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 8000 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 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
Finally activate your conda envrionment via:
conda activate YOURENV
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.