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Comparative analysis of an experimental subcellular protein localization assay and in silico prediction methods.

ABSTRACT: The subcellular localization of a protein can provide important information about its function within the cell. As eukaryotic cells and particularly mammalian cells are characterized by a high degree of compartmentalization, most protein activities can be assigned to particular cellular compartments. The categorization of proteins by their subcellular localization is therefore one of the essential goals of the functional annotation of the human genome. We previously performed a subcellular localization screen of 52 proteins encoded on human chromosome 21. In the current study, we compared the experimental localization data to the in silico results generated by nine leading software packages with different prediction resolutions. The comparison revealed striking differences between the programs in the accuracy of their subcellular protein localization predictions. Our results strongly suggest that the recently developed predictors utilizing multiple prediction methods tend to provide significantly better performance over purely sequence-based or homology-based predictions.

SUBMITTER: Hu Y

PROVIDER: S-EPMC2834777 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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Comparative analysis of an experimental subcellular protein localization assay and in silico prediction methods.

Hu Yuhui Y Lehrach Hans H Janitz Michal M

Journal of molecular histology 20091001 5-6

The subcellular localization of a protein can provide important information about its function within the cell. As eukaryotic cells and particularly mammalian cells are characterized by a high degree of compartmentalization, most protein activities can be assigned to particular cellular compartments. The categorization of proteins by their subcellular localization is therefore one of the essential goals of the functional annotation of the human genome. We previously performed a subcellular local ...[more]

PMID: 20033263

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Project description:The 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.Magnetotactic 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:BackgroundThe study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. However, determining the localization sites of a protein through wet-lab experiments can be time-consuming and labor-intensive. Thus, computational approaches become highly desirable. Most of the PSL prediction systems are established for single-localized proteins. However, a significant number of eukaryotic proteins are known to be localized into multiple subcellular organelles. Many studies have shown that proteins may simultaneously locate or move between different cellular compartments and be involved in different biological processes with different roles.ResultsIn this study, we propose a knowledge based method, called KnowPredsite, to predict the localization site(s) of both single-localized and multi-localized proteins. Based on the local similarity, we can identify the "related sequences" for prediction. We construct a knowledge base to record the possible sequence variations for protein sequences. When predicting the localization annotation of a query protein, we search against the knowledge base and used a scoring mechanism to determine the predicted sites. We downloaded the dataset from ngLOC, which consisted of ten distinct subcellular organelles from 1923 species, and performed ten-fold cross validation experiments to evaluate KnowPred site's performance. The experiment results show that KnowPred site achieves higher prediction accuracy than ngLOC and Blast-hit method. For single-localized proteins, the overall accuracy of KnowPred site is 91.7%. For multi-localized proteins, the overall accuracy of KnowPred site is 72.1%, which is significantly higher than that of ngLOC by 12.4%. Notably, half of the proteins in the dataset that cannot find any Blast hit sequence above a specified threshold can still be correctly predicted by KnowPred site.ConclusionKnowPred site demonstrates the power of identifying related sequences in the knowledge base. The experiment results show that even though the sequence similarity is low, the local similarity is effective for prediction. Experiment results show that KnowPred site is a highly accurate prediction method for both single- and multi-localized proteins. It is worth-mentioning the prediction process of KnowPred site is transparent and biologically interpretable and it shows a set of template sequences to generate the prediction result. The KnowPred site prediction server is available at http://bio-cluster.iis.sinica.edu.tw/kbloc/.

Dataset Information

Comparative analysis of an experimental subcellular protein localization assay and in silico prediction methods.

Publications

Comparative analysis of an experimental subcellular protein localization assay and in silico prediction methods.

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