Ssbio: A Python framework for structural systems biology

Nathan Mih*, Elizabeth Brunk, Ke Chen, Edward Catoiu, Anand Sastry, Erol Kavvas, Jonathan M. Monk, Zhen Zhang, Bernhard O. Palsson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Summary Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows. Availability and implementation ssbio is implemented in Python and available to download under the MIT license at Documentation and Jupyter notebook tutorials are available at Interactive notebooks can be launched using Binder at Supplementary informationSupplementary dataare available at Bioinformatics online.

Original languageEnglish
Pages (from-to)2155-2157
Number of pages3
Issue number12
Publication statusPublished - 15 Jun 2018

Bibliographical note

Funding Information:
This work was supported by the Novo Nordisk Foundation Center for Biosustainability [NNF10CC1016517 to N.M., K.C., E.C. and A.S.]; the Swiss National Science Foundation [p2elp2_148961 to E.B.]; and the National Institute of General Medical Sciences of the National Institutes of Health [U01-GM102098 to B.O.P., 1-U01-AI124316-01 to J.M.M. and E.K.].

Publisher Copyright:
© The Author(s) 2018.


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