ANTIPASTI: interpretable prediction of antibody binding affinity exploiting Normal Modes and Deep Learning
ANTIPASTI (ANTIbody Predictor of Affinity from STructural Information) is a Python Deep Learning method that predicts the binding affinity of antibodies from their three-dimensional structure.
ANTIPASTI releases are distributed through the Python Package Index (PyPI). To install the latest version use pip:
pip install antipastiWe provide an Anaconda environment that satisfies all the dependencies in antipasti-env.yml.
git clone https://github.com/kevinmicha/ANTIPASTI
cd ANTIPASTI
conda env create -f antipasti-env.yml
conda activate antipasti-env
pip install .Next, you can run the tests to make sure your installation is working correctly.
# While still in the ANTIPASTI directory:
pytest . If you want to use an existing environment, just omit the Anaconda commands above:
git clone https://github.com/kevinmicha/ANTIPASTI
cd ANTIPASTI
pip install .or if you need to install it for your user only:
python setup.py install --user ANTIPASTI requires the following Python packages:
adabelief-pytorchbiopythonmatplotlibnumpyopencv-pythonoptunapandasscikit-learntorchtorchmetricsumap-learn
The full documentation can be found here.
Example notebooks are located in the notebooks folder:
- [Tutorial] Training ANTIPASTI
- [Tutorial] Predicting affinity using ANTIPASTI
- [Tutorial] Explaining binding affinity with ANTIPASTI
- [Tutorial] Combining AlphaFold and ANTIPASTI
You can download normal mode correlation maps computed at atomistic scale (instead of residue scale) here and place them in data/dccm_maps_full_ags_all_aa.
If you use this code, please cite the paper indicated in the documentation.
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