Code for the paper ABodyBuilder3: Improved and scalable antibody structure predictions.

Data and model weights are hosted at https://zenodo.org/records/11354577.

The bash script download.sh will download and extract data and model weights into appropriate directories.

If you only require model weights for inference, these can be downloaded and extracted with the following commands.

mkdir -p output/ zenodo/
wget -P zenodo/ https://zenodo.org/records/11354577/files/output.tar.gz
tar -xzvf zenodo/output.tar.gz -C output/

To create a conda environment with all required dependencies, you can use

./init_conda_venv.sh

After installation, the environment can be activated with

conda activate ./.venv

A simple example of using the model is given in notebooks/example.ipynb.

The repo comes with pre-specified data filtering (specified in data/filters.csv) and splits (specified in data/split.csv). If you want to reproduce these steps then run

1. python src/abodybuilder3/stages/data/combine_data_dfs.py
2. python src/abodybuilder3/stages/data/filter_data.py
3. python src/abodybuilder3/stages/data/split_data.py

Pre-computed language model embeddings are provided in data/structures/structures_plm after running download.sh. If you wish to regenerate then run

python src/abodybuilder3/stages/data/language_model_embeddings.py

The model can be trained using

1. python src/abodybuilder3/stages/train.py
2. python src/abodybuilder3/stages/finetune.py

The model can be used to predict structures from the validation and test set using

python src/abodybuilder3/stages/inference.py

For general sequences inputs can be prepared following the examples given in notebooks/example.ipynb.

Our code is built using dvc pipelines, an alternative way to run the code is via dvc exp run. See experiment_scripts for the configurations we used for the experiments in the manuscript.

If this code is useful to you please cite our paper using the following bibtex entry,

@article{abodybuilder3,
    author = {Kenlay, Henry and Dreyer, Frédéric A and Cutting, Daniel and Nissley, Daniel and Deane, Charlotte M},
    title = "{ABodyBuilder3: improved and scalable antibody structure predictions}",
    journal = {Bioinformatics},
    volume = {40},
    number = {10},
    pages = {btae576},
    year = {2024},
    month = {10},
    issn = {1367-4811},
    doi = {10.1093/bioinformatics/btae576}
}

along with the original ImmuneBuilder paper on which this work was based.

@article{immunebuilder,
  author = {Abanades, Brennan and Wong, Wing Ki and Boyles, Fergus and Georges, Guy and Bujotzek, Alexander and Deane, Charlotte M.},
  doi = {10.1038/s42003-023-04927-7},
  issn = {2399-3642},
  journal = {Communications Biology},
  number = {1},
  pages = {575},
  title = {ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins},
  volume = {6},
  year = {2023}
}