- 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 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
- 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.
Classifying the effects of missense variants using AlphaMissense
AlphaMissense is a new tool, based on AlphaFold2 but separate from it. It can predict whether a missense variant is likely to be pathogenic. Like AlphaFold2, AlphaMissense supplies confidence metrics to enable careful interpretation of its predictions.
By the end of this section, you will be able to:
- Understand the significance of missense variants for biomedical research
- Understand how to use AlphaMissense to explore whether or not a missense variation is likely to be pathogenic
- Critically interpret AlphaMissense predictions using both confidence metrics and an understanding of the scores limitations