sokrypton / sokrypton/ColabFold
How to save time to predict multimer structure by reusing previous monomer structure results
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- Dominant language
- Jupyter Notebook
- Stars
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- Forks
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Description
Expected Behavior
I used colabfold to predict two monomer proteins cas9 and deaminase, and I wanted to continue to predict their possible complex structure by multimer method.
While the cas9 protein was too large (~1400) AA), how can I adjust the code to reuse the previous monomer structure prediction results so that the prediction time of the multimer structure is greatly reduced?
How, if at all, can the code be tweaked to achieve this?
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
The issue does not identify a file, test, or entry point. Start by locating the multimer prediction workflow and how monomer prediction results are represented; determine whether those results can be reused, and validate the expected reduction in prediction time.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook
- Domain
- bioinformatics, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100