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Fast design of arbitrary length loops in proteins using InteractiveRosetta

Overview of attention for article published in BMC Bioinformatics, September 2018
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Title
Fast design of arbitrary length loops in proteins using InteractiveRosetta
Published in
BMC Bioinformatics, September 2018
DOI 10.1186/s12859-018-2345-5
Pubmed ID
Authors

William F. Hooper, Benjamin D. Walcott, Xing Wang, Christopher Bystroff

Abstract

With increasing interest in ab initio protein design, there is a desire to be able to fully explore the design space of insertions and deletions. Nature inserts and deletes residues to optimize energy and function, but allowing variable length indels in the context of an interactive protein design session presents challenges with regard to speed and accuracy. Here we present a new module (INDEL) for InteractiveRosetta which allows the user to specify a range of lengths for a desired indel, and which returns a set of low energy backbones in a matter of seconds. To make the loop search fast, loop anchor points are geometrically hashed using C α-C α and C β-C β distances, and the hash is mapped to start and end points in a pre-compiled random access file of non-redundant, protein backbone coordinates. Loops with superposable anchors are filtered for collisions and returned to InteractiveRosetta as poly-alanine for display and selective incorporation into the design template. Sidechains can then be added using RosettaDesign tools. INDEL was able to find viable loops in 100% of 500 attempts for all lengths from 3 to 20 residues. INDEL has been applied to the task of designing a domain-swapping loop for T7-endonuclease I, changing its specificity from Holliday junctions to paranemic crossover (PX) DNA.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 24%
Student > Master 5 20%
Student > Ph. D. Student 3 12%
Student > Bachelor 3 12%
Student > Doctoral Student 2 8%
Other 3 12%
Unknown 3 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 36%
Agricultural and Biological Sciences 4 16%
Chemistry 4 16%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Medicine and Dentistry 1 4%
Other 1 4%
Unknown 5 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 25 September 2018.
All research outputs
#20,533,782
of 23,103,903 outputs
Outputs from BMC Bioinformatics
#6,905
of 7,329 outputs
Outputs of similar age
#296,205
of 340,828 outputs
Outputs of similar age from BMC Bioinformatics
#98
of 109 outputs
Altmetric has tracked 23,103,903 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,329 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.