sokrypton / sokrypton/ColabFold
AlphaFold2 protein embedding Notebook as example
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- Dominant language
- Jupyter Notebook
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Description
Good morning,
I would like to propose you to share another extremely useful example of AF2 usage. Many scientists are using protein embeddings for downstream tasks (i.e. function prediction). AF2 issue described the codebase which gonna access you to the protein embedding vector but many users are not able to handle it by themself.
I hope you will consider my idea, to demonstrate how to load and prepare AF2 minimum setup to execute embedding part of the workflow on Colab or local machine. The most expected example could be AA sequence on the input and fixed-length numerical vector as output (averaged residue vector).
Warm regards,
Piotr
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Begin by reviewing the linked AlphaFold issue and the existing ColabFold examples, then determine how the AlphaFold2 embedding workflow can run in Colab or locally. Done means a notebook accepts an amino-acid sequence and produces the expected fixed-length, averaged residue embedding vector.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook
- Domain
- bioinformatics, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100