DeepAb

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.

Setup

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

Common workflows

Additional options for all scripts are available by running with --help.

Note: This project is tested with Python 3.7.9

Structure prediction

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.

Attention annotation

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.

Design scoring

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.

References

[1] JA Ruffolo, J Sulam, and JJ Gray. "Antibody structure prediction using interpretable deep learning." Patterns (2021).