarity of the course.
We would like to express our gratitude to the reviewers : Robbie Joosten, Jan Kosinski, Dan Rigden, Martin Steinegger, Sergey Ovchinnikov, Minkyung Baek, Wojciech Pawel Galej and Dmitry Molodenskiy, who offered their time and expertise. Their critical feedback and suggestions allowed polishing of these materials and helped bring this course to life.