ssbio 0.9.9.1

Tools to enable structural systems biology

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ssbio: A Framework for Structural Systems Biology
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Introduction
============

This Python package provides a collection of tools for people with questions in the realm of structural systems biology. The main goals of this package are to:

#. Provide an easy way to map genes to their encoded proteins sequences and structures
#. Directly link structures to genome-scale SBML models
#. Prepare structures for downstream analyses, such as their use in molecular modeling software
#. Demonstrate fully-featured Python scientific analysis environments in Jupyter notebooks

Example questions you can (start to) answer with this package:

- How can I determine the number of protein structures available for my list of genes?
- What is the best, representative structure for my protein?
- Where, in a metabolic network, do these proteins work?
- Where do popular mutations show up on a protein?
- How can I compare the structural features of entire proteomes?
- How can I zoom in and visualize the interactions happening in the cell at the molecular level?
- How do structural properties correlate with my experimental datasets?
- How can I improve the contents of my model with structural data?
- and more...

Installation
============

First install NGLview using pip:

.. code-block:: bash

pip install nglview

Then install ssbio:

.. code-block:: bash

pip install ssbio

**Updating**

.. code-block:: bash

pip install ssbio --upgrade

**Uninstalling**

.. code-block:: bash

pip uninstall ssbio


Dependencies
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See: `Software Installations <https://github.com/SBRG/ssbio/wiki/Software-Installations>`_ for additional programs to install. Most of these additional programs are used to predict or calculate properties of proteins.


Tutorials
=========

Check out some Jupyter notebook tutorials at :ref:`protein` and :ref:`gempro`.


Citation
========

The manuscript for the ``ssbio`` package can be found and cited at [1]_.

.. [1] Mih, N. et al. ssbio: A Python Framework for Structural Systems Biology. bioRxiv 165506 (2017). doi:10.1101/165506