A software package for molecular PACKing and Motion ANalysis (PACKMAN)
Project description
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PACKMAN: PACKing and Motion ANalysis
<img src=”https://github.com/Pranavkhade/PACKMAN/blob/master/docs/_static/gallary/logo.gif” width=”250”>
This package focuses on studying molecular structures and their dynamics using a simple yet informative property known as Protein Packing. Over the last few years, we have worked on several techniques to capture and quantify the protein packing, resulting in a few publications. This package has all the code to repeat and further develop these techniques.
DOCUMENTATION
[IMPORTANT : Documentation, Tutorials & More](https://py-packman.readthedocs.io)
[Hinge Prediction Web Server](https://packman.bb.iastate.edu/)
[hd-ANM Web Server](https://hdanm.bb.iastate.edu/)
[Reference for Hinge Prediction](https://doi.org/10.1016/j.jmb.2019.11.018)
[Reference for Compliance](https://doi.org/10.1002/prot.25968)
[Reference for hd-ANM](coming_soon)
INSTALLATION
1. Installing with pip (Recommended) ` pip install py-packman `
2. Installing from source ` git clone https://github.com/Pranavkhade/PACKMAN cd PACKMAN python setup.py install `
HOW TO USE
PACKMAN, along with its components, can be accessed via Graphical User Interface (GUI), Command-line Interface (CLI), and Application programming interface (API).
For the GUI, please run the following command. ` python -m packman gui ` OR ` python3 -m packman gui `
For the CLI and API, please read the tutorials & documentation.
PREREQUISITES
[numpy](http://www.numpy.org/)
[scipy](https://www.scipy.org/)
[networkx](https://networkx.github.io/)
[mlxtend](http://rasbt.github.io/mlxtend/)
[sklearn](https://scikit-learn.org/stable/)
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