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

• 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 Lanial, 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.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 Lanial, 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.…