ssbio: A Framework for Structural Systems Biology¶
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 hundreds or thousands of genes to their encoded protein sequences and structures
- Directly link protein structures to genome-scale metabolic models
- 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 do structural properties correlate with my experimental datasets?
- How can I improve the contents of my metabolic model with structural data?
Try it without installing¶
Binder notebooks are still in beta, but they mostly work! Third-party programs are also preinstalled in the Binder notebooks except I-TASSER and TMHMM due to licensing restrictions.
First install NGLview using pip, then install ssbio
pip install nglview jupyter-nbextension enable nglview --py --sys-prefix pip install ssbio
pip install ssbio --upgrade
pip uninstall ssbio
The manuscript for the ssbio package can be found and cited at .
|||Mih N, Brunk E, Chen K, Catoiu E, Sastry A, Kavvas E, Monk JM, Zhang Z, Palsson BO. 2018. ssbio: A Python Framework for Structural Systems Biology. Bioinformatics. https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty077/4850940.|
Table of Contents¶
- Getting Started
- The GEM-PRO Pipeline
- The Protein Class
- The StructProp Class
- The SeqProp Class
- Index: Software
- Protein structure predictions
- Protein structure calculations
- Protein sequence predictions
- Protein sequence calculations
- Index: Tutorials
- Python API