Dataset Information

SLocX: Predicting Subcellular Localization of Arabidopsis Proteins Leveraging Gene Expression Data.

ABSTRACT: 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.

SUBMITTER: Ryngajllo M

PROVIDER: S-EPMC3355584 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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SLocX: Predicting Subcellular Localization of Arabidopsis Proteins Leveraging Gene Expression Data.

Ryngajllo Malgorzata M Childs Liam L Lohse Marc M Giorgi Federico M FM Lude Anja A Selbig Joachim J Usadel Björn B

Frontiers in plant science 20110912

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 predi ...[more]

PMID: 22639594

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Project description:BackgroundThe expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features.ResultsHere, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM). In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets; 1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after five-fold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively; best accuracies reported till date on the same datasets.ConclusionThese results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search based results. The presently developed modules are implemented as web server "ESLpred2" available at http://www.imtech.res.in/raghava/eslpred2/.

Project description:Barley stripe mosaic virus (BSMV) spreads from cell to cell through the coordinated actions of three triple gene block (TGB) proteins (TGB1, TGB2, and TGB3) arranged in overlapping open reading frames (ORFs). Our previous studies (D. M. Lawrence and A. O. Jackson, J. Virol. 75:8712-8723, 2001; D. M. Lawrence and A. O. Jackson, Mol. Plant Pathol. 2:65-75, 2001) have shown that each of these proteins is required for cell-to-cell movement in monocot and dicot hosts. We recently found (H.-S. Lim, J. N. Bragg, U. Ganesan, D. M. Lawrence, J. Yu, M. Isogai, J. Hammond, and A. O. Jackson, J. Virol. 82:4991-5006, 2008) that TGB1 engages in homologous interactions leading to the formation of a ribonucleoprotein complex containing viral genomic and messenger RNAs, and we have also demonstrated that TGB3 functions in heterologous interactions with TGB1 and TGB2. We have now used Agrobacterium tumefaciens-mediated protein expression in Nicotiana benthamiana leaf cells and site-specific mutagenesis to determine how TGB protein interactions influence their subcellular localization and virus spread. Confocal microscopy revealed that the TGB3 protein localizes at the cell wall (CW) in close association with plasmodesmata and that the deletion or mutagenesis of a single amino acid at the immediate C terminus can affect CW targeting. TGB3 also directed the localization of TGB2 from the endoplasmic reticulum to the CW, and this targeting was shown to be dependent on interactions between the TGB2 and TGB3 proteins. The optimal localization of the TGB1 protein at the CW also required TGB2 and TGB3 interactions, but in this context, site-specific TGB1 helicase motif mutants varied in their localization patterns. The results suggest that the ability of TGB1 to engage in homologous binding interactions is not essential for targeting to the CW. However, the relative expression levels of TGB2 and TGB3 influenced the cytosolic and CW distributions of TGB1 and TGB2. Moreover, in both cases, localization at the CW was optimal at the 10:1 TGB2-to-TGB3 ratios occurring in virus infections, and mutations reducing CW localization had corresponding effects on BSMV movement phenotypes. These data support a model whereby TGB protein interactions function in the subcellular targeting of movement protein complexes and the ability of BSMV to move from cell to cell.

Dataset Information

SLocX: Predicting Subcellular Localization of Arabidopsis Proteins Leveraging Gene Expression Data.

Publications

SLocX: Predicting Subcellular Localization of Arabidopsis Proteins Leveraging Gene Expression Data.

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