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  • Open Access

DBMLoc: a Database of proteins with multiple subcellular localizations

  • †1,
  • †1,
  • 1,
  • 1 and
  • 1Email author
†Contributed equally
BMC Bioinformatics20089:127

https://doi.org/10.1186/1471-2105-9-127

©  Zhang et al; licensee BioMed Central Ltd. 2008

  • Received: 22 July 2007
  • Accepted: 28 February 2008
  • Published:

Abstract

Background

Subcellular localization information is one of the key features to protein function research. Locating to a specific subcellular compartment is essential for a protein to function efficiently. Proteins which have multiple localizations will provide more clues. This kind of proteins may take a high proportion, even more than 35%.

Description

We have developed a database of proteins with multiple subcellular localizations, designated DBMLoc. The initial release contains 10470 multiple subcellular localization-annotated entries. Annotations are collected from primary protein databases, specific subcellular localization databases and literature texts. All the protein entries are cross-referenced to GO annotations and SwissProt. Protein-protein interactions are also annotated. They are classified into 12 large subcellular localization categories based on GO hierarchical architecture and original annotations. Download, search and sequence BLAST tools are also available on the website.

Conclusion

DBMLoc is a protein database which collects proteins with more than one subcellular localization annotation. It is freely accessed at http://www.bioinfo.tsinghua.edu.cn/DBMLoc/index.htm.

Keywords

  • Gene Ontology
  • Subcellular Localization
  • NCBI Taxonomy
  • Protein Entry
  • Plain Text File

Background

Knowledge of subcellular localization is crucial to understanding protein function and biological process. During translation or later, proteins will be transported into different compartments such as cytoplasm, membrane system, mitochondrion, etc., or may be secreted out of the cell. Locating to a specific subcellular compartment is essential for a protein to function efficiently. High-throughput experimental approaches like immuno-localization[1], tagged genes and reported fusions[2, 3] have made the growth of localization data catch up with the avalanche of protein data. Swiss-Prot is a comprehensive database which includes subcellular localization information. In the recent years, some specific subcellular localization databases are constructed based on experimentation, computational prediction or both. The subcellular localization data of LOCATE[4] are from high-throughput immunofluorescence-based assay and publications. Organelle DB[5] annotates all protein localizations using vocabulary from the Gene Ontology consortium which facilitates data interoperability. DBSubLoc[6] uses a keyword-based system to integrate Swiss-Prot subcellular localization annotations. LOCtarget[7] and PA-GOSUB[8] implement predictors of subcellular localization based on different methods have been reported. PSORTdb[9] is a database for bacteria that contains both information determined through laboratory experimentation (ePSORTdb) and computational predictions (cPSORTdb). Eukaryotic database, eSLDB[10], collects five species' location data which are experimental-determined, homology-based or predicted. In addition, some bioinformatics methods have been developed to predict the protein subcellular location, which make use of the sorting signals[11], domain information[12], amino acid composition in the sequences [13–15] or other information[16].

However, a lot of proteins have more than one subcellular localization annotations. These proteins may simultaneously locate or move between different cellular compartments, for example, transcription factors and signaling pathway transduction factors. Proteins may play different roles in biological process when they are in different subcellular localizations. For these proteins, single subcellular localization annotation will lose some important information. Usually these proteins have more important biological functions. Their localization annotations will provide more valuable clues to researchers. These proteins are quite common, accounting for about 39% of all organellar proteins in mouse liver[17]. However, there are very few proteins annotated with multiple locations in the available subcellular localization databases. Here we have built the database DBMLoc which collects proteins with multiple subcellular localization annotations. It provides useful information for protein functional research as well as computational prediction. In addition, taxonomy, Swiss-Prot, GO and interaction information are also annotated. If protein has interactions, a subcellular localization quality score is computed on the basis of its interaction proteins' locations.

