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A machine learning strategy for predicting localization of post-translational modification sites in protein-protein interacting regions

Overview of attention for article published in BMC Bioinformatics, August 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

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Title
A machine learning strategy for predicting localization of post-translational modification sites in protein-protein interacting regions
Published in
BMC Bioinformatics, August 2016
DOI 10.1186/s12859-016-1165-8
Pubmed ID
Authors

Thammakorn Saethang, D. Michael Payne, Yingyos Avihingsanon, Trairak Pisitkun

Abstract

One very important functional domain of proteins is the protein-protein interacting region (PPIR), which forms the binding interface between interacting polypeptide chains. Post-translational modifications (PTMs) that occur in the PPIR can either interfere with or facilitate the interaction between proteins. The ability to predict whether sites of protein modifications are inside or outside of PPIRs would be useful in further elucidating the regulatory mechanisms by which modifications of specific proteins regulate their cellular functions. Using two of the comprehensive databases for protein-protein interaction and protein modification site data (PDB and PhosphoSitePlus, respectively), we created new databases that map PTMs to their locations inside or outside of PPIRs. The mapped PTMs represented only 5 % of all known PTMs. Thus, in order to predict localization within or outside of PPIRs for the vast majority of PTMs, a machine learning strategy was used to generate predictive models from these mapped databases. For the three mapped PTM databases which had sufficient numbers of modification sites for generating models (acetylation, phosphorylation, and ubiquitylation), the resulting models yielded high overall predictive performance as judged by a combined performance score (CPS). Among the multiple properties of amino acids that were used in the classification tasks, hydrophobicity was found to contribute substantially to the performance of the final predictive models. Compared to the other classifiers we also evaluated, the SVM provided the best performance overall. These models are the first to predict whether PTMs are located inside or outside of PPIRs, as demonstrated by their high predictive performance. The models and data presented here should be useful in prioritizing both known and newly identified PTMs for further studies to determine the functional relationship between specific PTMs and protein-protein interactions. The implemented R package is available online ( http://sysbio.chula.ac.th/PtmPPIR ).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 2%
Unknown 42 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 28%
Student > Master 9 21%
Student > Bachelor 6 14%
Researcher 4 9%
Professor > Associate Professor 3 7%
Other 4 9%
Unknown 5 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 28%
Computer Science 7 16%
Chemistry 5 12%
Agricultural and Biological Sciences 4 9%
Engineering 3 7%
Other 7 16%
Unknown 5 12%
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 20 August 2016.
All research outputs
#4,459,989
of 22,882,389 outputs
Outputs from BMC Bioinformatics
#1,676
of 7,298 outputs
Outputs of similar age
#77,063
of 342,741 outputs
Outputs of similar age from BMC Bioinformatics
#30
of 115 outputs
Altmetric has tracked 22,882,389 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,298 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 77% 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 342,741 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 77% of its contemporaries.
We're also able to compare this research output to 115 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 73% of its contemporaries.