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Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement

This is the implementation of our ICML 2022 paper: https://arxiv.org/pdf/2207.06616.pdf

Dependencies

Our model is tested in Linux with the following packages:

  • CUDA >= 11.1
  • PyTorch == 1.8.2 (LTS Version)
  • Numpy >= 1.18.1
  • tqdm

You need additional packages for process_data.py, which convert PDB into JSON format. This is required only if you want to process your own data.

  • sidechainnet
  • prody

To calculate DockQ score (docking evaluation), you need to git clone https://github.com/bjornwallner/DockQ

Data

Our data are downloaded from the Structural Antibody Database (SAbDab). All the PDBs must be renumbered under IMGT scheme. The processed JSON files are saved in data/ folder, along with train/val/test splits. If you want to process your own PDB file, please run

python process_data.py ${pdb_file} ${heavy_chain_id} ${antigen_chain_id}

It will process the PDB file into JSON format used by this codebase.

CDR-H3 Local Docking

The training script can be launched by

python dock_train.py --hierarchical --L_target 20 --save_dir ckpts/HERN-dock

where L_target is the size of the epitope and --hierarchical means using hierarchical encoder.

At test time, we can dock CDR-H3 paratopes onto their corresponding epitopes:

mkdir outputs
python predict.py ckpts/HERN_dock.ckpt data/rabd/test_data.jsonl

It will produce a PDB file for each epitope in the test set with docked CDR-H3 structure. You can evaluate those docked structures using DockQ.py

Epitope-specific CDR-H3 Design

The training script can be launched by

python lm_train.py --hierarchical --L_target 20 --save_dir ckpts/HERN-gen

At test time, we can generate new CDR-H3 paratopes specific to a given epitope:

python generate.py ckpts/HERN_gen.ckpt data/rabd/test_data.jsonl 1 > results/HERN.txt

The above script will generate one CDR-H3 sequence per epitope. You can sample more candidates by changing this parameter.

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