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Adding Diverse Noncanonical Backbones to Rosetta: Enabling Peptidomimetic Design

Overview of attention for article published in PLOS ONE, July 2013
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Title
Adding Diverse Noncanonical Backbones to Rosetta: Enabling Peptidomimetic Design
Published in
PLOS ONE, July 2013
DOI 10.1371/journal.pone.0067051
Pubmed ID
Authors

Kevin Drew, P. Douglas Renfrew, Timothy W. Craven, Glenn L. Butterfoss, Fang-Chieh Chou, Sergey Lyskov, Brooke N. Bullock, Andrew Watkins, Jason W. Labonte, Michael Pacella, Krishna Praneeth Kilambi, Andrew Leaver-Fay, Brian Kuhlman, Jeffrey J. Gray, Philip Bradley, Kent Kirshenbaum, Paramjit S. Arora, Rhiju Das, Richard Bonneau

Abstract

Peptidomimetics are classes of molecules that mimic structural and functional attributes of polypeptides. Peptidomimetic oligomers can frequently be synthesized using efficient solid phase synthesis procedures similar to peptide synthesis. Conformationally ordered peptidomimetic oligomers are finding broad applications for molecular recognition and for inhibiting protein-protein interactions. One critical limitation is the limited set of design tools for identifying oligomer sequences that can adopt desired conformations. Here, we present expansions to the ROSETTA platform that enable structure prediction and design of five non-peptidic oligomer scaffolds (noncanonical backbones), oligooxopiperazines, oligo-peptoids, [Formula: see text]-peptides, hydrogen bond surrogate helices and oligosaccharides. This work is complementary to prior additions to model noncanonical protein side chains in ROSETTA. The main purpose of our manuscript is to give a detailed description to current and future developers of how each of these noncanonical backbones was implemented. Furthermore, we provide a general outline for implementation of new backbone types not discussed here. To illustrate the utility of this approach, we describe the first tests of the ROSETTA molecular mechanics energy function in the context of oligooxopiperazines, using quantum mechanical calculations as comparison points, scanning through backbone and side chain torsion angles for a model peptidomimetic. Finally, as an example of a novel design application, we describe the automated design of an oligooxopiperazine that inhibits the p53-MDM2 protein-protein interaction. For the general biological and bioengineering community, several noncanonical backbones have been incorporated into web applications that allow users to freely and rapidly test the presented protocols (http://rosie.rosettacommons.org). This work helps address the peptidomimetic community's need for an automated and expandable modeling tool for noncanonical backbones.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 137 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Israel 1 <1%
Canada 1 <1%
Unknown 131 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 46 34%
Researcher 31 23%
Student > Bachelor 10 7%
Student > Master 9 7%
Student > Doctoral Student 6 4%
Other 18 13%
Unknown 17 12%
Readers by discipline Count As %
Chemistry 41 30%
Biochemistry, Genetics and Molecular Biology 30 22%
Agricultural and Biological Sciences 27 20%
Physics and Astronomy 4 3%
Computer Science 3 2%
Other 15 11%
Unknown 17 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 August 2013.
All research outputs
#12,587,003
of 22,714,025 outputs
Outputs from PLOS ONE
#97,310
of 193,925 outputs
Outputs of similar age
#95,792
of 194,441 outputs
Outputs of similar age from PLOS ONE
#2,357
of 4,759 outputs
Altmetric has tracked 22,714,025 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 193,925 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 194,441 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 4,759 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.