↓ Skip to main content

MDexciteR: Enhanced Sampling Molecular Dynamics by Excited Normal Modes or Principal Components Obtained from Experiments

Overview of attention for article published in Journal of Chemical Theory and Computation, January 2023
Altmetric Badge

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 (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

twitter
14 X users

Readers on

mendeley
17 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Article details
Title
MDexciteR: Enhanced Sampling Molecular Dynamics by Excited Normal Modes or Principal Components Obtained from Experiments
Published in
Journal of Chemical Theory and Computation, January 2023
DOI 10.1021/acs.jctc.2c00599
Pubmed ID
Authors
Abstract

Molecular dynamics with excited normal modes (MDeNM) is an enhanced sampling method for exploring conformational changes in proteins with minimal biases. The excitation corresponds to injecting kinetic energy along normal modes describing intrinsic collective motions. Herein, we developed a new automated open-source implementation, MDexciteR (https://github.com/mcosta27/MDexciteR), enabling the integration of MDeNM with two commonly used simulation programs with GPU support. Second, we generalized the method to include the excitation of principal components calculated from experimental ensembles. Finally, we evaluated whether the use of coarse-grained normal modes calculated with elastic network representations preserved the performance and accuracy of the method. The advantages and limitations of these new approaches are discussed based on results obtained for three different protein test cases: two globular and a protein/membrane system.

Login to access the Attention Digest and the Sentiment Analysis related to this output.

Timeline Attention over time Attention Score history
Login to access the full charts related to this output.
Activity
Login to access the full charts related to this output.
X Demographics

X Demographics

The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Login to view Mendeley reader trends over time.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 4 24%
Researcher 3 18%
Professor 2 12%
Student > Master 2 12%
Student > Bachelor 1 6%
Other 1 6%
Unknown 4 24%
Readers by discipline
Readers by discipline Count As %
Chemistry 5 29%
Biochemistry, Genetics and Molecular Biology 3 18%
Chemical Engineering 1 6%
Business, Management and Accounting 1 6%
Agricultural and Biological Sciences 1 6%
Other 1 6%
Unknown 5 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 10 January 2023.
All research outputs
#6,192,196
of 34,804,214 outputs
Outputs from Journal of Chemical Theory and Computation
#1,225
of 8,700 outputs
Outputs of similar age
#92,314
of 533,373 outputs
Outputs of similar age from Journal of Chemical Theory and Computation
#21
of 152 outputs
Altmetric has tracked 34,804,214 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,700 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done well, scoring higher than 85% 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 533,373 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 82% of its contemporaries.
We're also able to compare this research output to 152 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.