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.