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Modelling the Self-Assembly of Elastomeric Proteins Provides Insights into the Evolution of Their Domain Architectures

Overview of attention for article published in PLoS Computational Biology, March 2012
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Title
Modelling the Self-Assembly of Elastomeric Proteins Provides Insights into the Evolution of Their Domain Architectures
Published in
PLoS Computational Biology, March 2012
DOI 10.1371/journal.pcbi.1002406
Pubmed ID
Authors

Hongyan Song, John Parkinson

Abstract

Elastomeric proteins have evolved independently multiple times through evolution. Produced as monomers, they self-assemble into polymeric structures that impart properties of stretch and recoil. They are composed of an alternating domain architecture of elastomeric domains interspersed with cross-linking elements. While the former provide the elasticity as well as help drive the assembly process, the latter serve to stabilise the polymer. Changes in the number and arrangement of the elastomeric and cross-linking regions have been shown to significantly impact their assembly and mechanical properties. However, to date, such studies are relatively limited. Here we present a theoretical study that examines the impact of domain architecture on polymer assembly and integrity. At the core of this study is a novel simulation environment that uses a model of diffusion limited aggregation to simulate the self-assembly of rod-like particles with alternating domain architectures. Applying the model to different domain architectures, we generate a variety of aggregates which are subsequently analysed by graph-theoretic metrics to predict their structural integrity. Our results show that the relative length and number of elastomeric and cross-linking domains can significantly impact the morphology and structural integrity of the resultant polymeric structure. For example, the most highly connected polymers were those constructed from asymmetric rods consisting of relatively large cross-linking elements interspersed with smaller elastomeric domains. In addition to providing insights into the evolution of elastomeric proteins, simulations such as those presented here may prove valuable for the tuneable design of new molecules that may be exploited as useful biomaterials.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Israel 1 2%
United Kingdom 1 2%
Unknown 39 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 23%
Researcher 9 21%
Student > Master 5 12%
Student > Bachelor 3 7%
Other 3 7%
Other 10 23%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 30%
Engineering 6 14%
Physics and Astronomy 6 14%
Biochemistry, Genetics and Molecular Biology 3 7%
Chemistry 2 5%
Other 9 21%
Unknown 4 9%
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 02 March 2012.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#8,565
of 8,960 outputs
Outputs of similar age
#152,817
of 168,144 outputs
Outputs of similar age from PLoS Computational Biology
#100
of 109 outputs
Altmetric has tracked 25,373,627 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 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 1st percentile – i.e., 1% 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 168,144 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
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.