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The Structural Pathway of Interleukin 1 (IL-1) Initiated Signaling Reveals Mechanisms of Oncogenic Mutations and SNPs in Inflammation and Cancer

Overview of attention for article published in PLoS Computational Biology, February 2014
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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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
8 X users
peer_reviews
1 peer review site

Citations

dimensions_citation
60 Dimensions

Readers on

mendeley
158 Mendeley
citeulike
1 CiteULike
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Title
The Structural Pathway of Interleukin 1 (IL-1) Initiated Signaling Reveals Mechanisms of Oncogenic Mutations and SNPs in Inflammation and Cancer
Published in
PLoS Computational Biology, February 2014
DOI 10.1371/journal.pcbi.1003470
Pubmed ID
Authors

Saliha Ece Acuner Ozbabacan, Attila Gursoy, Ruth Nussinov, Ozlem Keskin

Abstract

Interleukin-1 (IL-1) is a large cytokine family closely related to innate immunity and inflammation. IL-1 proteins are key players in signaling pathways such as apoptosis, TLR, MAPK, NLR and NF-κB. The IL-1 pathway is also associated with cancer, and chronic inflammation increases the risk of tumor development via oncogenic mutations. Here we illustrate that the structures of interfaces between proteins in this pathway bearing the mutations may reveal how. Proteins are frequently regulated via their interactions, which can turn them ON or OFF. We show that oncogenic mutations are significantly at or adjoining interface regions, and can abolish (or enhance) the protein-protein interaction, making the protein constitutively active (or inactive, if it is a repressor). We combine known structures of protein-protein complexes and those that we have predicted for the IL-1 pathway, and integrate them with literature information. In the reconstructed pathway there are 104 interactions between proteins whose three dimensional structures are experimentally identified; only 15 have experimentally-determined structures of the interacting complexes. By predicting the protein-protein complexes throughout the pathway via the PRISM algorithm, the structural coverage increases from 15% to 71%. In silico mutagenesis and comparison of the predicted binding energies reveal the mechanisms of how oncogenic and single nucleotide polymorphism (SNP) mutations can abrogate the interactions or increase the binding affinity of the mutant to the native partner. Computational mapping of mutations on the interface of the predicted complexes may constitute a powerful strategy to explain the mechanisms of activation/inhibition. It can also help explain how an oncogenic mutation or SNP works.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 158 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 155 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 19%
Student > Master 28 18%
Student > Bachelor 26 16%
Researcher 14 9%
Student > Doctoral Student 9 6%
Other 16 10%
Unknown 35 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 23%
Biochemistry, Genetics and Molecular Biology 32 20%
Medicine and Dentistry 16 10%
Immunology and Microbiology 8 5%
Neuroscience 6 4%
Other 22 14%
Unknown 38 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 17 September 2023.
All research outputs
#1,773,882
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#1,533
of 8,964 outputs
Outputs of similar age
#20,646
of 329,887 outputs
Outputs of similar age from PLoS Computational Biology
#18
of 131 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done well, scoring higher than 82% 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 329,887 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 131 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.