GIST-Tutorial

A tutorial for Grid Inhomogenous Solvation Theory (GIST) as implemented in AmberTool's cpptraj. The tutorial aims to teach how to apply GIST for small molecules and proteins, with biotin-streptavidin as a showcase example.

The current version of the manuscript is found on our GitHub page liedlab.github.io.

The tutorial is developed in line with LiveCoMS guidelines on Paper Writing as Code Development and will be further updated in correspondence with the community. If you notice any issues or have suggestions, please raise them as an Issue or write up a Pull Request.

Repository Content

This repository contains the following folders and files:

• code: Input files and scripts to run the Biotin/Streptavidin example shown in the manuscript.
• manuscript: LaTeX files for the manuscript and bibliography.
• manuscript/figures: Figures and plots used in the manuscript.
• output: GIST output for the example provided in the manuscript.
• github/workflows: A GitHub Actions workflow to automatically compile and publish the manuscript.

Dependencies and Installation

• cpptraj (Version 6.24 or higher)
• gisttools (Version 0.2 or higher)
• mdtraj (Version 1.9.7 or higher)
• numpy (Tested with version 1.23.5)
• pandas (Tested with version 1.5.3)

To install the dependencies, we recommend using mamba or conda. A python environment can then be created with the following command:

mamba create -n gist-tutorial python=3.10 numpy pandas mdtraj dacase::ambertools-dac=25
mamba activate gist-tutorial

Note that gisttools is not available via mamba/conda, and must be installed manually. You can do this by cloning its repository and installing it with pip:

git clone https://github.com/liedllab/gisttools.git
cd gisttools
pip install .

The molecular dynamics simulations used in the tutorial are hosted here:

The tutorial code is provided as a Jupyter Notebook at code/tutorial-gist.ipynb.
We recommend using JupyterLab or VS Code (with the Jupyter extensions) for editing and working with the notebook.

Molecular visualisations are generated with PyMol and input scripts are provided in the output/visualization folder.

Authors

In the same order as in the manuscript:

• Valentin J. Egger-Hoerschinger
• Franz Waibl
• Vjay Molino
• Helmut Carter
• Monica L. Fernández-Quintero
• Steven Ramsey
• Daniel R. Roe
• Klaus R. Liedl
• Michael K. Gilson
• Tom Kurtzman

The repository is currently managed by Valentin (@vhoer).

Citation

@article{EggerHoerschinger2025,
author = {Egger-Hoerschinger, Valentin J. and Waibl, Franz and Molino, Vjay and Carter, Helmut and Fernández-Quintero, Monica L. and Ramsey, Steven and Roe, Daniel R. and Liedl, Klaus R. and Gilson, Michael K. and Kurtzman, Tom},
title = {Quantifying Spatially Resolved Hydration Thermodynamics Using Grid Inhomogeneous Solvation Theory [Article v1.0]},
journal = {Living Journal of Computational Molecular Science},
volume = {6},
number = {1},
pages = {3059},
year = {2025},
doi = {11.33011/livecoms.6.1.3059},
}