Dataset Information

Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study.

ABSTRACT:

Background

Proteins that interact in vivo tend to reside within the same or "adjacent" subcellular compartments. This observation provides opportunities to reveal protein subcellular localization in the context of the protein-protein interaction (PPI) network. However, so far, only a few efforts based on heuristic rules have been made in this regard.

Results

We systematically and quantitatively validate the hypothesis that proteins physically interacting with each other probably share at least one common subcellular localization. With the result, for the first time, four graph-based semi-supervised learning algorithms, Majority, χ2-score, GenMultiCut and FunFlow originally proposed for protein function prediction, are introduced to assign "multiplex localization" to proteins. We analyze these approaches by performing a large-scale cross validation on a Saccharomyces cerevisiae proteome compiled from BioGRID and comparing their predictions for 22 protein subcellular localizations. Furthermore, we build an ensemble classifier to associate 529 unlabeled and 137 ambiguously-annotated proteins with subcellular localizations, most of which have been verified in the previous experimental studies.

Conclusions

Physical interaction of proteins has actually provided an essential clue for their co-localization. Compared to the local approaches, the global algorithms consistently achieve a superior performance.

SUBMITTER: Jiang JQ

PROVIDER: S-EPMC3314587 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Publications

Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study.

Jiang Jonathan Q JQ Wu Maoying M

BMC bioinformatics 20120625

<h4>Background</h4>Proteins that interact in vivo tend to reside within the same or "adjacent" subcellular compartments. This observation provides opportunities to reveal protein subcellular localization in the context of the protein-protein interaction (PPI) network. However, so far, only a few efforts based on heuristic rules have been made in this regard.<h4>Results</h4>We systematically and quantitatively validate the hypothesis that proteins physically interacting with each other probably s ...[more]

PMID: 22759426

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Project description:Predicting protein subcellular localization is an important and difficult problem, particularly when query proteins may have the multiplex character, i.e., simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular location predictor can only be used to deal with the single-location or "singleplex" proteins. Actually, multiple-location or "multiplex" proteins should not be ignored because they usually posses some unique biological functions worthy of our special notice. By introducing the "multi-labeled learning" and "accumulation-layer scale", a new predictor, called iLoc-Euk, has been developed that can be used to deal with the systems containing both singleplex and multiplex proteins. As a demonstration, the jackknife cross-validation was performed with iLoc-Euk on a benchmark dataset of eukaryotic proteins classified into the following 22 location sites: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centriole, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole, where none of proteins included has ?25% pairwise sequence identity to any other in a same subset. The overall success rate thus obtained by iLoc-Euk was 79%, which is significantly higher than that by any of the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, iLoc-Euk is freely accessible to the public at the web-site http://icpr.jci.edu.cn/bioinfo/iLoc-Euk. It is anticipated that iLoc-Euk may become a useful bioinformatics tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development Also, its novel approach will further stimulate the development of predicting other protein attributes.

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Project description:UnlabelledThe magnetosome is an organelle specialized for inorganic magnetite crystal synthesis in magnetotactic bacteria. The complex mechanism of magnetosome formation is regulated by magnetosome proteins in a stepwise manner. Protein localization is a key step for magnetosome development; however, a global study of magnetosome protein localization remains to be conducted. Here, we comparatively analyzed the subcellular localization of a series of green fluorescent protein (GFP)-tagged magnetosome proteins. The protein localizations were categorized into 5 groups (short-length linear, middle-length linear, long-length linear, cell membrane, and intracellular dispersing), which were related to the protein functions. Mms6, which regulates magnetite crystal growth, localized along magnetosome chain structures under magnetite-forming (microaerobic) conditions but was dispersed in the cell under nonforming (aerobic) conditions. Correlative fluorescence and electron microscopy analyses revealed that Mms6 preferentially localized to magnetosomes enclosing magnetite crystals. We suggest that a highly organized spatial regulation mechanism controls magnetosome protein localization during magnetosome formation in magnetotactic bacteria.ImportanceMagnetotactic bacteria synthesize magnetite (Fe3O4) nanocrystals in a prokaryotic organelle called the magnetosome. This organelle is formed using various magnetosome proteins in multiple steps, including vesicle formation, magnetosome alignment, and magnetite crystal formation, to provide compartmentalized nanospaces for the regulation of iron concentrations and redox conditions, enabling the synthesis of a morphologically controlled magnetite crystal. Thus, to rationalize the complex organelle development, the localization of magnetosome proteins is considered to be highly regulated; however, the mechanisms remain largely unknown. Here, we performed comparative localization analysis of magnetosome proteins that revealed the presence of a spatial regulation mechanism within the linear structure of magnetosomes. This discovery provides evidence of a highly regulated protein localization mechanism for this bacterial organelle development.

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.

Dataset Information

Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study.

Background

Results

Conclusions

Publications

Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study.

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