For reporting bugs or suggesting new features/improvements to the code, please open an issue.
A toolkit that enables automatic generation of Martini forcefields for small organic molecules.
For a detailed account of the software, see:
Bereau and Kremer, J Chem Theory Comput, DOI:10.1021/acs.jctc.5b00056 (2015)
Please consider citing the paper if you find auto_martini useful in your research.
@article{bereau2015automartini,
author = {Bereau, Tristan and Kremer, Kurt},
title = {Automated parametrization of the coarse-grained MARTINI force field
for small organic molecules},
journal = {J Chem Theory Comput},
year = {2015},
volume = {11},
number = {6},
pages = {2783-2791},
doi = {10.1021/acs.jctc.5b00056}
}
For full documentation, click here.
- Tristan Bereau (University of Amsterdam, Netherlands)
- Kiran Kanekal (Max Planck Institute for Polymer Research, Mainz, Germany)
- Andrew Abi-Mansour
The main branch is now fully compatible with Python 3. For the original Python2-based version of the code used in the JCTC 2015 paper, see branch original_jctc2015.
You can install auto_martini with pip by running the following command from the source directory:
pip install .If you do not wish to clone the repo, you can instead run:
pip install git+https://github.com/tbereau/auto_martiniYou can install auto_martini with poetry by running the following command from the source directory:
poetry installThis will install auto_martini in a virtual environment, which you can activate via:
poetry shellTo exit the environment, simply run deactivate.
You can use conda-lock to install auto_martini as well. From the source directory run:
conda-lock -f pyproject.toml -k explicit --filename-template auto_martini-py3.11.conda.lock
This generates a conda lock file which you can use to create a new conda virtual environment:
conda create --name YOURENV --file auto_martini-py3.11.conda.lock
YOURENV is the name of your conda env, and the lock filename can change depending on the python interpreter version.
Finally activate your conda envrionment via:
conda activate YOURENV
Now you can install the pkg with pip by running from the source directory:
pip install .
To exit the environment, simply run conda deactivate.
To run the test cases and validate your installation, you will need to have pytest
installed. If you installed auto_martini with conda, then pytest should already be available in your environment.
To initiate testing, activate the virtual environment and run the following from the source directory:
pytest -v auto_martini/testsAll tests should pass within few minutes. If any of the tests fail, please open an issue.
You can invoke auto_martini from the command-line via:
python -m auto_martini [mode] [options]
By default, mode is set to 'run', which computes the MARTINI forcefield for a given molecule.
To display the usage-information (help), either supply -h, --help, or nothing to auto_martini:
usage: auto_martini [-h] [--mode {run,test}] [--sdf SDF | --smi SMI]
[--mol MOLNAME] [--aa AA] [--cg CG] [--top TOPFNAME] [-v]
[--fpred]
Generates Martini force field for atomistic structures of small organic molecules
optional arguments:
-h, --help show this help message and exit
--mode {run,test} mode: run (compute FF) or test (validate)
--sdf SDF SDF file of atomistic coordinates
--smi SMI SMILES string of atomistic structure
--mol MOLNAME Name of CG molecule
--aa AA filename of all-atom structure .gro file
--cg CG filename of coarse-grained structure .gro file
--top TOPFNAME filename of output topology file
-v, --verbose increase verbosity
--fpred Atomic partitioning prediction
Developers:
===========
Tristan Bereau (bereau [at] mpip-mainz.mpg.de)
Kiran Kanekal (kanekal [at] mpip-mainz.mpg.de)
Andrew Abi-Mansour (andrew.gaam [at] gmail.com)
To coarse-grain a molecule, simply provide its SMILES code (option --smi SMI) or a .SDF file (option '--sdf file.sdf). You also need to provide a name for the CG molecule (not longer than 5 characters) using the --mol option. For instance, to coarse grain guanazole, you can either obtain/generate (e.g., from Open Babel) an SDF file:
python -m auto_martini --sdf guanazole.sdf --mol GUA --top GUA.itp
(the name GUA is arbitrary) or use its SMILES code within double quotes
python -m auto_martini --smi "N1=C(N)NN=C1N" --mol GUA --top GUA.itp
In case no problem arises, it will output the gromacs GUA.itp file:
;;;; GENERATED WITH auto-martini
; INPUT SMILES: N1=C(N)NN=C1N
; Tristan Bereau (2014)
[moleculetype]
; molname nrexcl
GUA 2
[atoms]
; id type resnr residu atom cgnr charge smiles
1 SP2 1 GUA S01 1 0 ; Nc1ncnn1
2 SP2 1 GUA S02 2 0 ; Nc1ncnn1
[constraints]
; i j funct length
1 2 1 0.21
Optionally, the code can also output a corresponding .gro file for the coarse-grained coordinates
python -m auto_martini --smi "N1=C(N)NN=C1N" --mol GUA --cg gua.gro --top GUA.itp
Atomistic coordinates can be written using the --aa output.gro option.
For frequently encountered problems, see FEP.