biasNet

Scaffold Analysis of Ligands Causing GPCR Bias Signaling

Website

Table of Contents

• Requirements
• Installation and Initialization
  • Docker
    • Run Docker Images
    • Build your own images
  • Manual Start
• Contributors
• Funding
• Acknowledgements
• License

Requirements

At the moment, a standard machine with CPUs will work.

Installation and Initialization

Biasnet runs in two ways: Docker and manually running from source. With Docker, you can either pull an image from DockerHub, or build one on your own.

Docker

To install Docker, just follow the docker documentation.

1. Run Docker Images

The latest biasNet image is available on the Docker Hub.

• docker pull sirimullalab/biasnet:latest
• docker run -p 5000:5000 sirimullalab/biasnet:latest
• curl -F smiles='ClC1=CC=C(C=C1)C2=NOC3=C2CCNCC3' localhost:5000/predict OR curl -d 'smiles=ClC1=CC=C(C=C1)C2=NOC3=C2CCNCC3' localhost:5000/predict

2. Build your own images

1. git clone this repo
2. cd /path/to/this/repo
3. docker build --build-arg USER=$USER --build-arg UID=$UID --build-arg GID=$GID -t biasnet .
4. docker run --rm biasnet:latest --smiles <compound_smiles>

Manual Start

1. Install Miniconda, for your operating system, from https://conda.io/miniconda.html
2. git clone this repo
3. cd /path/to/this/repo
4. conda env create -f environment.yml
5. conda activate biasnet (or source activate biasnet for older versions of conda)`
6. Example:

• python3 run_biasnet.py --smiles "ClC1=CC=C(C=C1)C2=NOC3=C2CCNCC3"

Contributors

Govinda KC, Jason Sanchez, Suman Sirimulla

Acknowledgments

Suman Sirimulla acknowledge support from the National Science Foundation with NSF-PREM grant #DMR-1827745.

License

MIT License