Computer Simulations of Aggregation of Proteins and Peptides
Springer US
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| Chapter title |
Molecular Insights into the Effect of Metals on Amyloid Aggregation.
|
|---|---|
| Book title |
Computer Simulations of Aggregation of Proteins and Peptides
|
| Published in |
Methods in molecular biology, January 2022
|
| DOI | 10.1007/978-1-0716-1546-1_7 |
| Pubmed ID | |
| Book ISBNs |
978-1-07-161545-4, 978-1-07-161546-1
|
| Authors |
Miller, Yifat, Yifat Miller |
| Abstract |
Amyloid diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and type 2 diabetes (T2D) are characterized by accumulation of misfolded proteins' species, e.g., oligomers and fibrils. The formation of these species occurs via self-assemble of the misfolded proteins in a process which is named "aggregation." It is known that essential divalent metal ions initiate the aggregation of these misfolded proteins, and that specific concentrations of these metal ions may be implicated in the pathology of amyloid diseases. This chapter focuses on the effects of two of the most common divalent metal ions in the brain-Zn2+ and Cu2+, and while Zn2+ ion is known as a metal that is release from the pancreas. Specifically, the spotlight of this chapter illustrates recent computational molecular modelling studies that investigate the effect of the concentrations of metal ions on aggregation of the misfolded proteins amylin, amyloid β, and α-synuclein. The challenges for computational molecular modeling and future perspectives are discussed. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 4 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 50% |
| Researcher | 1 | 25% |
| Unknown | 1 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 1 | 25% |
| Neuroscience | 1 | 25% |
| Unknown | 2 | 50% |
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 18 February 2022.
All research outputs
#3,258,766
of 23,151,189 outputs
Outputs from Methods in molecular biology
#749
of 13,279 outputs
Outputs of similar age
#80,633
of 504,866 outputs
Outputs of similar age from Methods in molecular biology
#24
of 594 outputs
Altmetric has tracked 23,151,189 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,279 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 94% 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 504,866 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 83% of its contemporaries.
We're also able to compare this research output to 594 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.