Online tutorial
AlphaFold
A practical guide
Time to complete:
3 hours
This course includes:
Written by:
Last reviewed:
March 2024
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.
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Proteins are essential components of life, predicting their 3D structure enables researchers to get an insight into its function and role. AlphaFold is an artificial intelligence (AI) system, developed by Google DeepMind, that predicts a protein’s 3D structure based on its primary amino acid sequence. It regularly achieves accuracy competitive with experiment.
Who is this course for?
This tutorial is aimed at researchers who are interested in using AlphaFold2 to predict protein structures and integrate these predictions into their projects. An undergraduate-level knowledge of protein structure and structural biology would be an advantage.
The content of this course provides an understanding of the fundamental concepts behind AlphaFold2, how users can run protein predictions and how AlphaFold2 has been used to enhance research.
Throughout the course there may be terms used you are unfamiliar with. If so, please review the Glossary of terms of help.
This training module on AlphaFold2 has been developed in collaboration with Google DeepMind.
What will I achieve?
By the end of the course you will be able to:
- Explain how AlphaFold2 works and its strengths and limitations
- Describe how AlphaFold2 predictions were validated experimentally
- Discuss the fundamental concepts behind AlphaFold2 and why it is considered a significant breakthrough in protein structure prediction
- Assess the best way to predict protein structures
- Identify the best way to access pre-computed predictions from the AlphaFold2 Protein Structure Database
- Evaluate predicted structures from AlphaFold2 by integrating the different confidence metrics
What resources do I need?
To access some of the resources listed in this course, you will need a Google Account which can be created at accounts.google.com.
Disclaimer:
AlphaFold is a research tool and its outputs have varying levels of confidence. The information provided in these modules and by AlphaFold (including in the AlphaFold Database) is provided free of charge, strictly as-is and without representation or warranty of any kind. We are not responsible for the accuracy, reliability, availability, effectiveness, or correct use of this information or for any impact from ongoing use of such information. If you rely on any such information, you do so solely at your own risk. These modules, AlphaFold and its outputs are not intended for, have not been validated for, and are not approved for clinical use. You should not use any of this information for clinical purposes or rely on it for medical or other professional advice. Any content regarding those topics is provided for informational purposes only and is not a substitute for advice from a qualified professional.
DOI:
10.6019/TOL.AlphaFold-w.2024.00001.1
Course contents
- 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
- Future directions and summary
- Your feedback
- Glossary of terms
- References
- Acknowledgements
How and when to access the course
All our courses are designed with flexibility in mind. You can access them for free at any time, just click on the “Enter Course” button.
It is up to you how you use the course; you can either study the full course or you can focus on sections that are relevant to you. To jump between sections, use the navigation bar on the left or the arrows at the bottom of the page. You can also choose whether to complete the course in one go, or over several visits.
The average time to read through the main body of the course is 3 hours (not including exercises and external links). The time may vary depending on your prior knowledge and how you choose to work through the course.
Making the most of the course
Learning something new takes time and practice. We encourage you to:
- Use the activities and quizzes to help you check your learning, recall and apply key concepts. Look out for these icons:-
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Activities
Quizzes
Videos- Revisit sections as and when you need them. Bookmark relevant pages in your browser or use the navigation panel to jump the relevant section.
Getting help and providing feedback
If something isn’t working or if you have a question get in touch by contacting us at [email protected]
Tell us what you thought about the course (both good and bad!) using the “Feedback and help” button found at the top of each page.
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Learn more
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