Construction and content

The DBMLoc database is mainly developed from primary protein databases (Swiss-Prot/TrEMBL[18]), available experimental-determined subcellular localization databases (DBSubloc[6], ePSORTdb[9], MitoProteome[19], Organelle DB[5] and LOCATE[4]) and some literature references. Only full-length and unambiguous proteins are selected from Swiss-Prot, and those whose subcellular localization annotations are marked with "by similarity", "probable", "possible", "potential", "may be" are excluded. At the same time, multiple annotations are collected from subcellular localization databases (DBSubloc, ePSORTdb, MitoProteome, Organelle DB and LOCATE), then they are mapped to the protein set derived from Swiss-Prot. The redundant annotations are filtered. In order to standardize subcellular localization annotation terms, various terms of cellular compartments and complexes are assigned into twelve large organelle categories as follows: extracellular, cell wall, membrane, cytoplasm, mitochondrion, nucleus, ribosome, plastid, endoplasmic reticulum, Golgi apparatus, vacuole and virion. Cell wall, plastid and vacuole are unique in plant cell. Some subcellular localization annotations which can not be classified into the twelve categories are assigned into "others". There are 616 proteins that have "others" annotations. This process is mainly based on the Gene Ontology[20] annotations and original subcellular localization annotations. We annotate the proteins with GO ID from their primary sources or the annotation tools provided by GOA (Gene Ontology Annotation Database)[21]. The proteins are also cross-referenced to the NCBI Taxonomy database[22]. Sub-datasets are derived based on their taxonomy class (i.e. animal, plant, eukaryote, etc.)

Proteins that interact with each other tend to share the same subcellular localizations, so we annotate the protein with interaction data collected from DIP[23], MINT[24] and BIND[25]. To check the subcellular localization annotation quality, if it has interaction proteins, a quality score is computed based on the following formula. The higher the score is, the more reliable the subcellular localization annotations are. All the proteins whose score equals 1 are integrated into a high quality dataset.
S c o r e = N 1 N 2 MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xI8qiVKYPFjYdHaVhbbf9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaGaem4uamLaem4yamMaem4Ba8MaemOCaiNaemyzauMaeyypa0tcfa4aaSaaaeaacqWGobGtcqaIXaqmaeaacqWGobGtcqaIYaGmaaaaaa@3896@

N1: Number of the localizations shared by its interaction proteins' subcellular localizations.

N2: Number of protein's subcellular localizations.

Finally, with some literature annotated proteins added, 10470 protein entries are integrated into DBMLoc database. The downloadable DBMLoc database and non-redundant sub-datasets are released as plain text files. The format is similar to that of Swiss-Prot data file. Each line in the file is one record of an entry in the 'KEY VALUE' format. The cross-reference records begin with a 'CX' key. Each of the value data contains one cross-reference record in the 'Reference Database: Reference ID' format, for example, the 'CX SWISS-PROT: Q85FL3' record means that the protein entry is linked to SWISS-PROT database Q85FL3 entry. More detailed description of the format can be found on the web page.

Utility and discussion

We provide free download of the database, organism specific sub-datasets and taxonomy-categorized files for all the education and research users. Users can search the database with DBMLoc identity, cross-referenced database identity or protein name. Figures 1 and 2 show the name and identity search results. Protein sequence also can be submitted to search for homologous proteins in the full DBMLoc database or in one of its subsets.
Figure 1
Figure 1

Protein name search result with keyword "actin".

Figure 2
Figure 2

Swiss-Prot identity search result with query "Q9Y5S9".

The initial release contains 10470 multiple subcellular localization-annotated protein entries. Non-redundant protein data sets with sequence similarity less than 90% and 25% are also generated by BLAST. Table 1 lists brief statistical information on full and non-redundant data sets. The detailed statistical information is on the web page.
Table 1

Brief statistics of DBMLoc

 

Full data sets

Non-redundant data sets (90%)

Non-redundant data sets (25%)

Two subcellular localizations

8887

6055

2366

Three subcellular localizations

1461

1112

593

Four subcellular localizations

107

100

85

Eukaryote

9954

6727

2549

Animal

6492

4240

1523

Plant

3462

2487

1278

Various databases' annotations integrated together in DBMLoc database might be false annotations or conflicts. So, we will pay more attention to the quality of data in the future development. More experimental data and other available information, like experimental method and post-translation modification, will be integrated to the database. The database will be updated regularly as new version of Swiss-Prot is available. Besides, more web services and analysis tools will be developed.

