DEER-PREdict

Overview

A package for double electron-electron resonance (DEER) and paramagnetic relaxation enhancement (PRE) predictions from molecular dynamics ensembles.

Installation

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 .

Documentation

Testing

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

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)

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)

License

This project is licensed under the GNU General Public License version 3.0 (GPL-3.0). However, the rotamer libraries 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.

Authors

Giulio Tesei (@gitesei)

João M Martins (@joaommartins)

Micha BA Kunze (@mbakunze)

Ramon Crehuet (@rcrehuet)

Kresten Lindorff-Larsen (@lindorff-larsen)

Article

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