Evidence map›Paper›PMID 22973286›Full record

ArticleFrontiers in plant science2012

Characterization and Prediction of Protein Phosphorylation Hotspots in Arabidopsis thaliana.

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

Abstract read
In one paragraph

Article in Frontiers in plant science, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Mapping the proteome landscape inPlant signaling & behavior · 2026
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Jan-Ole ChristianMax Planck Institute for Molecular Plant Physiology Potsdam-Golm, Brandenburg, Germany.
Rostyslav Braginets
Waltraud X Schulze
Dirk Walther

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Arabidopsis thalianahotspotsprotein phosphorylationregulationsupport vector machines

Identifiers

PMID22973286
PMCPMC3433687

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.