Conclusion

DBMLoc is a specific database aimed at multiple localization annotated proteins. Proteins are cross-referenced to NCBI taxonomy, Gene Ontology and original database. Proteins that interact with each other tend to share the same subcellular localizations. So, protein-protein interaction information is also integrated into the database. A quality score is derived from protein-protein interactions. These data will be valuable to help experimental and computational biologists understand and analyze biological function.

Availability and requirements

DBMLoc home page: http://www.bioinfo.tsinghua.edu.cn/DBMLoc/index.htm

License: The database is freely available.

Notes

List of abbreviations

GO: 

Gene Ontology.

Declarations

Acknowledgements

This project was supported in part by the National Natural Science Grant in China 863 (no.2006AA020403), 973(no.2003CB715900) and the National Natural Science Grants (no.30770498).

Authors’ Affiliations

(1)
MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing, 100084, China

References

  1. Huh WK, Falvo JV, Gerke LC, Carroll AS, Howson RW, Weissman JS, O'Shea EK: Global analysis of protein localization in budding yeast. Nature 2003, 425(6959):686–691. 10.1038/nature02026View ArticlePubMedGoogle Scholar
  2. Kumar A, Agarwal S, Heyman JA, Matson S, Heidtman M, Piccirillo S, Umansky L, Drawid A, Jansen R, Liu Y, Cheung KH, Miller P, Gerstein M, Roeder GS, Snyder M: Subcellular localization of the yeast proteome. Genes Dev 2002, 16(6):707–719. 10.1101/gad.970902PubMed CentralView ArticlePubMedGoogle Scholar
  3. Ross-Macdonald P, Coelho PS, Roemer T, Agarwal S, Kumar A, Jansen R, Cheung KH, Sheehan A, Symoniatis D, Umansky L, Heidtman M, Nelson FK, Iwasaki H, Hager K, Gerstein M, Miller P, Roeder GS, Snyder M: Large-scale analysis of the yeast genome by transposon tagging and gene disruption. Nature 1999, 402(6760):413–418. 10.1038/46558View ArticlePubMedGoogle Scholar
  4. Fink JL, Aturaliya RN, Davis MJ, Zhang F, Hanson K, Teasdale MS, Kai C, Kawai J, Carninci P, Hayashizaki Y, Teasdale RD: LOCATE: a mouse protein subcellular localization database. Nucleic Acids Res 2006, 34(Database issue):D213–7. 10.1093/nar/gkj069PubMed CentralView ArticlePubMedGoogle Scholar
  5. Wiwatwattana N, Kumar A: Organelle DB: a cross-species database of protein localization and function. Nucleic Acids Res 2005, 33(Database issue):D598–604. 10.1093/nar/gki071PubMed CentralView ArticlePubMedGoogle Scholar
  6. Guo T, Hua S, Ji X, Sun Z: DBSubLoc: database of protein subcellular localization. Nucleic Acids Res 2004, 32(Database issue):D122–4. 10.1093/nar/gkh109PubMed CentralView ArticlePubMedGoogle Scholar
  7. Nair R, Rost B: LOCnet and LOCtarget: sub-cellular localization for structural genomics targets. Nucleic Acids Res 2004, 32(Web Server issue):W517–21. 10.1093/nar/gkh441PubMed CentralView ArticlePubMedGoogle Scholar
  8. Lu P, Szafron D, Greiner R, Wishart DS, Fyshe A, Pearcy B, Poulin B, Eisner R, Ngo D, Lamb N: PA-GOSUB: a searchable database of model organism protein sequences with their predicted Gene Ontology molecular function and subcellular localization. Nucleic Acids Res 2005, 33(Database issue):D147–53. 10.1093/nar/gki120PubMed CentralView ArticlePubMedGoogle Scholar
  9. Rey S, Acab M, Gardy JL, Laird MR, deFays K, Lambert C, Brinkman FS: PSORTdb: a protein subcellular localization database for bacteria. Nucleic Acids Res 2005, 33(Database issue):D164–8. 10.1093/nar/gki027PubMed CentralView ArticlePubMedGoogle Scholar
