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A Real-Time All-Atom Structural Search Engine for Proteins

Overview of attention for article published in PLoS Computational Biology, July 2014
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Title
A Real-Time All-Atom Structural Search Engine for Proteins
Published in
PLoS Computational Biology, July 2014
DOI 10.1371/journal.pcbi.1003750
Pubmed ID
Authors

Gabriel Gonzalez, Brett Hannigan, William F. DeGrado

Abstract

Protein designers use a wide variety of software tools for de novo design, yet their repertoire still lacks a fast and interactive all-atom search engine. To solve this, we have built the Suns program: a real-time, atomic search engine integrated into the PyMOL molecular visualization system. Users build atomic-level structural search queries within PyMOL and receive a stream of search results aligned to their query within a few seconds. This instant feedback cycle enables a new "designability"-inspired approach to protein design where the designer searches for and interactively incorporates native-like fragments from proven protein structures. We demonstrate the use of Suns to interactively build protein motifs, tertiary interactions, and to identify scaffolds compatible with hot-spot residues. The official web site and installer are located at http://www.degradolab.org/suns/ and the source code is hosted at https://github.com/godotgildor/Suns (PyMOL plugin, BSD license), https://github.com/Gabriel439/suns-cmd (command line client, BSD license), and https://github.com/Gabriel439/suns-search (search engine server, GPLv2 license).

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 5%
United States 2 5%
Mexico 1 2%
Unknown 39 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 30%
Student > Ph. D. Student 10 23%
Student > Bachelor 8 18%
Other 3 7%
Student > Postgraduate 3 7%
Other 5 11%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 34%
Biochemistry, Genetics and Molecular Biology 8 18%
Chemistry 7 16%
Computer Science 3 7%
Physics and Astronomy 3 7%
Other 5 11%
Unknown 3 7%