PSL-LCCL: a resource for subcellular protein localization in liver cancer cell line SK_HEP1
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Abstract The characterization of subcellular protein localization provides a basis for further understanding cellular behaviors. A delineation of subcellular localization of proteins on cytosolic membrane-bound organelles in human liver cancer cell lines (hLCCLs) has yet to be performed. To obtain its proteome-wide view, we isolated and enriched six cytosolic membrane-bound organelles in one of the hLCCLs (SK_HEP1) and quantified their proteins using mass spectrometry. The vigorous selection of marker proteins and a machine-learning-based algorithm were implemented to localize proteins at cluster and neighborhood levels. We validated the performance of the proposed method by comparing the predicted subcellular protein localization with publicly available resources. The profiles enabled investigating the correlation of protein domains with their subcellular localization and colocalization of protein complex members. A subcellular proteome database for SK_HEP1, including (i) the subcellular protein localization and (ii) the subcellular locations of protein complex members and their interactions, was constructed. Our research provides resources for further research on hLCCLs proteomics. Database URL: http://www.igenetics.org.cn/project/PSL-LCCL/Keywords:
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Mapping the proteome Proteins function in the context of their environment, so an understanding of cellular processes requires a knowledge of protein localization. Thul et al. used immunofluorescence microscopy to map 12,003 human proteins at a single-cell level into 30 cellular compartments and substructures (see the Perspective by Horwitz and Johnson). They validated their results by mass spectroscopy and used them to model and refine protein-protein interaction networks. The cellular proteome is highly spatiotemporally regulated. Many proteins localize to multiple compartments, and many show cell-to-cell variation in their expression patterns. Presented as an interactive database called the Cell Atlas, this work provides an important resource for ongoing efforts to understand human biology. Science , this issue p. eaal3321 ; see also p. 806
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Cell is the fundamental unit of living organism,proteins with different functions distributed in cellular compartments,including plasmolemma,nucleus,cytoplasm and organelles like mitochondria,chloroplast,Golgi,and endoplasmic reticulum etc.Protein subcellular localization is one of the key questions for functional genomics.The techniques used for subcellular localization of plants protein include fusion reporter gene localization,immunohistochemical localization,2D combined with mass spectrometry,marker enzyme-assisted localization and bioinformatics prediction.The development and application of high-throughtput protein subcellular localization technology stimulated the establishment of protein subcellular localization database.In model plant Arabidopsis thaliana,the number of proteins with localization data is over 4000.
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Functional annotation of unknown proteins is a major goal in proteomics. A key step in this annotation process is the definition of a protein’s subcellular localization. As a consequence, numerous prediction techniques for localization have been developed over the years. These methods typically focus on a single underlying biological aspect or predict a subset of all possible subcellular localizations. There is a clear need for new methods that utilize and represent available protein specific biological knowledge from several sources, in order to improve accuracy and localization coverage for a wide range of organisms. Here we present a novel Support Vector Machine (SVM)-based approach for predicting protein subcellular localization, which integrates information about N-terminal targeting sequences, amino acid composition, and protein sequence motifs. An important step is taken towards emulating the protein sorting process by capturing and bringing together biologically relevant information. Our novel approach has been used to develop two new prediction methods, TargetLoc and MultiLoc. TargetLoc is restricted to analysis of proteins containing N-terminal targeting sequences, whereas MultiLoc covers all major eukaryotic subcellular localizations for animal, plant, and fungal proteins. Compared to similar methods, TargetLoc performs better than these. MultiLoc performs considerably better than comparable prediction methods predicting all major eukaryotic subcellular localizations, and shows better or comparable results to methods that are specialized on fewer localizations or for one organism.
