RosettaCommons / RosettaCommons/RFdiffusion
Integration of ProteinMPNN & AF2 filtering
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- Python
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Description
Thanks a lot for making the RFDiffusion project available! I am trying to wrap my head around what is needed to get the whole design workflow set up locally.
RFdiffusion as described here only seems to output "poly-glycine" PDBs. So we still need to run ProteinMPNN and AF2 filtering on all candidate solutions. The colab version of RFdiffusion seems to perform these steps through a call to colabdesign/rf/designability_test.py. However, that script doesn't seem to exist neither in this repo nor in the colabdesign/rf one.
Could you please add this script to this repo here so that one can really reproduce the workflow described in your paper?
Contributor guide
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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
Start with the RFdiffusion workflow and the referenced colabdesign/rf/designability_test.py path, then trace how ProteinMPNN and AF2 filtering are expected to fit after candidate generation. Done means the repository contains the requested integration and users can reproduce the complete design workflow described in the paper locally.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100