- Course overview
- Search within this course
- An introductory guide to AlphaFold’s strengths and limitations
- Inputs and outputs
- Accessing and predicting protein structures with AlphaFold 2
- 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
- AlphaFold 3 and AlphaFold Server
- Summary
- Course slides
- 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.
Validation and impact
Are AlphaFold structures “just structure predictions”? Are they even close to the experimentally determined structures? Are they good enough for any research use? This section answers these questions.
By the end of this section you will be able to:
- Explain how AlphaFold structure predictions were validated experimentally
- Describe the use of confidence scores to assess AlphaFold structure predictions
- Recall the diverse potential research uses of AlphaFold and recognise AlphaFold’s role in advancing diverse areas of scientific research