- 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 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
- 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.
Future directions and summary
In this tutorial, we have explained how AlphaFold2 works and explored its diverse applications. In this final section, we explore Google DeepMind’s ongoing efforts to improve AlphaFold2. Over the next few pages you can review a summary of the information presented in this tutorial. There is also a glossary to assist with some of the terms used in the tutorial that may be new to you.
By the end of the remainder of this tutorial you will be able to:
- Recall that Google DeepMind continues to develop and improve AlphaFold2
- Describe recent enhancements to AlphaFold2
- Know where to find additional resources
At the end of this tutorial there is also an opportunity for you to provide feedback.