An empirical graph neural network for protein-ligand binding affinity prediction
At first, download the source code of EGNA from GitHub:
$ git clone https://github.com/chunqiux/EGNA.git
EGNA is implemented with Python3.7. We recommend you to use Anaconda to install the dependencies of EGNA. Anaconda can be downloaded here.
After installing anaconda, create and activate the virtual environment as follows:
$ conda env create -f egna_env.yml
$ conda activate egna_env
When you want to quit the virtual environment, just:
$ conda deactivate
HHblits is used to generate sequence profiles in our model. It can be downloaded here. The sequence databases can also be downloaded in the same page. In this study, Uniclust30 is used.
At first, prepare the PDB file of the protein structure and the sdf or mol2 file of the ligand. It should be guaranteed that the two molecules are well docked or are extracted from a true complex. Then, the pKd of the target complex can be predicted as follows:
$ python predict.py -p protein.pdb -l ligand.mol2 -d "path of your sequene databases"
where '-p protein.pdb' means the path of protein file is 'protein.pdb', '-l ligand.mol2' means the path of ligand file is 'ligand.mol2' and '-d' is used to designate the path of sequence database for HHblits. Other options can be found by '-h'.
The code of EGNA is under GPLv3.0.
The EGNA parameters are made availabe under a Creative Commons Attribution 4.0 International License.
