Literature DB >> 29444205

ssbio: a Python framework for structural systems biology.

Nathan Mih1,2, Elizabeth Brunk2, Ke Chen2, Edward Catoiu2, Anand Sastry2, Erol Kavvas2, Jonathan M Monk2, Zhen Zhang2, Bernhard O Palsson2.   

Abstract

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 http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb. Supplementary information: Supplementary data are available at Bioinformatics online.

Entities:  

Mesh:

Year:  2018        PMID: 29444205      PMCID: PMC6658713          DOI: 10.1093/bioinformatics/bty077

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  15 in total

Review 1.  The role of artificial intelligence in the battle against antimicrobial-resistant bacteria.

Authors:  Hul Juan Lau; Chern Hong Lim; Su Chern Foo; Hock Siew Tan
Journal:  Curr Genet       Date:  2021-02-13       Impact factor: 3.886

Review 2.  Towards gaining sight of multiscale events: utilizing network models and normal modes in hybrid methods.

Authors:  James M Krieger; Pemra Doruker; Ana Ligia Scott; David Perahia; Ivet Bahar
Journal:  Curr Opin Struct Biol       Date:  2020-07-01       Impact factor: 6.809

Review 3.  Path to improving the life cycle and quality of genome-scale models of metabolism.

Authors:  Yara Seif; Bernhard Ørn Palsson
Journal:  Cell Syst       Date:  2021-09-22       Impact factor: 11.091

4.  An atlas of human metabolism.

Authors:  Jonathan L Robinson; Pınar Kocabaş; Hao Wang; Pierre-Etienne Cholley; Daniel Cook; Avlant Nilsson; Mihail Anton; Raphael Ferreira; Iván Domenzain; Virinchi Billa; Angelo Limeta; Alex Hedin; Johan Gustafsson; Eduard J Kerkhoven; L Thomas Svensson; Bernhard O Palsson; Adil Mardinoglu; Lena Hansson; Mathias Uhlén; Jens Nielsen
Journal:  Sci Signal       Date:  2020-03-24       Impact factor: 8.192

5.  The Staphylococcus aureus Two-Component System AgrAC Displays Four Distinct Genomic Arrangements That Delineate Genomic Virulence Factor Signatures.

Authors:  Kumari S Choudhary; Nathan Mih; Jonathan Monk; Erol Kavvas; James T Yurkovich; George Sakoulas; Bernhard O Palsson
Journal:  Front Microbiol       Date:  2018-05-25       Impact factor: 5.640

6.  Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.

Authors:  David Heckmann; Colton J Lloyd; Nathan Mih; Yuanchi Ha; Daniel C Zielinski; Zachary B Haiman; Abdelmoneim Amer Desouki; Martin J Lercher; Bernhard O Palsson
Journal:  Nat Commun       Date:  2018-12-07       Impact factor: 14.919

7.  A computational knowledge-base elucidates the response of Staphylococcus aureus to different media types.

Authors:  Yara Seif; Jonathan M Monk; Nathan Mih; Hannah Tsunemoto; Saugat Poudel; Cristal Zuniga; Jared Broddrick; Karsten Zengler; Bernhard O Palsson
Journal:  PLoS Comput Biol       Date:  2019-01-09       Impact factor: 4.475

8.  Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes.

Authors:  Nathan Mih; Jonathan M Monk; Xin Fang; Edward Catoiu; David Heckmann; Laurence Yang; Bernhard O Palsson
Journal:  BMC Bioinformatics       Date:  2020-04-29       Impact factor: 3.169

9.  Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance.

Authors:  Erol S Kavvas; Edward Catoiu; Nathan Mih; James T Yurkovich; Yara Seif; Nicholas Dillon; David Heckmann; Amitesh Anand; Laurence Yang; Victor Nizet; Jonathan M Monk; Bernhard O Palsson
Journal:  Nat Commun       Date:  2018-10-17       Impact factor: 14.919

10.  Genome-scale metabolic modeling reveals key features of a minimal gene set.

Authors:  Jean-Christophe Lachance; Dominick Matteau; Joëlle Brodeur; Colton J Lloyd; Nathan Mih; Zachary A King; Thomas F Knight; Adam M Feist; Jonathan M Monk; Bernhard O Palsson; Pierre-Étienne Jacques; Sébastien Rodrigue
Journal:  Mol Syst Biol       Date:  2021-07       Impact factor: 11.429

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