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Generalized Fragment Picking in Rosetta: Design, Protocols and Applications

Overview of attention for article published in PLOS ONE, August 2011
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Title
Generalized Fragment Picking in Rosetta: Design, Protocols and Applications
Published in
PLOS ONE, August 2011
DOI 10.1371/journal.pone.0023294
Pubmed ID
Authors

Dominik Gront, Daniel W. Kulp, Robert M. Vernon, Charlie E. M. Strauss, David Baker

Abstract

The Rosetta de novo structure prediction and loop modeling protocols begin with coarse grained Monte Carlo searches in which the moves are based on short fragments extracted from a database of known structures. Here we describe a new object oriented program for picking fragments that greatly extends the functionality of the previous program (nnmake) and opens the door for new approaches to structure modeling. We provide a detailed description of the code design and architecture, highlighting its modularity, and new features such as extensibility, total control over the fragment picking workflow and scoring system customization. We demonstrate that the program provides at least as good building blocks for ab-initio structure prediction as the previous program, and provide examples of the wide range of applications that are now accessible.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 211 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
Korea, Republic of 2 <1%
Germany 2 <1%
Italy 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Russia 1 <1%
Serbia 1 <1%
Unknown 197 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 69 33%
Researcher 55 26%
Student > Master 18 9%
Student > Bachelor 15 7%
Student > Doctoral Student 8 4%
Other 26 12%
Unknown 20 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 68 32%
Biochemistry, Genetics and Molecular Biology 53 25%
Chemistry 32 15%
Computer Science 13 6%
Physics and Astronomy 4 2%
Other 17 8%
Unknown 24 11%