Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models
by Rahmatullah Roche, Sumit Tarafder, and Debswapna Bhattacharya
Codebase for our single-sequence protein-RNA complex structure prediction method, ProRNA3D-single.
1.) We recommend conda virtual environment to install dependencies for ProRNA3D-single. The following command will create a virtual environment named 'ProRNA3D-single'
conda env create -f ProRNA3D-single_environment.yml
2.) Then activate the virtual environment
conda activate ProRNA3D-single
3.) The folding script requires PyRosetta, which can be installed following these instuctions.
4.) Download the trained model from here and place inside ProRNA3D_model/
curl --output ProRNA3D_model/model.pt "https://zenodo.org/records/11477127/files/model.pt?download=1"
That's it! ProRNA3D-single is ready to be used.
1.) Place the protein and RNA monomers (pdbs) inside inputs/
2.) Place the ESM2 embeddings inside inputs/ (see example inputs/7ZLQB.rep_1280.npy), and place the RNA-FM embeddings inside inputs/ (see example here inputs/7ZLQC_RNA.npy).
3.) Place the protein (Cα-Cα) distance maps inside prot_dist/ (see example prot_dist/7ZLQB_prot.dist), and place the RNA (C'4-C'4) distance maps inside rna_dist/ (see example rna_dist/7ZLQC_RNA.c4p.dist).
4.) Put the list of targets in the file inputs.list inside inputs/.
5.) Run
python run_predictions.py
This script will run inference and generate inter-protein-RNA interactions inside out_inter_rr/. Then it will transform the predictions into folded 3D protein-RNA complex structures inside predictions/
- The train, validation, and test set lists are available inside
Datasets/. - Data curated from PDB.