Extract protein-ligand interactions and their frequencies to score and predict the activity of a complex.
If not already installed: Install Docker.
A short manual how to install Docker can be found here: Docker Installation.
Either manually build the image using the provided Dockerfile:
docker image build -f Dockerfile -t michabirklbauer/pia:latest .OR pull it from dockerhub via michabirklbauer/pia:latest:
docker pull michabirklbauer/pia:latestOnce the image is built or downloaded you should create a directory that can be mounted to the container to share files e.g. in Windows 10 create a directory on drive C: called docker_share. You will have to add this directory in Docker Settings > Resources > File Sharing and restart Docker. Then the container can be run with:
docker run -v C:\docker_share:/exchange -p 8888:8888 -it michabirklbauer/pia:latestThis will mount the directory C:/docker_share to the directory /exchange in the container and files can be shared freely by copy-pasting into those directories. Python can be run normally in the container and PIA can be imported without needing to install any requirements. To get started copy PIAScript.py and the files you want to process into C:/docker_share. Navigate to /exchange in the container to run an analysis. For example, to build a scoring model we would do the following (files used are provided in the example_files directory):
# navigate to the exchange directory containing all files from docker_share
cd exchange
# run a scoring workflow
python3 PIAScript.py -m score -f 6hgv.pdb sEH_6hgv_results.sdf
# inspect training summary
cat sEH_6hgv_results*.txtThis will create scoring models, evaluation files and a summary of quality metrics in the /exchange and C:/docker_share directory.
To launch JupyterLab in the container you need to run the command:
jupyter lab --ip=0.0.0.0 --port=8888 --allow-rootJupyterLab can then be accessed from any browser via the given link.
- Mail: [email protected]
- Telegram: https://telegram.me/micha_birklbauer