KinaseDocker²

A PyMOL plugin with accompanying Docker image for kinase inhibitor binding and affinity prediction

KinaseDocker² is a computational tool that implements fully automated docking and scoring. The tool allows for docking in either AutoDock VinaGPU or DiffDock and subsequent scoring by a Deep Neural Network that has been trained on kinase-inhibitor docking poses. This tool can both be installed as a PyMOL plugin and used through the CLI.

In the backend, it uses a Docker image to run the GPU-accelerated VinaGPU, DiffDock and PyTorch DNN implementation. The instructions below assume you have a working GPU-enable Docker installation on your system. Refer to guides such as https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html for detailed Docker installation instructions.

How to install (PyMOL plugin):

1. Setup conda environment:

• Download the KinaseDocker.yml file
• Create environment

conda env create -f KinaseDocker.yml

• Activate environment

conda activate KinaseDocker

Or, if you really want to install it in an existing environment:

• Get PyMOL

conda install -c conda-forge PyMOL-open-source

• Install dependencies

conda install anaconda::h5py
pip install meeko==0.3.3 scipy docker pandas rdkit

2. Setup docker image:

• Download and load the docker image

docker pull apajanssen/kinasedocker2

3. Install plugin into PyMOL

• Download kinasedocker_plugin.zip
• Run PyMOL

pymol

• Go to Plugin > Plugin manager > Install New Plugin
• Click Choose file... and select the kinasedocker_plugin.zip
• Press Ok a bunch of times

How to install for only CLI-use:

1. Setup conda environment:

• Create environment

conda create -n MY_ENV python=3.9 

• Activate environment

conda activate MY_ENV

• Install dependecies

pip install meeko==0.3.3 scipy docker pandas rdkit

2. Setup docker image:

• Download the docker image: vina_diffdock_dnn.tar
• Load the image

docker load -i vina_diffdock_dnn.tar

3. Get the CLI:

• Download the files pipeline.py and kinase_data.csv
• Run the pipeline

python pipeline.py --help