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MELD (Modeling Employing Limited Data)

MELDxMD is a Bayesian inference approach to incorporate ambiguous and noisy data into simulations. We contribute and apply the method to solve problems like folding and binding of biomolecules. You can access it on github. MELD has recently been packaged as docker by NVIDIA (NGC) It can also be installed using conda-forge (directions from the MELD github page below):

First install Miniconda or miniforge by following the appropriate instructions.

If using miniconda, we recommend setting conda-forge as the default channel. (This is already enabled for miniforge.)

conda config --add channels conda-forge 
conda config --set channel_priority strict

We recommend installing MELD into a conda environment. You can name this however you want. We usually name this by the meld version or by the project name, e.g.

conda create -n my-meld-project python
conda activate my-meld-project
conda install meld

To cite use: 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.

MELD Tutorials

Tutorials will be added to GitHub at

Protein structure predictions from distance histograms
MELD installation using Conda

Protein-peptide binding predictions with MELD

Protein-DNA binding predictions with MELD

NMR Exchange Format Compatibility (NEF)

NEF aims to be a universal way of storing and sharing NMR data. For it to succed, software that uses NMR data needs to be able to read, process and write these type of files to generate structural knowledge. We have incorporated this into a set of scripts available through github, that read NEF files and generate all required input for MELD, producing structures in good agreement with experiments (