Run AlphaFold from a Notebook
A simplified version of AlphaFold has been packaged as a container image for a Vertex AI Workbench notebook instance (used by Verily Workbench under the hood), along with an an example notebook. The prebuilt container image lets you get started without doing lots of additional installation. The notebook shows how you can predict the structure of a protein (or multiple proteins). For most targets, this method obtains predictions that are near-identical in accuracy compared to the full version.
This tutorial walks you through the process of setting up a Workbench notebook instance using the AlphaFold custom container image, and running the example notebook.
A blog post accompanies the example notebook. To learn more about how to correctly interpret these predictions, see the “Using the AlphaFold predictions” section of the post. The Supplementary Information article provides a more detailed description of the method.
Create a Workbench notebook instance
The AlphaFold custom container, created for the Vertex AI Workbench, is here:
After you’ve installed and configured the Workbench command-line tool, run the following command to create a new notebook instance. The args indicate to use the AlphaFold container image, and specify that the notebook instance should use 8 cores and one NVIDIA Tesla V100 GPU.
In the following command,
af_test is the terra resource name. You may want to change the
af-202203, or you can omit the arg and let the system generate an ID for you; this is the string you’ll see listed for the notebook in the Notebooks panel in the GCP Cloud Console.
terra resource create gcp-notebook --name af_test --instance-id af-202203 \ --accelerator-core-count=1 --accelerator-type=nvidia-tesla-v100 --machine-type=n1-standard-8 \ --install-gpu-driver=true --location=us-central1-c \ --container-repository=us-west1-docker.pkg.dev/cloud-devrel-public-resources/alphafold/alphafold-on-gcp \ --container-tag=latest
This command may take a while to run. You’ll get a confirmation when it’s finished. You can view the running notebook instance in the GCP Cloud Console if you like.
Upload and run the example notebook
Once your notebook instance is running, you can upload the AlphaFold example notebook to it. An easy way to do this is to bring up a browser window that is logged in with your Workbench email, and visit this URL:
You’ll see a dialog that lets you select an existing notebook instance or create a new one. Click ‘Select’, then select your new notebook instance.
Click CONTINUE, then click ‘Confirm’ in the next dialog.
The notebook will be automatically imported and ready for you to run. The notebook example walks you through the process of generating predictions for one or more protein sequences.
TipIn the imported notebook, there is some code that tries to copy a file called
stereo_chemical_props.txtto a directory under
If this copy fails due to permissions issues, then at the top of the “Run AlphaFold” cell of the notebook, try setting
run_relax = False; or alternately edit the code to read the
stereo_chemical_props.txtfile from another location.
Download the generated predictions
You can download the generated predictions via the
prediction.zip file, which includes a .pdb file.
You should see this archive listed in the left sidebar; right-click on the file to see the
Shut down the notebook instance when you’re done
Because this notebook instance uses a powerful GPU, it is fairly expensive to run. Shut it down via the Workbench CLI when you’re not using it, as follows, where
af_test is the resource name that you defined when you created the notebook instance.
terra notebook stop --name af_test
When you’re ready to use the notebook instance again, restart it via:
terra notebook start --name af_test
You can also delete the notebook resource if you’re entirely done with it.
Last Modified: 16 November 2023