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Identifying EGFR mutation-induced drug resistance based on alpha shape model analysis of the dynamics

Overview of attention for article published in Proteome Science, September 2016
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Title
Identifying EGFR mutation-induced drug resistance based on alpha shape model analysis of the dynamics
Published in
Proteome Science, September 2016
DOI 10.1186/s12953-016-0102-0
Pubmed ID
Authors

Lichun Ma, Bin Zou, Hong Yan

Abstract

Epidermal growth factor receptor (EGFR) mutation-induced drug resistance is a difficult problem in lung cancer treatment. Studying the molecular mechanisms of drug resistance can help to develop corresponding treatment strategies and benefit new drug design. In this study, Rosetta was employed to model the EGFR mutant structures. Then Amber was carried out to conduct molecular dynamics (MD) simulation. Afterwards, we used Computational Geometry Algorithms Library (CGAL) to compute the alpha shape model of the mutants. We analyzed the EGFR mutation-induced drug resistance based on the motion trajectories obtained from MD simulation. We computed alpha shape model of all the trajectory frames for each mutation type. Solid angle was used to characterize the curvature of the atoms at the drug binding site. We measured the knob level of the drug binding pocket of each mutant from two ways and analyzed its relationship with the drug response level. Results show that 90 % of the mutants can be grouped correctly by setting a certain knob level threshold. There is a strong correlation between the geometric properties of the drug binding pocket of the EGFR mutants and the corresponding drug responses, which can be used to predict the response of a new EGFR mutant to a drug molecule.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 40%
Student > Master 1 20%
Unknown 2 40%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 20%
Computer Science 1 20%
Engineering 1 20%
Unknown 2 40%

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 08 September 2016.
All research outputs
#6,335,798
of 8,348,788 outputs
Outputs from Proteome Science
#65
of 110 outputs
Outputs of similar age
#178,486
of 252,659 outputs
Outputs of similar age from Proteome Science
#1
of 3 outputs
Altmetric has tracked 8,348,788 research outputs across all sources so far. This one is in the 13th percentile – i.e., 13% of other outputs scored the same or lower than it.
So far Altmetric has tracked 110 research outputs from this source. They receive a mean Attention Score of 2.3. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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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. This one has scored higher than all of them