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Subcellular localization is crucial to the study of virus and diseases. Specifically, research on protein subcellular localization can help identify clues between virus and host cells that can aid in the design of targeted drugs. Research on RNA subcellular localization is significant for human diseases (such as Alzheimer's disease, colon cancer, etc.). To date, only reviews addressing subcellular localization of proteins have been published, which are outdated for reference, and reviews of RNA subcellular localization are not comprehensive. Therefore, we collated (the most up-to-date) literature on protein and RNA subcellular localization to help researchers understand changes in the field of protein and RNA subcellular localization. Extensive and complete methods for constructing subcellular localization models have also been summarized, which can help readers understand the changes in application of biotechnology and computer science in subcellular localization research and explore how to use biological data to construct improved subcellular localization models. This paper is the first review to cover both protein subcellular localization and RNA subcellular localization. We urge researchers from biology and computational biology to jointly pay attention to transformation patterns, interrelationships, differences, and causality of protein subcellular localization and RNA subcellular localization. Subcellular localization is crucial to the study of virus and diseases. Specifically, research on protein subcellular localization can help identify clues between virus and host cells that can aid in the design of targeted drugs. Research on RNA subcellular localization is significant for human diseases (such as Alzheimer's disease, colon cancer, etc.). To date, only reviews addressing subcellular localization of proteins have been published, which are outdated for reference, and reviews of RNA subcellular localization are not comprehensive. Therefore, we collated (the most up-to-date) literature on protein and RNA subcellular localization to help researchers understand changes in the field of protein and RNA subcellular localization. Extensive and complete methods for constructing subcellular localization models have also been summarized, which can help readers understand the changes in application of biotechnology and computer science in subcellular localization research and explore how to use biological data to construct improved subcellular localization models. This paper is the first review to cover both protein subcellular localization and RNA subcellular localization. We urge researchers from biology and computational biology to jointly pay attention to transformation patterns, interrelationships, differences, and causality of protein subcellular localization and RNA subcellular localization.
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Characterizing the subcellular localization of a protein provides a key clue for understanding protein function. However, different protein localization prediction programs often deliver conflicting results regarding the localization of the same protein. As the number of available localization prediction programs continues to grow, there is a need for a consensus prediction approach. To address this need, we developed a consensus localization prediction method called ConLoc based on a large-scale, systematic integration of 13 available programs that make predictions for five major subcellular localizations (cytosol, extracellular, mitochondria, nucleus, and plasma membrane). The ability of ConLoc to accurately predict protein localization was substantially better than existing programs. Using ConLoc prediction, we built a localization-guided functional interaction network of the human proteome and mapped known disease associations within this network. We found a high degree of shared disease associations among functionally interacting proteins that are localized to the same cellular compartment. Thus, the use of consensus localization prediction, such as ConLoc, is a new approach for the identification of novel disease associated genes.
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Abstract The characterization of subcellular protein localization provides a basis for further understanding cellular behaviors. A delineation of subcellular localization of proteins on cytosolic membrane-bound organelles in human liver cancer cell lines (hLCCLs) has yet to be performed. To obtain its proteome-wide view, we isolated and enriched six cytosolic membrane-bound organelles in one of the hLCCLs (SK_HEP1) and quantified their proteins using mass spectrometry. The vigorous selection of marker proteins and a machine-learning-based algorithm were implemented to localize proteins at cluster and neighborhood levels. We validated the performance of the proposed method by comparing the predicted subcellular protein localization with publicly available resources. The profiles enabled investigating the correlation of protein domains with their subcellular localization and colocalization of protein complex members. A subcellular proteome database for SK_HEP1, including (i) the subcellular protein localization and (ii) the subcellular locations of protein complex members and their interactions, was constructed. Our research provides resources for further research on hLCCLs proteomics. Database URL: http://www.igenetics.org.cn/project/PSL-LCCL/
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LOCATE is a curated, web-accessible database that houses data describing the membrane organization and subcellular localization of mouse and human proteins. Over the past 2 years, the data in LOCATE have grown substantially. The database now contains high-quality localization data for 20% of the mouse proteome and general localization annotation for nearly 36% of the mouse proteome. The proteome annotated in LOCATE is from the RIKEN FANTOM Consortium Isoform Protein Sequence sets which contains 58 128 mouse and 64 637 human protein isoforms. Other additions include computational subcellular localization predictions, automated computational classification of experimental localization image data, prediction of protein sorting signals and third party submission of literature data. Collectively, this database provides localization proteome for individual subcellular compartments that will underpin future systematic investigations of these regions. It is available at http://locate.imb.uq.edu.au/
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