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Characterization and Prediction of Protein Phosphorylation Hotspots in Arabidopsis thaliana

Overview of attention for article published in Frontiers in Plant Science, January 2012
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Title
Characterization and Prediction of Protein Phosphorylation Hotspots in Arabidopsis thaliana
Published in
Frontiers in Plant Science, January 2012
DOI 10.3389/fpls.2012.00207
Pubmed ID
Authors

Jan-Ole Christian, Rostyslav Braginets, Waltraud X. Schulze, Dirk Walther

Abstract

The regulation of protein function by modulating the surface charge status via sequence-locally enriched phosphorylation sites (P-sites) in so called phosphorylation "hotspots" has gained increased attention in recent years. We set out to identify P-hotspots in the model plant Arabidopsis thaliana. We analyzed the spacing of experimentally detected P-sites within peptide-covered regions along Arabidopsis protein sequences as available from the PhosPhAt database. Confirming earlier reports (Schweiger and Linial, 2010), we found that, indeed, P-sites tend to cluster and that distributions between serine and threonine P-sites to their respected closest next P-site differ significantly from those for tyrosine P-sites. The ability to predict P-hotspots by applying available computational P-site prediction programs that focus on identifying single P-sites was observed to be severely compromised by the inevitable interference of nearby P-sites. We devised a new approach, named HotSPotter, for the prediction of phosphorylation hotspots. HotSPotter is based primarily on local amino acid compositional preferences rather than sequence position-specific motifs and uses support vector machines as the underlying classification engine. HotSPotter correctly identified experimentally determined phosphorylation hotspots in A. thaliana with high accuracy. Applied to the Arabidopsis proteome, HotSPotter-predicted 13,677 candidate P-hotspots in 9,599 proteins corresponding to 7,847 unique genes. Hotspot containing proteins are involved predominantly in signaling processes confirming the surmised modulating role of hotspots in signaling and interaction events. Our study provides new bioinformatics means to identify phosphorylation hotspots and lays the basis for further investigating novel candidate P-hotspots. All phosphorylation hotspot annotations and predictions have been made available as part of the PhosPhAt database at http://phosphat.mpimp-golm.mpg.de.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Israel 1 3%
Germany 1 3%
Unknown 33 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 39%
Student > Ph. D. Student 10 28%
Student > Master 4 11%
Student > Postgraduate 2 6%
Professor 2 6%
Other 3 8%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 67%
Biochemistry, Genetics and Molecular Biology 7 19%
Computer Science 2 6%
Business, Management and Accounting 1 3%
Chemistry 1 3%
Other 0 0%
Unknown 1 3%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 05 September 2012.
All research outputs
#20,166,700
of 22,678,224 outputs
Outputs from Frontiers in Plant Science
#15,750
of 19,848 outputs
Outputs of similar age
#221,176
of 244,088 outputs
Outputs of similar age from Frontiers in Plant Science
#109
of 195 outputs
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