Official repository for DeepAb: Antibody structure prediction using interpretable deep learning. The code, data, and weights for this work are made available under the Rosetta-DL license as part of the Rosetta-DL bundle.
Try antibody structure prediction in Google Colab.
Optional: Create and activate a python virtual environment
python3 -m venv venv
source venv/bin/activate
Install project dependencies
pip install -r requirements.txt
Note: PyRosetta should be installed following the instructions here.
Download pretrained model weights
wget https://data.graylab.jhu.edu/ensemble_abresnet_v1.tar.gz
tar -xf ensemble_abresnet_v1.tar.gz
After unzipping, pre-trained models might need to be moved such that they have paths trained_models/ensemble_abresnet/rs*.pt
Additional options for all scripts are available by running with --help.
Note: This project is tested with Python 3.7.9
Note: Using --renumber option will send your antibody to the AbNum server. If working with confidential sequences you should avoid this option and use an external renumbering tool.
Generate an antibody structure prediction from an Fv sequence with five decoys:
python predict.py data/sample_files/4h0h.fasta --decoys 5 --renumber
Generate a structure for a single heavy or light chain:
python predict.py data/sample_files/4h0h.fasta --decoys 5 --single_chain
Note: The fasta file should contain a single entry labeled "H" (even if the sequence is a light chain).
Expected output
After the script completes, the final prediction will be saved as pred.deepab.pdb. The numbered decoy structures will be stored in the decoys/ directory.
Annotate an Fv structure with H3 attention:
python annotate_attention.py data/sample_files/4h0h.truncated.pdb --renumber --cdr_loop h3
Note: CDR loop residues are determined using Chothia definitions, so the input structure should be numbered beforehand or renumbered by passing --renumber
Expected output
After the script completes, the annotated PDB will overwrite the input file (unless --out_file is specificed). Annotations will be stored as b-factor information, and can be visualized in PyMOL or similar software.
Calculate ΔCCE for list of designed sequences:
python score_design.py data/sample_files/wt.fasta data/sample_files/h_mut_seqs.fasta data/sample_files/l_mut_seqs.fasta design_out.csv
Expected output
After the script completes, the designs and scores will be written to a CSV file with each row containing the design ID, heavy chain sequence, light chain sequence, and ΔCCE value.
[1] JA Ruffolo, J Sulam, and JJ Gray. "Antibody structure prediction using interpretable deep learning." Patterns (2022).
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