Repository containing various scripts to predict the binding affinity of protein-protein complexes from structure
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README.md

PRODIGY / Binding Affinity Prediction

DOI

Collection of scripts to predict binding affinity values for protein-protein complexes from atomic structures.

The online version of PRODIGY predictor can be found here:

Details of the binding affinity predictor implemented in PRODIGY can be found here:

Quick & Dirty Installation

git clone http://github.com/biopython/biopython.git
cd biopython
sudo python setup.py install # Alternatively, install locally but fix $PYTHONPATH

wget http://freesasa.github.io/freesasa-2.0.2.tar.gz
tar -xzvf freesasa-2.0.2.tar.gz
cd freesasa-2.0.2
./configure && make && make install

git clone http://github.com/haddocking/binding_affinity

# Edit the config.py to setup the paths to the freesasa binary and radii files
# or set the respective environment variables (FREESASA_BIN and FREESASA_PAR - these will have precedence)

# Have fun!

Usage

python predict_IC.py <pdb file> [--selection <chain1><chain2>]

Type --help to get a list of all the possible options of the script.

Installation & Dependencies

The scripts rely on Biopython to validate the PDB structures and calculate interatomic distances. freesasa, with the parameter set used in NACCESS (Chothia, 1976), is also required for calculating the buried surface area.

DISCLAIMER: given the different software to calculate solvent accessiblity, predicted values might differ (very slightly) from those published in the reference implementations. The correlation of the actual atomic accessibilities is over 0.99, so we expect these differences to be very minor.

To install and use the scripts, just clone the git repository or download the tarball zip archive. Make sure freesasa and Biopython are accessible to the Python scripts through the appropriate environment variables ($PYTHONPATH).

License

These utilities are open-source and licensed under the Apache License 2.0. For more information read the LICENSE file.

Citing us

If our predictive model or any scripts are useful to you, consider citing them in your publications:

Xue L, Rodrigues J, Kastritis P, Bonvin A.M.J.J, Vangone A.: PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics (2016) (link)

Anna Vangone and Alexandre M.J.J. Bonvin: Contacts-based prediction of binding affinity in protein-protein complexes. eLife, e07454 (2015) (link)

Panagiotis L. Kastritis , João P.G.L.M. Rodrigues, Gert E. Folkers, Rolf Boelens, Alexandre M.J.J. Bonvin: Proteins Feel More Than They See: Fine-Tuning of Binding Affinity by Properties of the Non-Interacting Surface. Journal of Molecular Biology, 14, 2632–2652 (2014). (link)

Contact

For questions about PRODIGY usage, please contact the team at: prodigy.bonvinlab@gmail.com