- 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.
AlphaMissense in the AlphaFold Database
At the AlphaFold Database, you can now explore the predicted structures for canonical human proteins and seamlessly toggle between two key visual representations, the quality of structure model (pLDDT) and the average pathogenicity scores from AlphaMissense.
The variant data is displayed using an innovative and interactive heatmap and 3D visualisation, offering insights into the broader implications of specific residue changes. The heatmap can help interpreting important functional areas with higher predicted pathogenicity.
Users can filter the heatmap by category and modify the range for categories based on their focus.
The AFDB update significantly aids researchers in identifying structural areas to guide their investigations into protein function and provides easy access to download the data.
Further information
If you are interested in a deeper dive into AlphaMissense, we recommend you watch our webinar “AlphaMissense predictions for human genetic variation research”. It details how AlphaMissense data can be accessed and visualised by researchers studying human genetics variations.
The webinar also demonstrates how EMBL-EBI resources have incorporated AlphaMissense data.