OctSurf: Efficient Hierarchical Voxel-based Molecular Surface Representation for the Protein-Ligand Affinity Prediction.

Please cite:
Liu, Q., Wang, P. S., Zhu, C., Gaines, B. B., Zhu, T., Bi, J., & Song, M. (2021). OctSurf: Efficient hierarchical voxel-based molecular surface representation for protein-ligand affinity prediction. Journal of Molecular Graphics and Modelling, 105, 107865.

@article{liu2021octsurf,
  title={OctSurf: Efficient hierarchical voxel-based molecular surface representation for protein-ligand affinity prediction},
  author={Liu, Qinqing and Wang, Peng-Shuai and Zhu, Chunjiang and Gaines, Blake Blumenfeld and Zhu, Tan and Bi, Jinbo and Song, Minghu},
  journal={Journal of Molecular Graphics and Modelling},
  volume={105},
  pages={107865},
  year={2021},
  publisher={Elsevier}
}

Disclaimer

This is the copied repository for OctSurf: Efficient Hierarchical Voxel-based Molecular Surface Representation for the Protein-Ligand Affinity Prediction. I am a user of OctSurf and found the original repository is under Enterprise GitHub, which is not easy to share and use for general GitHub users. Thus, based on the MIT License, I copied the original repository to my repository and made it public.

The below readme is copied from the original repository.

Experiments

Download PDBbind

Download PDBbind general, refined, and core(CASF) from http://www.pdbbind.org.cn.
Also fix some minor problems in data(replace several mol2 files by transforming sdf, and remove the CONECT with index 0 in pdb file).

cd pdbbind
bash data_download.sh
cd ..

Set-up Enviroment

Install packages and compile the required tools, e.g. the java tool for generating surface points, the C++ code for octree, and the operation (convolution etc.) API that work with tensorflow. (cmake/3.10.2, gcc/5.4.0, cuda/10.1)

# compile java
cd pdbbind/java
javac -cp cdk-2.3-SNAPSHOT.jar Surface_for_single.java
cd ../..

# set-up TF enviroment and compile cpp
conda create -n OctSurf_env tensorflow-gpu==1.14.0
conda activate OctSurf_env
conda install -c conda-forge openbabel==3.0.0
conda install -c conda-forge yacs tqdm
pip install scikit-learn
pip install matplotlib
pip install seaborn
# uncomment if want to visualize in Paraview
# pip install vtk==9.0.3
# uncomment if cuda is not installed (or path is not found)
# conda install nvidia/label/cuda-11.3.0::cuda-toolkit


cd octree/external && git clone --recursive https://github.com/wang-ps/octree-ext.git
cd .. && mkdir build && cd build
cmake -D CMAKE_CUDA_COMPILER=[cuda_nvcc_path] .. -DUSE_CUDA=ON && cmake --build . --config Release
export PATH=`pwd`:$PATH

cd ../../tensorflow/libs
python build.py --cuda [cuda_path]
cd ../../

Octree Generation Example (Optional)

Provide one example data 1A1E from refined-set.
Following steps can generate the points and build the OctSurf. (Default density for points is 6, and depth for OctSurf is 10. Can be specified in octree_for_single.sh file)
We also provide python tool to parse the generated OctSurf, and generate vtk files that can be visualized in Paraview.

cd pdbbind/data_example/pdbbind
cp ../../bash_scripts/octree_for_single.sh .
cd 1a1e
bash ../octree_for_single.sh
cd octree_folder
ls -lh
cd ../../../../

# parse by python, and visualization(optional)
cd python
python octree_parse.py
cd ../

CNN Modeling

prepare the data for modeling

First it will generate the points file for each complex in general, refined, core set. (The density of points can be specificed in data_prepare_points.sh, low resolution OctSurf can use low density points to accelerate the process, here for depth=6 model, we use density 3.)
Then the points and labels will be transformed into tfrecords file.

bash data_prepare_model.sh
cd ..

train model

Specify the config files (the network architecture, the input/log path, iterations etc.)

cd tensorflow/script
python run_cls.py --config configs/train_resnet_depth6.yaml

test performance

Specify the config files (the path for pretrained model/test dataset, network architecture, iterations etc)
Test the pre-trained model on test dataset, and report the performance.

python test_reg_model.py --config configs/test_resnet_depth6.yaml

Acknowledgments

Code is inspired by O-CNN.

The code is released under the MIT license.
Please contact us (Qinqing Liu qinqing.liu@uconn.edu, Minghu Song minghu.song@uconn.edu ) if you have any problems about our implementation.