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Computer Simulations of Aggregation of Proteins and Peptides

Overview of attention for book
Computer Simulations of Aggregation of Proteins and Peptides
Springer US
Attention for Chapter: Molecular Dynamics Simulations of Protein Aggregation: Protocols for Simulation Setup and Analysis with Markov State Models and Transition Networks.
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Chapter title
Molecular Dynamics Simulations of Protein Aggregation: Protocols for Simulation Setup and Analysis with Markov State Models and Transition Networks.
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_12
Pubmed ID
Book ISBNs
978-1-07-161545-4, 978-1-07-161546-1
Authors

Samantray, Suman, Schumann, Wibke, Illig, Alexander-Maurice, Carballo-Pacheco, Martin, Paul, Arghadwip, Barz, Bogdan, Strodel, Birgit

Abstract

Protein disorder and aggregation play significant roles in the pathogenesis of numerous neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases. The end products of the aggregation process in these diseases are highly structured amyloid fibrils. Though in most cases, small, soluble oligomers formed during amyloid aggregation are the toxic species. A full understanding of the physicochemical forces that drive protein aggregation is thus required if one aims for the rational design of drugs targeting the formation of amyloid oligomers. Among a multitude of biophysical and biochemical techniques that are employed for studying protein aggregation, molecular dynamics (MD) simulations at the atomic level provide the highest temporal and spatial resolution of this process, capturing key steps during the formation of amyloid oligomers. Here we provide a step-by-step guide for setting up, running, and analyzing MD simulations of aggregating peptides using GROMACS. For the analysis, we provide the scripts that were developed in our lab, which allow to determine the oligomer size and inter-peptide contacts that drive the aggregation process. Moreover, we explain and provide the tools to derive Markov state models and transition networks from MD data of peptide aggregation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 87 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 23%
Researcher 15 17%
Student > Bachelor 8 9%
Student > Master 5 6%
Unspecified 4 5%
Other 7 8%
Unknown 28 32%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 20%
Chemistry 8 9%
Engineering 5 6%
Unspecified 4 5%
Agricultural and Biological Sciences 4 5%
Other 19 22%
Unknown 30 34%
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 16 February 2022.
All research outputs
#20,563,454
of 23,138,859 outputs
Outputs from Methods in molecular biology
#10,034
of 13,274 outputs
Outputs of similar age
#413,696
of 504,712 outputs
Outputs of similar age from Methods in molecular biology
#398
of 595 outputs
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So far Altmetric has tracked 13,274 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 595 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.