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Proteome Sampling by the HLA Class I Antigen Processing Pathway

Overview of attention for article published in PLoS Computational Biology, May 2012
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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 (81st percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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2 patents
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1 research highlight platform

Citations

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28 Dimensions

Readers on

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88 Mendeley
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1 CiteULike
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Title
Proteome Sampling by the HLA Class I Antigen Processing Pathway
Published in
PLoS Computational Biology, May 2012
DOI 10.1371/journal.pcbi.1002517
Pubmed ID
Authors

Ilka Hoof, Debbie van Baarle, William H. Hildebrand, Can Keşmir

Abstract

The peptide repertoire that is presented by the set of HLA class I molecules of an individual is formed by the different players of the antigen processing pathway and the stringent binding environment of the HLA class I molecules. Peptide elution studies have shown that only a subset of the human proteome is sampled by the antigen processing machinery and represented on the cell surface. In our study, we quantified the role of each factor relevant in shaping the HLA class I peptide repertoire by combining peptide elution data, in silico predictions of antigen processing and presentation, and data on gene expression and protein abundance. Our results indicate that gene expression level, protein abundance, and rate of potential binding peptides per protein have a clear impact on sampling probability. Furthermore, once a protein is available for the antigen processing machinery in sufficient amounts, C-terminal processing efficiency and binding affinity to the HLA class I molecule determine the identity of the presented peptides. Having studied the impact of each of these factors separately, we subsequently combined all factors in a logistic regression model in order to quantify their relative impact. This model demonstrated the superiority of protein abundance over gene expression level in predicting sampling probability. Being able to discriminate between sampled and non-sampled proteins to a significant degree, our approach can potentially be used to predict the sampling probability of self proteins and of pathogen-derived proteins, which is of importance for the identification of autoimmune antigens and vaccination targets.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Argentina 2 2%
India 1 1%
Unknown 85 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 30%
Student > Ph. D. Student 22 25%
Professor > Associate Professor 7 8%
Other 6 7%
Student > Master 5 6%
Other 13 15%
Unknown 9 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 39%
Biochemistry, Genetics and Molecular Biology 14 16%
Medicine and Dentistry 10 11%
Immunology and Microbiology 9 10%
Computer Science 5 6%
Other 8 9%
Unknown 8 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 07 November 2023.
All research outputs
#4,947,179
of 25,806,080 outputs
Outputs from PLoS Computational Biology
#3,899
of 9,043 outputs
Outputs of similar age
#31,741
of 177,542 outputs
Outputs of similar age from PLoS Computational Biology
#36
of 108 outputs
Altmetric has tracked 25,806,080 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,043 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 gotten more attention than average, scoring higher than 56% 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 177,542 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 81% of its contemporaries.
We're also able to compare this research output to 108 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 66% of its contemporaries.