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Peptide Self-Assembly

Overview of attention for book
Attention for Chapter 5: Replica Exchange Molecular Dynamics: A Practical Application Protocol with Solutions to Common Problems and a Peptide Aggregation and Self-Assembly Example
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)

Mentioned by

news
1 news outlet
twitter
2 tweeters

Citations

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1 Dimensions

Readers on

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101 Mendeley
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Chapter title
Replica Exchange Molecular Dynamics: A Practical Application Protocol with Solutions to Common Problems and a Peptide Aggregation and Self-Assembly Example
Chapter number 5
Book title
Peptide Self-Assembly
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7811-3_5
Pubmed ID
Book ISBNs
978-1-4939-7809-0, 978-1-4939-7811-3
Authors

Ruxi Qi, Guanghong Wei, Buyong Ma, Ruth Nussinov

Abstract

Protein aggregation is associated with many human diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and type II diabetes (T2D). Understanding the molecular mechanism of protein aggregation is essential for therapy development. Molecular dynamics (MD) simulations have been shown as powerful tools to study protein aggregation. However, conventional MD simulations can hardly sample the whole conformational space of complex protein systems within acceptable simulation time as it can be easily trapped in local minimum-energy states. Many enhanced sampling methods have been developed. Among these, the replica exchange molecular dynamics (REMD) method has gained great popularity. By combining MD simulation with the Monte Carlo algorithm, the REMD method is capable of overcoming high energy-barriers easily and of sampling sufficiently the conformational space of proteins. In this chapter, we present a brief introduction to REMD method and a practical application protocol with a case study of the dimerization of the 11-25 fragment of human islet amyloid polypeptide (hIAPP(11-25)), using the GROMACS software. We also provide solutions to problems that are often encountered in practical use, and provide some useful scripts/commands from our research that can be easily adapted to other systems.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 101 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 30%
Researcher 13 13%
Student > Doctoral Student 11 11%
Student > Master 9 9%
Student > Bachelor 6 6%
Other 11 11%
Unknown 21 21%
Readers by discipline Count As %
Chemistry 30 30%
Biochemistry, Genetics and Molecular Biology 13 13%
Agricultural and Biological Sciences 8 8%
Engineering 7 7%
Chemical Engineering 5 5%
Other 16 16%
Unknown 22 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 20 January 2020.
All research outputs
#2,145,732
of 16,641,846 outputs
Outputs from Methods in molecular biology
#407
of 9,624 outputs
Outputs of similar age
#55,120
of 282,599 outputs
Outputs of similar age from Methods in molecular biology
#3
of 3 outputs
Altmetric has tracked 16,641,846 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,624 research outputs from this source. They receive a mean Attention Score of 2.7. This one has done particularly well, scoring higher than 95% of its peers.
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 282,599 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.