Poisson Boltzmann & Jupyter: Python based biomolecular electrostatics solver.

Welcome!

Poisson-Boltzmann and Jupyter (PBJ) is a boundary element solver based on the Bempp-cl library. It provides an easy API for electrostatics calculations of biomolecules from a Jupyter notebook, with full access to all the nice (and fast!) features in Bempp-cl. It is built to be easily extensible, like a playground for new models!

PBJ may not only be the acronym of your favorite sandwich, but also your favorite Poisson-Boltzmann solver!

Installation

Download the repository and create a conda environment using the environment.yml file as

conda env create -f environment.yml

and you're good to go!

Docker

You can install PBJ in a ready-to-use Jupyter-Notebook Docker image based on the docker bempp image. To launch this image, simply run:

docker run -v $(pwd):/root/shared -w "/root/shared" -p 8888:8888 pescapil/pbj

Next, you can install and use PBJ within the notebook. Furthermore, the Docker image can be built locally and run as:

cd docker
docker build -f Dockerfile . -t pbj 
docker run -v $(pwd):/root/shared -w "/root/shared" -p 8888:8888 pbj

Documentation

This is work-in-progress, but you'll be able to find examples under notebooks/tutorials/.

The team

This code is developed by a research group based in the Mechanical Engineering Department of Universidad Técnica Federico Santa María. The main contributors are

• Stefan Search (@sdsearch): initial design and main functions
• Kenneth Styles (@kstylesc): implementation of first version and the SLIC model
• Miguel Godoy (@mgodoydiaz): implementation of firt version
• Sergio Urzúa (@urzuasergio): protein surface interacion features (under development)
• Ian Addison-Smith (@iaddison-smith): force calculation (under development)
• Christopher Cooper (@cdcooper84): PI

How to cite

Search, S. D., Cooper, C. D., van't Wout, E., "Towards optimal boundary integral formulations of the Poisson–Boltzmann equation for molecular electrostatics" J. Comput. Chem. 2022, 1. https://doi.org/10.1002/jcc.26825. Preprint on arXiv

Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.6.