DeepRank-Ab is a scoring function for ranking antibody-antigen docking models based on geometric deep learning.
📄 Publication
https://www.biorxiv.org/content/10.64898/2025.12.03.691974v1
This repository provides the full inference pipeline for the model described in the paper.
- PDB Processing
- Split ensemble models
- Extract chain sequences
- Merge chains for downstream analysis
- FASTA Conversion
- Generate FASTA files for CDR annotation and ESM embeddings
- ESM Embeddings
- Compute embeddings using
esm2_t33_650M_UR50D
- Compute embeddings using
- Graph Construction
- Build atom-level graphs with precomputed node and edge features
- Prediction
- Inference with pretrained EGNN models and output predicted DockQ
git clone https://github.com/haddocking/DeepRank-Ab
cd DeepRank-Abmamba env create -f environment-gpu.yml
mamba activate deeprank-abANARCI is required for CDR annotation.
Installation instructions:
https://github.com/oxpig/ANARCI
Note: hmmscan is already included in the environment.
If you encounter issues, follow the workaround here:
oxpig/ANARCI#102
The inference pipeline is executed through:
DeepRank-Ab/scripts/inference.py
python3 scripts/inference.py <pdb_file> <antibody_heavy_chain_id> <antibody_light_chain_id> <antigen_chain_id>python3 scripts/inference.py example/test.pdb H L AThis will:
- Create a workspace
- Generate ESM embeddings
- Annotate CDRs
- Build atom-level graphs
- Cluster nodes
- Predict DockQ scores
- Save output files (
.csvand.hdf5)
-
PDB file
Antibody--antigen structure. Can be a single model or an ensemble. -
Heavy chain ID
Example:H -
Light chain ID
Example:L -
Antigen chain ID
Example:A
We provide a helper script for running DeepRank-Ab on large batches of complexes. Adapt it to your dataset as needed.
Example:
python3 scripts/run_batch_inference.shFor issues or questions, please open a GitHub issue.