/PyLipID

A python toolkit for analysing membrane protein-lipid interactions.

Primary LanguagePythonMIT LicenseMIT

PyLipID - A Python Package For Lipid Interactions Analysis

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PyLipID is a python package for analyzing lipid interactions with membrane proteins from Molecular Dynamics Simulations. PyLipID has the following main features, please check out the tutorials for examples and the documentations for the API functionalities:

  • Detection of binding sites via calculating community structures in the interactions networks.
  • Calculation of lipid koff and residence time for interaction with binding sites and residues.
  • Analysis of lipid interactions with binding sites and residues using a couple of metrics.
  • Generation of representative bound poses for binding sites.
  • Analysis of bound poses for binding sites via automated clustering scheme.
  • Adoption of a dual-cutoff scheme to overcome the 'rattling in cage' effect of coarse-grained simulations.
  • Generation of manuscript-ready figures for analysis.

PyLipID can be used from Jupyter (former IPython, recommended), or by writing Python scripts. The documentaion and tutorials can be found at pylipid.readthedocs.io.

Installation

We recommend installing PyLipID using the package installer pip:

pip install pylipid

Alternatively, PyLipID can be installed from the source code. The package is available for download on Github via:

git clone https://github.com/wlsong/PyLipID

Once the source code is downloaded, enter the source code directory and install the package as follow:

python setup.py install

Citation DOI for Citing PyEMMA

If you use PyLipID in scientific research, please cite the following paper:

@article{song_pylipid_2022,
        author = {Song, Wanling. and Corey, Robin A. and Ansell, T. Bertie. and
                    Cassidy, C. Keith. and Horrell, Michael R. and Duncan, Anna L.
                    and Stansfeld, Phillip J. and Sansom, Mark S.P.},
        title = {PyLipID: A Python package for analysis of protein-lipid interactions from MD simulations},
        journal = {J. Chem. Theory Comput},
        year = {2022},
        url = {https://doi.org/10.1021/acs.jctc.1c00708},
        doi = {10.1021/acs.jctc.1c00708},
        urldate = {2022-02-18},
}