Dataset Information

Isoform-specific subcellular localization among 14-3-3 proteins in Arabidopsis seems to be driven by client interactions.

ABSTRACT: In most higher eukaryotes, the predominantly phosphoprotein-binding 14-3-3 proteins are the products of a multigene family, with many organisms having 10 or more family members. However, current models for 14-3-3/phosphopeptide interactions suggest that there is little specificity among 14-3-3s for diverse phosphopeptide clients. Therefore, the existence of sequence diversity among 14-3-3s within a single organism begs questions regarding the in vivo specificities of the interactions between the various 14-3-3s and their clients. Chief among those questions is, Do the different 14-3-3 isoforms interact with different clients within the same cell? Although the members of the Arabidopsis 14-3-3 family of proteins typically contain highly conserved regions of sequence, they also display distinctive variability with deep evolutionary roots. In the current study, a survey of several Arabidopsis 14-3-3/GFP fusions revealed that 14-3-3s demonstrate distinct and differential patterns of subcellular distribution, by using trichomes and stomate guard cells as in vivo experimental cellular contexts. The effects of client interaction on 14-3-3 localization were further analyzed by disrupting the partnering with peptide and chemical agents. Results indicate that 14-3-3 localization is both isoform specific and highly dependent upon interaction with cellular clients.

SUBMITTER: Paul AL

PROVIDER: S-EPMC1073656 | biostudies-literature | 2005 Apr

REPOSITORIES: biostudies-literature

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Isoform-specific subcellular localization among 14-3-3 proteins in Arabidopsis seems to be driven by client interactions.

Paul Anna-Lisa AL Sehnke Paul C PC Ferl Robert J RJ

Molecular biology of the cell 20050119 4

In most higher eukaryotes, the predominantly phosphoprotein-binding 14-3-3 proteins are the products of a multigene family, with many organisms having 10 or more family members. However, current models for 14-3-3/phosphopeptide interactions suggest that there is little specificity among 14-3-3s for diverse phosphopeptide clients. Therefore, the existence of sequence diversity among 14-3-3s within a single organism begs questions regarding the in vivo specificities of the interactions between the ...[more]

PMID: 15659648

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Project description:Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mitochondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins.

Project description:BackgroundThe computational prediction of mycobacterial proteins' subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthew's correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40% identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins.ResultsA final validation set of 272 mycobacterial proteins was obtained from the initial set of 852 mycobacterial proteins. According to the results of the validation metrics, all tools presented specificity above 0.90, while dispersion sensitivity and MCC values were above 0.22. PA-SUB 2.5 presented the highest values; however, these results might be biased due to the methodology used by this tool. PSORTb v.2.0.4 left 56 proteins out of the classification, while Gpos-PLoc left just one protein out.ConclusionBoth subcellular localization approaches had high predictive specificity and high recognition of true negatives for the tested data set. Among those tools whose predictions are not based on homology searches against SWISS-PROT, Gpos-PLoc was the general localization tool with the best predictive performance, while SignalP 2.0 was the best tool among the ones using a feature-based approach. Even though PA-SUB 2.5 presented the highest metrics, it should be taken into account that this tool was trained using all proteins reported in SWISS-PROT, which includes the protein set tested in this study, either as a BLAST search or as a training model.

Dataset Information

Isoform-specific subcellular localization among 14-3-3 proteins in Arabidopsis seems to be driven by client interactions.

Publications

Isoform-specific subcellular localization among 14-3-3 proteins in Arabidopsis seems to be driven by client interactions.

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