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Spinophilin directs protein phosphatase 1 specificity by blocking substrate binding sites.

ABSTRACT: The serine/threonine protein phosphatase 1 (PP1) dephosphorylates hundreds of key biological targets. PP1 associates with >or=200 regulatory proteins to form highly specific holoenzymes. These regulatory proteins target PP1 to its point of action within the cell and prime its enzymatic specificity for particular substrates. However, how they direct PP1's specificity is not understood. Here we show that spinophilin, a neuronal PP1 regulator, is entirely unstructured in its unbound form, and it binds PP1 through a folding-upon-binding mechanism in an elongated fashion, blocking one of PP1's three putative substrate binding sites without altering its active site. This mode of binding is sufficient for spinophilin to restrict PP1's activity toward a model substrate in vitro without affecting its ability to dephosphorylate its neuronal substrate, glutamate receptor 1 (GluR1). Thus, our work provides the molecular basis for the ability of spinophilin to dictate PP1 substrate specificity.

SUBMITTER: Ragusa MJ

PROVIDER: S-EPMC2924587 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Spinophilin directs protein phosphatase 1 specificity by blocking substrate binding sites.

Ragusa Michael J MJ Dancheck Barbara B Critton David A DA Nairn Angus C AC Page Rebecca R Peti Wolfgang W

Nature structural & molecular biology 20100321 4

The serine/threonine protein phosphatase 1 (PP1) dephosphorylates hundreds of key biological targets. PP1 associates with >or=200 regulatory proteins to form highly specific holoenzymes. These regulatory proteins target PP1 to its point of action within the cell and prime its enzymatic specificity for particular substrates. However, how they direct PP1's specificity is not understood. Here we show that spinophilin, a neuronal PP1 regulator, is entirely unstructured in its unbound form, and it bi ...[more]

PMID: 20305656

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Project description:BACKGROUND: Protein O-GlcNAcylation, involving the attachment of single N-acetylglucosamine (GlcNAc) to the hydroxyl group of serine or threonine residues. Elucidation of O-GlcNAcylation sites on proteins is required in order to decipher its crucial roles in regulating cellular processes and aid in drug design. With an increasing number of O-GlcNAcylation sites identified by mass spectrometry (MS)-based proteomics, several methods have been proposed for the computational identification of O-GlcNAcylation sites. However, no development that focuses on the investigation of O-GlcNAcylated substrate motifs has existed. Thus, we were motivated to design a new method for the identification of protein O-GlcNAcylation sites with the consideration of substrate site specificity. RESULTS: In this study, 375 experimentally verified O-GlcNAcylation sites were collected from dbOGAP, which is an integrated resource for protein O-GlcNAcylation. Due to the difficulty in characterizing the substrate motifs by conventional sequence logo analysis, a recursively statistical method has been applied to obtain significant conserved motifs. To construct the predictive models learned from the identified substrate motifs, we adopted Support Vector Machines (SVMs). A five-fold cross validation was used to evaluate the predictive model, achieving sensitivity, specificity, and accuracy of 0.76, 0.80, and 0.78, respectively. Additionally, an independent testing set, which was really blind to the training data of predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (0.94) and outperform three other O-GlcNAcylation site prediction tools. CONCLUSION: This work proposed a computational method to identify informative substrate motifs for O-GlcNAcylation sites. The evaluation of cross validation and independent testing indicated that the identified motifs were effective in the identification of O-GlcNAcylation sites. A case study demonstrated that the proposed method could be a feasible means of conducting preliminary analyses of protein O-GlcNAcylation. We also anticipated that the revealed substrate motif may facilitate the study of extensive crosstalk between O-GlcNAcylation and phosphorylation. This method may help unravel their mechanisms and roles in signaling, transcription, chronic disease, and cancer.

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.

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Spinophilin directs protein phosphatase 1 specificity by blocking substrate binding sites.

Publications

Spinophilin directs protein phosphatase 1 specificity by blocking substrate binding sites.

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