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Connecting Macroscopic Observables and Microscopic Assembly Events in Amyloid Formation Using Coarse Grained Simulations

Overview of attention for article published in PLoS Computational Biology, October 2012
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Title
Connecting Macroscopic Observables and Microscopic Assembly Events in Amyloid Formation Using Coarse Grained Simulations
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002692
Pubmed ID
Authors

Noah S. Bieler, Tuomas P. J. Knowles, Daan Frenkel, Robert Vácha

Abstract

The pre-fibrillar stages of amyloid formation have been implicated in cellular toxicity, but have proved to be challenging to study directly in experiments and simulations. Rational strategies to suppress the formation of toxic amyloid oligomers require a better understanding of the mechanisms by which they are generated. We report Dynamical Monte Carlo simulations that allow us to study the early stages of amyloid formation. We use a generic, coarse-grained model of an amyloidogenic peptide that has two internal states: the first one representing the soluble random coil structure and the second one the [Formula: see text]-sheet conformation. We find that this system exhibits a propensity towards fibrillar self-assembly following the formation of a critical nucleus. Our calculations establish connections between the early nucleation events and the kinetic information available in the later stages of the aggregation process that are commonly probed in experiments. We analyze the kinetic behaviour in our simulations within the framework of the theory of classical nucleated polymerisation, and are able to connect the structural events at the early stages in amyloid growth with the resulting macroscopic observables such as the effective nucleus size. Furthermore, the free-energy landscapes that emerge from these simulations allow us to identify pertinent properties of the monomeric state that could be targeted to suppress oligomer formation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Chile 2 2%
Netherlands 2 2%
United Kingdom 2 2%
Germany 1 1%
Switzerland 1 1%
Unknown 89 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 31%
Researcher 21 21%
Student > Master 12 12%
Student > Bachelor 6 6%
Student > Doctoral Student 4 4%
Other 16 16%
Unknown 10 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 28%
Physics and Astronomy 17 17%
Chemistry 14 14%
Biochemistry, Genetics and Molecular Biology 10 10%
Engineering 6 6%
Other 12 12%
Unknown 13 13%
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 11 November 2012.
All research outputs
#19,944,994
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#7,953
of 8,960 outputs
Outputs of similar age
#143,881
of 191,748 outputs
Outputs of similar age from PLoS Computational Biology
#92
of 107 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
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We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.