Modeling with limited data
JL MacCallum, A Perez, and KA Dill, Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference, PNAS, 2015, 112(22), pp. 6985-6990.
Release versions are built here and can be installed from the maccallum_lab anaconda channel.
The preferred way to install is:
conda config --add channels maccallum_lab omnia
conda install meld-cuda{VER}
where VER is currently one of 75, 80, 90, or 92.
This will install MELD and all of its dependencies.
Test versions of MELD are built automatically. Current status:
Github master: 
MELD requires a CUDA compatible GPU.
- ambermini or ambertools
- netcdf4
- mpi4py
- openmm
- CUDA Toolkit
- python >= 3.6
- numpy
- scipy
- sklearn
- parmed
To install the python portion:
python setup.py install
To install the C++ / CUDA portion:
cd plugin
mkdir build
cd build
ccmake ..
make install
make PythonInstall
Documentation will eventually be at project website, but this is currently a placeholder.