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Integrating protein structural dynamics and evolutionary analysis with Bio3D

Overview of attention for article published in BMC Bioinformatics, December 2014
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Title
Integrating protein structural dynamics and evolutionary analysis with Bio3D
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0399-6
Pubmed ID
Authors

Lars Skjærven, Xin-Qiu Yao, Guido Scarabelli, Barry J Grant

Abstract

BackgroundPopular bioinformatics approaches for studying protein functional dynamics include comparisons of crystallographic structures, molecular dynamics simulations and normal mode analysis. However, determining how observed displacements and predicted motions from these traditionally separate analyses relate to each other, as well as to the evolution of sequence, structure and function within large protein families, remains a considerable challenge. This is in part due to the general lack of tools that integrate information of molecular structure, dynamics and evolution.ResultsHere, we describe the integration of new methodologies for evolutionary sequence, structure and simulation analysis into the Bio3D package. This major update includes unique high-throughput normal mode analysis for examining and contrasting the dynamics of related proteins with non-identical sequences and structures, as well as new methods for quantifying dynamical couplings and their residue-wise dissection from correlation network analysis. These new methodologies are integrated with major biomolecular databases as well as established methods for evolutionary sequence and comparative structural analysis. New functionality for directly comparing results derived from normal modes, molecular dynamics and principal component analysis of heterogeneous experimental structure distributions is also included. We demonstrate these integrated capabilities with example applications to dihydrofolate reductase and heterotrimeric G-protein families along with a discussion of the mechanistic insight provided in each case.ConclusionsThe integration of structural dynamics and evolutionary analysis in Bio3D enables researchers to go beyond a prediction of single protein dynamics to investigate dynamical features across large protein families. The Bio3D package is distributed with full source code and extensive documentation as a platform independent R package under a GPL2 license from http://thegrantlab.org/bio3d/.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 304 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Norway 2 <1%
France 1 <1%
Italy 1 <1%
Germany 1 <1%
Czechia 1 <1%
Brazil 1 <1%
Spain 1 <1%
United Kingdom 1 <1%
Other 0 0%
Unknown 293 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 79 26%
Researcher 51 17%
Student > Master 40 13%
Student > Bachelor 31 10%
Professor 13 4%
Other 47 15%
Unknown 43 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 70 23%
Biochemistry, Genetics and Molecular Biology 69 23%
Chemistry 44 14%
Computer Science 18 6%
Engineering 10 3%
Other 35 12%
Unknown 58 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 05 January 2015.
All research outputs
#13,418,483
of 22,774,233 outputs
Outputs from BMC Bioinformatics
#4,192
of 7,276 outputs
Outputs of similar age
#178,015
of 361,216 outputs
Outputs of similar age from BMC Bioinformatics
#61
of 135 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,276 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 38th percentile – i.e., 38% 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 361,216 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 135 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.