Dataset Information

Two Pdk1 phosphorylation sites on the plant cell death suppressor Adi3 contribute to substrate phosphorylation.

ABSTRACT: The tomato AGC kinase Adi3 is phosphorylated by Pdk1 for activation of its cell death suppression activity. The Pdk1 phosphorylation site for activation of Adi3 is at Ser539. However, there is at least one additional Pdk1 phosphorylation site on Adi3 that has an unknown function. Here we identify an Arabidopsis thaliana sequence homologue of Adi3 termed AGC1-3. Two Pdk1 phosphorylation sites were identified on AGC1-3, activation site Ser596 and Ser269, and by homology Ser212 on Adi3 was identified as a second Pdk1 phosphorylation site. While Ser212 is not required for Adi3 autophosphorylation, Ser212 was shown to be required for full phosphorylation of the Adi3 substrate Gal83.

SUBMITTER: Gray JW

PROVIDER: S-EPMC4301410 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Two Pdk1 phosphorylation sites on the plant cell death suppressor Adi3 contribute to substrate phosphorylation.

Gray Joel W JW Nelson Dittrich Anna C AC Chen Sixue S Avila Julian J Giavalisco Patrick P Devarenne Timothy P TP

Biochimica et biophysica acta 20130316 6

The tomato AGC kinase Adi3 is phosphorylated by Pdk1 for activation of its cell death suppression activity. The Pdk1 phosphorylation site for activation of Adi3 is at Ser539. However, there is at least one additional Pdk1 phosphorylation site on Adi3 that has an unknown function. Here we identify an Arabidopsis thaliana sequence homologue of Adi3 termed AGC1-3. Two Pdk1 phosphorylation sites were identified on AGC1-3, activation site Ser596 and Ser269, and by homology Ser212 on Adi3 was identifi ...[more]

PMID: 23507047

Similar Datasets

Project description:BackgroundProtein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. Due to the difficulty in performing high-throughput mass spectrometry-based experiment, there is a desire to predict phosphorylation sites using computational methods. However, previous studies regarding in silico prediction of plant phosphorylation sites lack the consideration of kinase-specific phosphorylation data. Thus, we are motivated to propose a new method that investigates different substrate specificities in plant phosphorylation sites.ResultsExperimentally verified phosphorylation data were extracted from TAIR9-a protein database containing 3006 phosphorylation data from the plant species Arabidopsis thaliana. In an attempt to investigate the various substrate motifs in plant phosphorylation, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. Profile hidden Markov model (HMM) is then applied to learn a predictive model for each subgroup. Cross-validation evaluation on the MDD-clustered HMMs yields an average accuracy of 82.4% for serine, 78.6% for threonine, and 89.0% for tyrosine models. Moreover, independent test results using Arabidopsis thaliana phosphorylation data from UniProtKB/Swiss-Prot show that the proposed models are able to correctly predict 81.4% phosphoserine, 77.1% phosphothreonine, and 83.7% phosphotyrosine sites. Interestingly, several MDD-clustered subgroups are observed to have similar amino acid conservation with the substrate motifs of well-known kinases from Phospho.ELM-a database containing kinase-specific phosphorylation data from multiple organisms.ConclusionsThis work presents a novel method for identifying plant phosphorylation sites with various substrate motifs. Based on cross-validation and independent testing, results show that the MDD-clustered models outperform models trained without using MDD. The proposed method has been implemented as a web-based plant phosphorylation prediction tool, PlantPhos http://csb.cse.yzu.edu.tw/PlantPhos/. Additionally, two case studies have been demonstrated to further evaluate the effectiveness of PlantPhos.

Project description:Viruses infect humans and progress inside the body leading to various diseases and complications. The phosphorylation of viral proteins catalyzed by host kinases plays crucial regulatory roles in enhancing replication and inhibition of normal host-cell functions. Due to its biological importance, there is a desire to identify the protein phosphorylation sites on human viruses. However, the use of mass spectrometry-based experiments is proven to be expensive and labor-intensive. Furthermore, previous studies which have identified phosphorylation sites in human viruses do not include the investigation of the responsible kinases. Thus, we are motivated to propose a new method to identify protein phosphorylation sites with its kinase substrate specificity on human viruses. The experimentally verified phosphorylation data were extracted from virPTM--a database containing 301 experimentally verified phosphorylation data on 104 human kinase-phosphorylated virus proteins. In an attempt to investigate kinase substrate specificities in viral protein phosphorylation sites, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. The experimental human phosphorylation sites are collected from Phospho.ELM, grouped according to its kinase annotation, and compared with the virus MDD clusters. This investigation identifies human kinases such as CK2, PKB, CDK, and MAPK as potential kinases for catalyzing virus protein substrates as confirmed by published literature. Profile hidden Markov model is then applied to learn a predictive model for each subgroup. A five-fold cross validation evaluation on the MDD-clustered HMMs yields an average accuracy of 84.93% for Serine, and 78.05% for Threonine. Furthermore, an independent testing data collected from UniProtKB and Phospho.ELM is used to make a comparison of predictive performance on three popular kinase-specific phosphorylation site prediction tools. In the independent testing, the high sensitivity and specificity of the proposed method demonstrate the predictive effectiveness of the identified substrate motifs and the importance of investigating potential kinases for viral protein phosphorylation sites.

Dataset Information

Two Pdk1 phosphorylation sites on the plant cell death suppressor Adi3 contribute to substrate phosphorylation.

Publications

Two Pdk1 phosphorylation sites on the plant cell death suppressor Adi3 contribute to substrate phosphorylation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets