- Course overview
- Search within this course
- An introductory guide to AlphaFold’s strengths and limitations
- Validation and impact
- 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.
Inputs and outputs
How does AlphaFold predict a protein structure? Why are confidence metrics important? AlphaFold is not perfect and its predictions can sometimes be inaccurate. By understanding these confidence metrics, we can make informed decisions about how to use the predicted structures and identify where further research is needed.
By the end of this section you will be able to:
- Recall the importance of the multiple sequence alignment (MSA) in the prediction of protein structures using AlphaFold.
- Explain how to interpret confidence scores, specifically by interpreting PAE (Predicted Aligned Error) and pLDDT (predicted Local Distance Difference Test).
- Analyse predicted structures from AlphaFold by integrating the different confidence metrics.