↓ Skip to main content

Prediction of virus-host protein-protein interactions mediated by short linear motifs

Overview of attention for article published in BMC Bioinformatics, March 2017
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
35 Dimensions

Readers on

mendeley
66 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.
Title
Prediction of virus-host protein-protein interactions mediated by short linear motifs
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1570-7
Pubmed ID
Authors

Andrés Becerra, Victor A. Bucheli, Pedro A. Moreno

Abstract

Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1). We implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them - with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID). The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions. With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions. We have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 66 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 20%
Student > Bachelor 8 12%
Student > Master 7 11%
Researcher 7 11%
Student > Doctoral Student 3 5%
Other 12 18%
Unknown 16 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 32%
Agricultural and Biological Sciences 11 17%
Computer Science 5 8%
Immunology and Microbiology 4 6%
Unspecified 2 3%
Other 6 9%
Unknown 17 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 14 October 2017.
All research outputs
#7,136,470
of 11,927,787 outputs
Outputs from BMC Bioinformatics
#2,832
of 4,350 outputs
Outputs of similar age
#138,661
of 265,308 outputs
Outputs of similar age from BMC Bioinformatics
#60
of 100 outputs
Altmetric has tracked 11,927,787 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,350 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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 265,308 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.