A package for double electron-electron resonance (DEER) and paramagnetic relaxation enhancement (PRE) predictions from molecular dynamics ensembles.
To install DEER-PREdict, use the PyPI package:
pip install DEERPREdict
or clone the repo:
git clone https://github.com/KULL-Centre/DEERpredict.git
cd DEERpredict
pip install -e .
The software requires Python 3.6-3.9.
In case of dependency issues, consider installing FRETpredict in a new environment
conda create -n myenv python=3.9 pip
conda activate myenv
pip install -e .
Run all the tests in one go
cd DEERpredict
python -m pytest
or run single tests, e.g.
cd DEERpredict
python -m pytest tests/test_PRE.py::test_ACBP
python -m pytest tests/test_DEER.py::test_T4L
Example of how to run PREpredict to calculate the intensity ratios and PRE rates for PDB code 1NTI (20 conformations) using the BASL MMMx rotamer library (see notebook). Available libraries for MTSSL, BASL, and MA-proxyl probes are listed in DEERPREdict/lib/libraries.yml
.
PRE = PREpredict(MDAnalysis.Universe('1nti.pdb'), residue=36, libname='BASL MMMx',
tau_t=.5*1e-9, log_file='calcPREs/log', temperature=298, z_cutoff=0.05,
atom_selection='H', Cbeta=False)
PRE.run(output_prefix='calcPREs/BASL', tau_t=.5e-9, delay=10e-3,
tau_c=2e-09, r_2=10, wh=750)
Example of how to run PREpredict to calculate the intensity ratios and PRE rates for PDB code 1NTI (20 conformations) using the MA-proxyl MMMx rotamer library.
PRE = PREpredict(MDAnalysis.Universe('1nti.pdb'), residue=36, libname='MA-proxyl MMMx',
tau_t=.5*1e-9, log_file='calcPREs/log', temperature=298, z_cutoff=0.05,
attract_scaling=2, atom_selection='H', Cbeta=False)
PRE.run(output_prefix='calcPREs/MAP', tau_t=.5e-9, delay=10e-3,
tau_c=2e-09, r_2=10, wh=750)
This project is licensed under the GNU General Public License version 3.0 (GPL-3.0). However, the rotamer libraries in DEERPREdict/lib
are modified versions of those from the MMMx program, and these modified libraries are licensed under the MIT License, as detailed in the LICENSE file. The rest of the project is licensed under the GPL-3.0, and any combination of GPL-3.0 licensed files with those under the MIT License will be subject to the terms of the GPL-3.0.
João M Martins (@joaommartins)
Kresten Lindorff-Larsen (@lindorff-larsen)
Tesei G, Martins JM, Kunze MBA, Wang Y, Crehuet R, and Lindorff-Larsen K (2021) DEER-PREdict: Software for efficient calculation of spin-labeling EPR and NMR data from conformational ensembles. PLOS Computational Biology 17(1): e1008551. https://doi.org/10.1371/journal.pcbi.1008551