Code for "Energy coupling and stoichiometry of Zn²⁺/H⁺ antiport by the cation diffusion facilitator YiiP"

This repository contains code used for some of the computational aspects of the paper

• Adel Hussein, Shujie Fan, Maria Lopez-Redondo, Ian Kenney, Xihui Zhang, Oliver Beckstein, and David L. Stokes. Energy coupling and stoichiometry of Zn²⁺/H⁺ antiport by the cation diffusion facilitator YiiP. eLife, Apr 2023. doi: 10.7554/elife.87167

Code and data are archived:

• Raw data files are archived in OSF project https://osf.io/r95qu (DOI 10.17605/OSF.IO/Y8BA2)
• This GitHub code repository is archived in Zenodo under DOI 10.5281/zenodo.8357619

The following packages are required to use these scripts.

• NumPy
• SciPy
• Matplotlib
• MDAnalysis
• tqdm
• seaborn
• pandas
• multibind
• CpHMD-Analysis (see https://github.com/Hendejac/CpHMD-Analysis for the original cphmdanalysis.py script/module)

analysis

angle.py, rms.py, and R210_D72.py are python scripts used to analyze equilibrium simulations, taking md.gro and md[].xtc as inputs, while the reference for this analysis is derived from the Protein Data Bank, accessible at https://www.rcsb.org/structure/5VRF.

cphmd.py is the python script for analyzing CpHMD simulations, with .log and .lamb files as inputs.

MST inference

The folders siteA and siteB each contain scripts and corresponding example input data for performing MST inference on site A and site B, respectively. While the scripts themselves are identical for both site A and site B, the distinction lies in their input and output files.

monte_carlo.py serves as the script responsible for executing a Monte Carlo process to refine the pKa values and binding free energies. It requires a .csv file that defines the states within the thermodynamic model, exemplified by state.csv, and an initial file, following the same format as result.csv. The .csv files located in the target folder as input data serves as the reference values for the Monte Carlo scheme. These files are reformatted experimental MST data, representing the data in Figure 4 of the paper.

analysis.ipynb is a jupyter-notebook showing how to use the output of the monte_carlo process, result.csv, to generate the figures in Figure 4 - figure supplement 5, and Figure 4 - figure supplement 6.The result.csv files in siteA and siteB folders are reformatted versions of the supplementary data figure4_figure-supplement5_source-data3.csv and figure4_figure-supplement6_source-data3.csv, respectively.