Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.

In order to fully understand protein kinase networks, new methods are needed to identify regulators and substrates of kinases, especially for weakly expressed proteins. Here we have developed a hybrid computational search algorithm that combines machine learning and expert knowledge to identify kina...

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Main Authors: Thomas C Whisenant, David T Ho, Ryan W Benz, Jeffrey S Rogers, Robyn M Kaake, Elizabeth A Gordon, Lan Huang, Pierre Baldi, Lee Bardwell
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2010-08-01
Series:PLoS Computational Biology
Online Access:https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000908&type=printable
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author Thomas C Whisenant
David T Ho
Ryan W Benz
Jeffrey S Rogers
Robyn M Kaake
Elizabeth A Gordon
Lan Huang
Pierre Baldi
Lee Bardwell
author_facet Thomas C Whisenant
David T Ho
Ryan W Benz
Jeffrey S Rogers
Robyn M Kaake
Elizabeth A Gordon
Lan Huang
Pierre Baldi
Lee Bardwell
author_sort Thomas C Whisenant
collection DOAJ
description In order to fully understand protein kinase networks, new methods are needed to identify regulators and substrates of kinases, especially for weakly expressed proteins. Here we have developed a hybrid computational search algorithm that combines machine learning and expert knowledge to identify kinase docking sites, and used this algorithm to search the human genome for novel MAP kinase substrates and regulators focused on the JNK family of MAP kinases. Predictions were tested by peptide array followed by rigorous biochemical verification with in vitro binding and kinase assays on wild-type and mutant proteins. Using this procedure, we found new 'D-site' class docking sites in previously known JNK substrates (hnRNP-K, PPM1J/PP2Czeta), as well as new JNK-interacting proteins (MLL4, NEIL1). Finally, we identified new D-site-dependent MAPK substrates, including the hedgehog-regulated transcription factors Gli1 and Gli3, suggesting that a direct connection between MAP kinase and hedgehog signaling may occur at the level of these key regulators. These results demonstrate that a genome-wide search for MAP kinase docking sites can be used to find new docking sites and substrates.
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spelling doaj-art-bd7cc5262aca4cefad1a00f3a405e5962025-08-20T03:07:20ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582010-08-0168e100090810.1371/journal.pcbi.1000908Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.Thomas C WhisenantDavid T HoRyan W BenzJeffrey S RogersRobyn M KaakeElizabeth A GordonLan HuangPierre BaldiLee BardwellIn order to fully understand protein kinase networks, new methods are needed to identify regulators and substrates of kinases, especially for weakly expressed proteins. Here we have developed a hybrid computational search algorithm that combines machine learning and expert knowledge to identify kinase docking sites, and used this algorithm to search the human genome for novel MAP kinase substrates and regulators focused on the JNK family of MAP kinases. Predictions were tested by peptide array followed by rigorous biochemical verification with in vitro binding and kinase assays on wild-type and mutant proteins. Using this procedure, we found new 'D-site' class docking sites in previously known JNK substrates (hnRNP-K, PPM1J/PP2Czeta), as well as new JNK-interacting proteins (MLL4, NEIL1). Finally, we identified new D-site-dependent MAPK substrates, including the hedgehog-regulated transcription factors Gli1 and Gli3, suggesting that a direct connection between MAP kinase and hedgehog signaling may occur at the level of these key regulators. These results demonstrate that a genome-wide search for MAP kinase docking sites can be used to find new docking sites and substrates.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000908&type=printable
spellingShingle Thomas C Whisenant
David T Ho
Ryan W Benz
Jeffrey S Rogers
Robyn M Kaake
Elizabeth A Gordon
Lan Huang
Pierre Baldi
Lee Bardwell
Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.
PLoS Computational Biology
title Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.
title_full Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.
title_fullStr Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.
title_full_unstemmed Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.
title_short Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.
title_sort computational prediction and experimental verification of new map kinase docking sites and substrates including gli transcription factors
url https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000908&type=printable
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