Dataset Information

Predicting sub-Golgi localization of type II membrane proteins.

ABSTRACT: MOTIVATION:Recent research underlines the importance of finegrained knowledge on protein localization. In particular, subcompartmental localization in the Golgi apparatus is important, for example, for the order of reactions performed in glycosylation pathways or the sorting functions of SNAREs, but is currently poorly understood. RESULTS:We assemble a dataset of type II transmembrane proteins with experimentally determined sub-Golgi localizations and use this information to develop a predictor based on the transmembrane domain of these proteins, making use of a dedicated proteinstructure based kernel in an SVM. Various applications demonstrate the power of our approach. In particular, comparison with a large set of glycan structures illustrates the applicability of our predictions on a 'glycomic' scale and demonstrates a significant correlation between sub-Golgi localization and the ordering of different steps in glycan biosynthesis. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: van Dijk AD

PROVIDER: S-EPMC7110242 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature

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Predicting sub-Golgi localization of type II membrane proteins.

van Dijk A D J AD Bosch D D ter Braak C J F CJ van der Krol A R AR van Ham R C H J RC

Bioinformatics (Oxford, England) 20080618 16

<h4>Motivation</h4>Recent research underlines the importance of finegrained knowledge on protein localization. In particular, subcompartmental localization in the Golgi apparatus is important, for example, for the order of reactions performed in glycosylation pathways or the sorting functions of SNAREs, but is currently poorly understood.<h4>Results</h4>We assemble a dataset of type II transmembrane proteins with experimentally determined sub-Golgi localizations and use this information to devel ...[more]

PMID: 18562268

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Project description:The molecular architecture and function of the Gram-negative bacterial cell envelope are dictated by protein composition and localization. Proteins that localize to the inner membranes (IM) and outer membranes (OM) of Gram-negative bacteria play critical and distinct roles in cellular physiology; however, approaches to systematically interrogate their distribution across both membranes and the soluble cell fraction are lacking. Here, we employed multiplexed quantitative mass spectrometry using tandem mass tag (TMT) labeling to assess membrane protein localization in a proteome-wide fashion by separating IM and OM vesicles from exponentially growing Escherichia coli K-12 cells on a sucrose density gradient. The migration patterns for >1,600 proteins were classified in an unbiased manner, accurately recapitulating decades of knowledge in membrane protein localization in E. coli For 559 proteins that are currently annotated as peripherally associated with the IM (G. Orfanoudaki and A. Economou, Mol Cell Proteomics 13:3674-3687, 2014, https://doi.org/10.1074/mcp.O114.041137) and that display potential for dual localization to either the IM or cytoplasm, we could allocate 110 proteins to the IM and 206 proteins to the soluble cell fraction based on their fractionation patterns. In addition, we uncovered 63 cases, in which our data disagreed with current localization annotation in protein databases. For 42 of these cases, we were able to find supportive evidence for our localization findings in the literature. We anticipate that our systems-level analysis of the E. coli membrane proteome will serve as a useful reference data set to query membrane protein localization, as well as to provide a novel methodology to rapidly and systematically map membrane protein localization in more poorly characterized Gram-negative species.IMPORTANCE Current knowledge of protein localization, particularly outer membrane proteins, is highly dependent on bioinformatic predictions. To date, no systematic experimental studies have directly compared protein localization spanning the inner and outer membranes of E. coli By combining sucrose density gradient fractionation of inner membrane (IM) and outer membrane (OM) proteins with multiplex quantitative proteomics, we systematically quantified localization patterns for >1,600 proteins, providing high-confidence localization annotations for 1,368 proteins. Of these proteins, we resolve the predominant localization of 316 proteins that currently have dual annotation (cytoplasmic and IM) in protein databases and identify new annotations for 42 additional proteins. Overall, we present a novel quantitative methodology to systematically map membrane proteins in Gram-negative bacteria and use it to unravel the biological complexity of the membrane proteome architecture in E. coli.

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/.

Dataset Information

Predicting sub-Golgi localization of type II membrane proteins.

Publications

Predicting sub-Golgi localization of type II membrane proteins.

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