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Characterization of yeast pyruvate kinase 1 as a protein kinase A substrate, and specificity of the phosphorylation site sequence in the whole protein.

ABSTRACT: Pyk1 (pyruvate kinase 1) from Saccharomyces cerevisiae was characterized as a substrate for PKA (protein kinase A) from bovine heart and yeast. By designing Pyk1 synthetic peptides containing potential PKA sequence targets (Ser22, Thr94 and Thr478) we determined that the peptide S22 was a substrate for PKA in vitro, with a K(sp)* (specificity constant) 10-fold and 3-fold higher than Kemptide for bovine heart and yeast PKA respectively. In vitro phosphorylation of the Pyk1 S22A mutant protein was decreased by as much as 90% when compared with wild-type Pyk1 and the Pyk1 T94A mutant. The K(sp)* values for Pyk1 and Pyk1 T94A were the same, indicating that both proteins are phosphorylated at the same site by PKA. Two-dimensional PAGE of Pyk1 and Pyk1 S22A indicates that in vivo the S22A mutation prevented the formation of one of the Pyk1 isoforms. We conclude that in yeast the major PKA phosphorylation site of Pyk1 is Ser22. Phosphorylation of Ser22 leads to a Pyk1 enzyme that is more active in the absence of FBP (fructose 1,6-bisphosphate). The specificity of yeast and mammalian PKA towards the S22 peptide and towards whole Pyk1 protein was measured and compared. The K(sp)* for the S22 peptide is higher than that for Pyk1, indicating that the peptide modelled on Pyk1 is a much better substrate than Pyk1, regardless of which tissue was used as the source of PKA. However, the K(m) of Pyk1 protein is lower than that of the better substrate, the S22 peptide, indicating that ground-state substrate binding is not the major determinant of substrate specificity for PKA.

SUBMITTER: Portela P

PROVIDER: S-EPMC1450003 | biostudies-other | 2006 May

REPOSITORIES: biostudies-other

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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.

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.

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Characterization of yeast pyruvate kinase 1 as a protein kinase A substrate, and specificity of the phosphorylation site sequence in the whole protein.

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