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IDPpi: Protein-Protein Interaction Analyses of Human Intrinsically Disordered Proteins

Overview of attention for article published in Scientific Reports, July 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

twitter
8 tweeters

Citations

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8 Dimensions

Readers on

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60 Mendeley
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Title
IDPpi: Protein-Protein Interaction Analyses of Human Intrinsically Disordered Proteins
Published in
Scientific Reports, July 2018
DOI 10.1038/s41598-018-28815-x
Pubmed ID
Authors

Vladimir Perovic, Neven Sumonja, Lindsey A. Marsh, Sandro Radovanovic, Milan Vukicevic, Stefan G. E. Roberts, Nevena Veljkovic

Abstract

Intrinsically disordered proteins (IDPs) are characterized by the lack of a fixed tertiary structure and are involved in the regulation of key biological processes via binding to multiple protein partners. IDPs are malleable, adapting to structurally different partners, and this flexibility stems from features encoded in the primary structure. The assumption that universal sequence information will facilitate coverage of the sparse zones of the human interactome motivated us to explore the possibility of predicting protein-protein interactions (PPIs) that involve IDPs based on sequence characteristics. We developed a method that relies on features of the interacting and non-interacting protein pairs and utilizes machine learning to classify and predict IDP PPIs. Consideration of both sequence determinants specific for conformational organizations and the multiplicity of IDP interactions in the training phase ensured a reliable approach that is superior to current state-of-the-art methods. By applying a strict evaluation procedure, we confirm that our method predicts interactions of the IDP of interest even on the proteome-scale. This service is provided as a web tool to expedite the discovery of new interactions and IDP functions with enhanced efficiency.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters 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 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 23%
Student > Master 10 17%
Student > Bachelor 8 13%
Researcher 7 12%
Student > Doctoral Student 5 8%
Other 7 12%
Unknown 9 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 38%
Agricultural and Biological Sciences 10 17%
Chemistry 4 7%
Computer Science 4 7%
Chemical Engineering 1 2%
Other 5 8%
Unknown 13 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 18 July 2018.
All research outputs
#4,411,505
of 14,571,155 outputs
Outputs from Scientific Reports
#28,113
of 75,790 outputs
Outputs of similar age
#103,538
of 272,978 outputs
Outputs of similar age from Scientific Reports
#15
of 54 outputs
Altmetric has tracked 14,571,155 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 75,790 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.7. This one has gotten more attention than average, scoring higher than 62% 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 272,978 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 54 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 72% of its contemporaries.