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Relaxation mode analysis for molecular dynamics simulations of proteins

Overview of attention for article published in Biophysical Reviews, March 2018
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35 Mendeley
Title
Relaxation mode analysis for molecular dynamics simulations of proteins
Published in
Biophysical Reviews, March 2018
DOI 10.1007/s12551-018-0406-7
Pubmed ID
Authors

Ayori Mitsutake, Hiroshi Takano

Abstract

Molecular dynamics simulation is a powerful method for investigating the structural stability, dynamics, and function of biopolymers at the atomic level. In recent years, it has become possible to perform simulations on time scales of the order of milliseconds using special hardware. However, it is necessary to derive the important factors contributing to structural change or function from the complicated movements of biopolymers obtained from long simulations. Although some analysis methods for protein systems have been developed using increasing simulation times, many of these methods are static in nature (i.e., no information on time). In recent years, dynamic analysis methods have been developed, such as the Markov state model and relaxation mode analysis (RMA), which was introduced based on spin and homopolymer systems. The RMA method approximately extracts slow relaxation modes and rates from trajectories and decomposes the structural fluctuations into slow relaxation modes, which characterize the slow relaxation dynamics of the system. Recently, this method has been applied to biomolecular systems. In this article, we review RMA and its improved versions for protein systems.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 20%
Researcher 7 20%
Student > Master 5 14%
Student > Bachelor 4 11%
Professor 1 3%
Other 2 6%
Unknown 9 26%
Readers by discipline Count As %
Chemistry 10 29%
Agricultural and Biological Sciences 3 9%
Physics and Astronomy 3 9%
Pharmacology, Toxicology and Pharmaceutical Science 2 6%
Biochemistry, Genetics and Molecular Biology 2 6%
Other 4 11%
Unknown 11 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 March 2021.
All research outputs
#13,508,179
of 23,026,672 outputs
Outputs from Biophysical Reviews
#219
of 799 outputs
Outputs of similar age
#171,670
of 333,788 outputs
Outputs of similar age from Biophysical Reviews
#5
of 17 outputs
Altmetric has tracked 23,026,672 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 799 research outputs from this source. They receive a mean Attention Score of 2.7. This one has gotten more attention than average, scoring higher than 72% 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 333,788 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.