  10. Pierleoni A, Martelli PL, Fariselli P, Casadio R: eSLDB: eukaryotic subcellular localization database. Nucleic Acids Res 2007, 35(Database issue):D208–12. 10.1093/nar/gkl775PubMed CentralView ArticlePubMedGoogle Scholar
  11. Nielsen H, Engelbrecht J, Brunak S, von Heijne G: A neural network method for identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Int J Neural Syst 1997, 8(5–6):581–599. 10.1142/S0129065797000537View ArticlePubMedGoogle Scholar
  12. Mott R, Schultz J, Bork P, Ponting CP: Predicting protein cellular localization using a domain projection method. Genome Res 2002, 12(8):1168–1174. 10.1101/gr.96802PubMed CentralView ArticlePubMedGoogle Scholar
  13. Gardy JL, Spencer C, Wang K, Ester M, Tusnady GE, Simon I, Hua S, deFays K, Lambert C, Nakai K, Brinkman FS: PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria. Nucleic Acids Res 2003, 31(13):3613–3617. 10.1093/nar/gkg602PubMed CentralView ArticlePubMedGoogle Scholar
  14. Hua S, Sun Z: Support vector machine approach for protein subcellular localization prediction. Bioinformatics 2001, 17(8):721–728. 10.1093/bioinformatics/17.8.721View ArticlePubMedGoogle Scholar
  15. Reinhardt A, Hubbard T: Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res 1998, 26(9):2230–2236. 10.1093/nar/26.9.2230PubMed CentralView ArticlePubMedGoogle Scholar
  16. Sarda D, Chua GH, Li KB, Krishnan A: pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties. BMC Bioinformatics 2005, 6: 152. 10.1186/1471-2105-6-152PubMed CentralView ArticlePubMedGoogle Scholar
  17. Foster LJ, de Hoog CL, Zhang Y, Zhang Y, Xie X, Mootha VK, Mann M: A mammalian organelle map by protein correlation profiling. Cell 2006, 125(1):187–199. 10.1016/j.cell.2006.03.022View ArticlePubMedGoogle Scholar
  18. Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, Gasteiger E, Martin MJ, Michoud K, O'Donovan C, Phan I, Pilbout S, Schneider M: The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res 2003, 31(1):365–370. 10.1093/nar/gkg095PubMed CentralView ArticlePubMedGoogle Scholar
  19. Cotter D, Guda P, Fahy E, Subramaniam S: MitoProteome: mitochondrial protein sequence database and annotation system. Nucleic Acids Res 2004, 32(Database issue):D463–7. 10.1093/nar/gkh048PubMed CentralView ArticlePubMedGoogle Scholar
  20. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000, 25(1):25–29. 10.1038/75556PubMed CentralView ArticlePubMedGoogle Scholar
  21. Camon E, Magrane M, Barrell D, Lee V, Dimmer E, Maslen J, Binns D, Harte N, Lopez R, Apweiler R: The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res 2004, 32(Database issue):D262–6. 10.1093/nar/gkh021PubMed CentralView ArticlePubMedGoogle Scholar
  22. Wheeler DL, Chappey C, Lash AE, Leipe DD, Madden TL, Schuler GD, Tatusova TA, Rapp BA: Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2000, 28(1):10–14. 10.1093/nar/28.1.10PubMed CentralView ArticlePubMedGoogle Scholar
  23. Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, Eisenberg D: DIP: the database of interacting proteins. Nucleic Acids Res 2000, 28(1):289–291. 10.1093/nar/28.1.289PubMed CentralView ArticlePubMedGoogle Scholar
  24. Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G: MINT: a Molecular INTeraction database. FEBS Lett 2002, 513(1):135–140. 10.1016/S0014-5793(01)03293-8View ArticlePubMedGoogle Scholar
  25. Bader GD, Betel D, Hogue CW: BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res 2003, 31(1):248–250. 10.1093/nar/gkg056PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© Zhang et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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