- Course overview
- Search within this course
- 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
- 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.
An introductory guide to AlphaFold’s strengths and limitations
Proteins are foundational components of life. Each protein molecule has a distinctive 3D shape that dictates its functions, such as catalysing (speeding up) a biochemical reaction or enabling muscular contractions. Accurately predicting a protein’s structure enables us to better understand its functions and roles.
By the end of this section you will be able to:
- Recall the significance of the protein folding problem.
- Evaluate the fundamental concepts behind AlphaFold and why it is considered a significant breakthrough in protein structure prediction.
- Describe the strengths and limitations of AlphaFold’s structure predictions.