Deep Graph Attention Neural Network (DGANN)

1. Installation

Start by grabbing this source codes:

git clone https://github.com/coffee19850519/PPDocking

Environment

Use python virutal environment with conda.

conda create -n PPDocking python=3.7
conda activate PPDocking

Then install all python packages in bash.

pip install -r requirements.txt

2. Examples

Use our trained model

You can directly use our trained model to score your docking conformations. You can find the model in the package subfolder /example/model/0fold_classification_model.pt. You can find the data in the package subfolder /example/data/0_classification_test.pt.

Quick start

You can use data and model we prepared in example/data and example/model folder with type:

python import_data.py

Test your own datasets

Requirements for preparing data:

• Use the following file structure

  example/
  |__data/
  |__model/
  |__raw_data/
     |__pdb/
     |__pssm/
     |__node_feature/
     |__caseID.lst
  

  The data folder contains the processed data files , model contains the model files of docking models. In raw_data folder, the pdb folder contains the PDB files of docking models, pssm contains the PSSM files, and node_feature contains the graph features files. The caseID.lst is a list of class ID and PDB file name for each docking model, like 7CEI.

• Biopython can be used to process PDB file. PSSMGen can be used to get consistent PSSM and PDB files. There are already installed along with PPDock.

Prepare datasets

You should run compute_pssm.py to generate side chain information first.

python ./pssm_generating/compute_pssm.py

Then you should run generate_node_feature.py to generate side chain information.

python ./feature_extraction/generate_node_feature.py

Test datasets

Finally, put data files in example/data and you can use the model we prepared example/model folder with type:

python import_data.py