Cargando…
Predicting Kinase Activity in Angiotensin Receptor Phosphoproteomes Based on Sequence-Motifs and Interactions
Recent progress in the understanding of seven-transmembrane receptor (7TMR) signalling has promoted the development of a new generation of pathway selective ligands. The angiotensin II type I receptor (AT(1a)R) is one of the most studied 7TMRs with respect to selective activation of the β-arrestin d...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3983226/ https://www.ncbi.nlm.nih.gov/pubmed/24722691 http://dx.doi.org/10.1371/journal.pone.0094672 |
_version_ | 1782311285607628800 |
---|---|
author | Bøgebo, Rikke Horn, Heiko Olsen, Jesper V. Gammeltoft, Steen Jensen, Lars J. Hansen, Jakob L. Christensen, Gitte L. |
author_facet | Bøgebo, Rikke Horn, Heiko Olsen, Jesper V. Gammeltoft, Steen Jensen, Lars J. Hansen, Jakob L. Christensen, Gitte L. |
author_sort | Bøgebo, Rikke |
collection | PubMed |
description | Recent progress in the understanding of seven-transmembrane receptor (7TMR) signalling has promoted the development of a new generation of pathway selective ligands. The angiotensin II type I receptor (AT(1a)R) is one of the most studied 7TMRs with respect to selective activation of the β-arrestin dependent signalling. Two complimentary global phosphoproteomics studies have analyzed the complex signalling induced by the AT(1a)R. Here we integrate the data sets from these studies and perform a joint analysis using a novel method for prediction of differential kinase activity from phosphoproteomics data. The method builds upon NetworKIN, which applies sophisticated linear motif analysis in combination with contextual network modelling to predict kinase-substrate associations with high accuracy and sensitivity. These predictions form the basis for subsequently nonparametric statistical analysis to identify likely activated kinases. This suggested that AT(1a)R-dependent signalling activates 48 of the 285 kinases detected in HEK293 cells. Of these, Aurora B, CLK3 and PKG1 have not previously been described in the pathway whereas others, such as PKA, PKB and PKC, are well known. In summary, we have developed a new method for kinase-centric analysis of phosphoproteomes to pinpoint differential kinase activity in large-scale data sets. |
format | Online Article Text |
id | pubmed-3983226 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-39832262014-04-15 Predicting Kinase Activity in Angiotensin Receptor Phosphoproteomes Based on Sequence-Motifs and Interactions Bøgebo, Rikke Horn, Heiko Olsen, Jesper V. Gammeltoft, Steen Jensen, Lars J. Hansen, Jakob L. Christensen, Gitte L. PLoS One Research Article Recent progress in the understanding of seven-transmembrane receptor (7TMR) signalling has promoted the development of a new generation of pathway selective ligands. The angiotensin II type I receptor (AT(1a)R) is one of the most studied 7TMRs with respect to selective activation of the β-arrestin dependent signalling. Two complimentary global phosphoproteomics studies have analyzed the complex signalling induced by the AT(1a)R. Here we integrate the data sets from these studies and perform a joint analysis using a novel method for prediction of differential kinase activity from phosphoproteomics data. The method builds upon NetworKIN, which applies sophisticated linear motif analysis in combination with contextual network modelling to predict kinase-substrate associations with high accuracy and sensitivity. These predictions form the basis for subsequently nonparametric statistical analysis to identify likely activated kinases. This suggested that AT(1a)R-dependent signalling activates 48 of the 285 kinases detected in HEK293 cells. Of these, Aurora B, CLK3 and PKG1 have not previously been described in the pathway whereas others, such as PKA, PKB and PKC, are well known. In summary, we have developed a new method for kinase-centric analysis of phosphoproteomes to pinpoint differential kinase activity in large-scale data sets. Public Library of Science 2014-04-10 /pmc/articles/PMC3983226/ /pubmed/24722691 http://dx.doi.org/10.1371/journal.pone.0094672 Text en © 2014 Bøgebo et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Bøgebo, Rikke Horn, Heiko Olsen, Jesper V. Gammeltoft, Steen Jensen, Lars J. Hansen, Jakob L. Christensen, Gitte L. Predicting Kinase Activity in Angiotensin Receptor Phosphoproteomes Based on Sequence-Motifs and Interactions |
title | Predicting Kinase Activity in Angiotensin Receptor Phosphoproteomes Based on Sequence-Motifs and Interactions |
title_full | Predicting Kinase Activity in Angiotensin Receptor Phosphoproteomes Based on Sequence-Motifs and Interactions |
title_fullStr | Predicting Kinase Activity in Angiotensin Receptor Phosphoproteomes Based on Sequence-Motifs and Interactions |
title_full_unstemmed | Predicting Kinase Activity in Angiotensin Receptor Phosphoproteomes Based on Sequence-Motifs and Interactions |
title_short | Predicting Kinase Activity in Angiotensin Receptor Phosphoproteomes Based on Sequence-Motifs and Interactions |
title_sort | predicting kinase activity in angiotensin receptor phosphoproteomes based on sequence-motifs and interactions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3983226/ https://www.ncbi.nlm.nih.gov/pubmed/24722691 http://dx.doi.org/10.1371/journal.pone.0094672 |
work_keys_str_mv | AT bøgeborikke predictingkinaseactivityinangiotensinreceptorphosphoproteomesbasedonsequencemotifsandinteractions AT hornheiko predictingkinaseactivityinangiotensinreceptorphosphoproteomesbasedonsequencemotifsandinteractions AT olsenjesperv predictingkinaseactivityinangiotensinreceptorphosphoproteomesbasedonsequencemotifsandinteractions AT gammeltoftsteen predictingkinaseactivityinangiotensinreceptorphosphoproteomesbasedonsequencemotifsandinteractions AT jensenlarsj predictingkinaseactivityinangiotensinreceptorphosphoproteomesbasedonsequencemotifsandinteractions AT hansenjakobl predictingkinaseactivityinangiotensinreceptorphosphoproteomesbasedonsequencemotifsandinteractions AT christensengittel predictingkinaseactivityinangiotensinreceptorphosphoproteomesbasedonsequencemotifsandinteractions |