- 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
- Classifying the effects of missense variants using AlphaMissense
- AlphaFold 3 and AlphaFold Server
- Summary
- Course slides
- Your feedback
- Glossary of terms
- 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.
Advanced modelling and applications of predicted protein structures
In the previous sections, we covered the basics of AlphaFold2, including how it works, the different metrics used to assess the predicted structures, and the different ways to access and predict structures using the algorithm.
This section will cover how to use AlphaFold for advanced modelling, such as predicting protein-protein interactions, modelling large protein complexes and alternative structural states. You will learn how to use AlphaFold to gain new insights into protein structure and function.
By the end of this section you will be able to:
- Recall the different ways to use AlphaFold for advanced modelling using the source code, Colab notebooks and the database.
- Use AlphaFold to predict protein structures.
- Recall the different ways to customise the AlphaFold structure prediction process to meet specific needs.
- Interpret and analyse structure predictions from AlphaFold.