macha is a meta-tool designed to (semi-)automate certain tasks related to setting up MD simulation systems. It is divided into a CLI front-end, written in bash, and a backend written in Python. The latter is accessible either directly or through the CLI frontend.
Typical use cases include rerunning (modified) CHARMM-GUI scripts1 locally or preparing water boxes and complexes for further setup of free energy calculations with transformato23, starting from PDB files of separate proteins and/or ligands and/or complexes, e.g. from protein data banks such as the PDBe or the RCSB PDB.
macha relies on CHARMM-GUI input scripts1 for CHARMM4, as well as on ParmEd5 and OpenBabel6. Please, see the references at the bottom.
The python backend of macha is available as a conda package and can be installed (together with its dependencies) in a conda environment with:
conda install -c conda-forge macha
If desirable, the CLI interface bash script manual_charmm_system_setup.sh can subsequently be downloaded from the macha GitHub repository.
A full copy of macha can be obtained by cloning this repository with;
git clone git@github.com:akaupang/macha.git
Subsequently, it can be installed in an active python environment (e.g. a conda environment) by issuing pip install . in the git package base directory. The python environment should also contain the packages parmed, openbabel and natsort.
After installation, the user must set the path to a local cgenff binary in main.py (see also Usage) to be able to use CGenFF parameterization of ligands (not required for single- or double-stranded RNA, which are natively parameterized in CHARMM).
For the CLI to work, a few variables should be set at the top of manual_charmm_system_setup.sh. The macha_py_base must point to the base directory of the python backend for this to be usable, typically ~/miniconda3/envs/macha/lib/python3.11/site-packages/macha/main.py, if using the conda package. If not set in the package version of main.py, the path to cgenff can be set here using the variable cgenff_bin. A user can also choose to set the variable charmm_bin_man, if a particular CHARMM binary should be used (by default, charmm is assumed to be in the $PATH).
For convenient usage of the CLI frontend, we recommend setting an alias in your ~/.bashrc to macha, like so:
alias macha="/path/to/manual_charmm_system_setup.sh"
Make sure that the charmm binary is in the $PATH or you have set a location in the script.
export PATH="/path/to/charmm/bin:$PATH"
Please note: This an early incarnation of macha, which may or may not be suitable for general use. We assume no responsibility for your use of the provided code. The macha CLI assumes that you are working in a folder containing input scripts generated by CHARMM-GUI[1] and was designed to work with these scripts in the state they were provided in the years 2022-2023.
The CLI intends to keep up with developments in the Python backend, but may not always be up to date in terms of exposed functionality. Please inspect and/or use/modify the Python scripts directly if you encounter any problems.
A main purpose for the CLI frontend is to give quick access to rerunning particular steps of the CHARMM-GUI-derived CHARMM input scripts, or all of them consecutively. The CLI also gives access to the main run types of the Python backend. The menus function by single-key selections and their operation should be fairly self-explanatory.
Having set an alias as suggested above, issue macha in the working directory of choice. This directory should contain the CHARMM-GUI input scripts. Python3 needs to be in the $PATH or available through an active conda environment.
If you want to perform direct Python calls, copy main.py from its package location /path/to/macha/main.py (if the git repo was cloned), to the working directory. The working directory must contain a subdirectory called "data", which in turn contains a subdirectory called "original", in which PDB files of ligands, complexes or RNA should reside. These directory names can be changed by editing main.py. More advanced options, such as a segment ID filter to allow production of multimeric complexes and adjustable system pH for addition of hydrogens, are exposed in main.py and users with less typical use cases/input structures are encouraged to explore these. Proper documentation may follow at some point. For now, advanced users are referred to the source code (found in functions.py and in charmm_factory.py)
Call macha in the working directory of choice, if you are using the CLI.
The submenu "System creation for transformato" is accessed by clicking "t". Here, five run types are exposed (the corresponding direct Python calls are shown in parentheses);
- Make water boxes/complexes from ligands/proteins/complexes (
python3 main.py) - Make complexes (no water boxes) from ligands/proteins/complexes (
python3 main.py --nowaterbox) - Make water boxes (no complexes) from ligands/complexes (
python3 main.py --nocomplex) - Make water boxes/complexes from double-stranded RNA (
python3 main.py --rna) - Make water boxes (no complexes) from double- or single-stranded RNA (
python3 main.py --rna --nocomplex)
If you are using the CLI, before running system creation, the main.py script must be copied to the working directory. This is done by choosing menu option "1" in the system creation for transformato submenu (note that this overwrites any existing local copy). This will also copy the path to the local cgenff binary to this local copy of main.py, if the variable cgenff_bin has been set at the top of the bash script manual_charmm_system_setup.sh, AND if the cgenff_path in main.py has not been set in the package version of this file ../macha/macha/main.py.
One may then select the run type of choice by clicking a number from 2 - 6 (if using the CLI).
Note that mixed batches of input files are not supported, e.g. a typical run (for ligands/proteins/complexes) will fail to process input files with RNA as the ligand/guest, and vice versa; small-molecules will not be handled correctly by an RNA run.
Footnotes
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Jo, S.; Kim, T.; Iyer, V. G.; Im, W. CHARMM-GUI: A Web-Based Graphical User Interface for CHARMM. Journal of Computational Chemistry 2008, 29(11), 1859–1865. https://doi.org/10.1002/jcc.20945 and https://charmm-gui.org/ ↩ ↩2
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Wieder, M.; Fleck, M.; Braunsfeld, B.; Boresch, S. Alchemical Free Energy Simulations without Speed Limits. A Generic Framework to Calculate Free Energy Differences Independent of the Underlying Molecular Dynamics Program. Journal of Computational Chemistry 2022, 43(17), 1151–1160. https://doi.org/10.1002/jcc.26877 and https://github.com/wiederm/transformato ↩
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Karwounopoulos, J.; Wieder, M.; Boresch, S. Relative Binding Free Energy Calculations with Transformato: A Molecular Dynamics Engine-Independent Tool. Frontiers in Molecular Biosciences 2022, 9, 954638. https://doi.org/10.3389/fmolb.2022.954638. ↩
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Brooks, B. R.; Bruccoleri, R. E.; Olafson, B. D.; States, D. J.; Swaminathan, S.; Karplus, M. CHARMM: A Program for Macromolecular Energy, Minimization, and Dynamics Calculations. Journal of Computational Chemistry 1983, 4(2), 187–217. https://doi.org/10.1002/jcc.540040211 and https://academiccharmm.org/ ↩
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Shirts, M. R.; Klein, C.; Swails, J. M.; Yin, J.; Gilson, M. K.; Mobley, D. L.; Case, D. A.; Zhong, E. D. Lessons Learned from Comparing Molecular Dynamics Engines on the SAMPL5 Dataset. Journal of Computer-Aided Molecular Design 2017, 31(1), 147–161. https://doi.org/10.1007/s10822-016-9977-1 and https://github.com/ParmEd/ParmEd ↩
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O’Boyle, N. M.; Banck, M.; James, C. A.; Morley, C.; Vandermeersch, T.; Hutchison, G. R. Open Babel: An Open Chemical Toolbox. Journal of Cheminformatics 2011, 3(1), 33. https://doi.org/10.1186/1758-2946-3-33 and https://github.com/openbabel/openbabel ↩