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Protein Subcellular Localization

Written by Super User. Posted in Protein

Information on protein subcellular localization.

Protein localization is important as protein function may be localized to specific areas inside the cell or within cellular organelles. These bioinformatic programs and databases contain information and are able to predict where a protein may be localized based on signal sequences or localization sequences contained within the protein. Also see our Link Directory - category Protein Subcellular Localization Bioinformatic Tools

Interesting protein localization papers: Predicting protein subcellular localization: past, present, and future.

Protein Subcellular Localization Databases and Subcellular Prediction for:

Eukaryotes

Mouse (Mus Muscularis)

Plants (Arabidopsis)

Bacteria (Prokaryotes - Gram positive and Negative)

Also see our Link Directory - category Protein Subcellular Localization

General Eukaryotic Protein Subcellular localization Databases:

DBSubLoc - Database of Protein Subcellular Localization

ESLPred (Bhasin and Raghava, 2004) uses Support Vector Machine and PSI-BLAST to assign eukaryotic proteins to the nucleus, mitochondrion, cytoplasm, or extracellular space.

LOCHom  database of subcellular localization predictions based on sequence homology.  Currently Predicts Subcellular Localization of proteins from the following database: SWISS-PROT proteins, Arabidopsis thaliana (plant), Caenorhabditis elegans (worm), Drosophila melanogaster (fly), Mus musculus (mouse), and Homosapiens (human) subcellular protein localization databases.

HSLpred (Bhasin et al, 2005) is a localization prediction tool for human proteins which utilizes support vector machine and PSI-BLAST to generate predictions for 4 localization sites.

LOCSVMPSI (Xie et al, 2005, NAR in press) is a eukaryotic localization prediction method that incorporates evolutionary information into its predictions. The method uses PSI-BLAST and support vector machine to generate predictions for up to 12 localization sites.

LOC3d  database of predicted subcellular localization for eukaryotic PDB chains. Subcellular localization is currently predicted using four different methods: predictNLS (nuclear localization signal), LOChom ( using homology ), LOCkey (using keywords) and LOC3d (neural network based prediction). The reported localization is based on the method which predicts localization of a given protein with the highest confidence.

LOCtree (Nair and Rost, 2005). LOCtree is a eukaryotic and prokaryotic localization prediction tool available at the CUBIC site. Databases of localization predictions made by CUBIC's servers are also available and are described below.

NucPred (Heddad et al, 2004) uses the presence of nuclear localization signals identified through a genetic programming algorithm as the basis of its classification method.

Predotar is designed to predict the presence of mitochondrial and plastid targeting peptides in plant sequences.

predictNLS (Cokol et al, 2000) uses nuclear localization signal motifs to predict whether a protein might be localized to the nucleus

PSLT (Scott et al, 2004) is a Bayesian network-based method that predicts human protein localization based on motif/domain co-occurence. The tool is not yet available online, however its predictions for 9793 human proteins in SWISS-PROT are available for download from the PSLT site.

pSLIP (Sarda et al, 2005) uses support vector machine and multiple physiochemical properties of amino acids to assign a eukaryotic protein to one of six localization sites.

Proteome Analyst's Subcellular Localization Server (Lu et al, 2004) This specialized server available at the PENCE Proteome Analyst site is able to classify Gram-negative, Gram-positive, fungi, plant and animal proteins to many localization sites. A database of predictions is also available and is described below.

pTARGET (Guda and Subramaniam, 2005) uses amino acid composition and localization-specific Pfam domains to assign a eukaryotic protein to one of nine localization sites.

Protein Prowler (Boden and Hawkins, 2005) classifies eukaryotic targeting signals as secretory, mitochondrion, chloroplast or other.

PSORTII

SecretomeP (Bendtsen et al, 2004) predicts eukaryotic proteins which are secreted via a non-traditional secretory mechanism.

SignalP (Bendtsen et al, 2004) predicts traditional N-terminal signal peptides in both prokaryotic and eukaryotic proteins.

SubLoc (Hua and Sun, 2001) uses Support Vector Machine to assign a prokaryotic protein to the cytoplasmic, periplasmic, or extracellular sites, and a eukaryotic protein to the cytoplasmic, mitochondrial, nuclear, or extracellular sites. A modified version of SubLoc was used in PSORT-B v.1.1 to differentiate cytoplasmic and non-cytoplasmic proteins.

TargetP (Emanuelsson et al, 2000) predicts the presence of signal peptides, chloroplast transit peptides, and mitochondrial targeting peptides for plant proteins, and the presence of signal peptides and mitochondrial targeting peptides for eukaryotic proteins.

Mouse Protein Subcellular localization Databases:

LOCATE is a curated database that houses data describing the membrane organization and subcellular localization of proteins from the RIKEN FANTOM3 mouse protein sequence set. The membrane organization is predicted by the high-throughput, computational pipeline MemO. The subcellular locations were determined by a high-throughput, immunofluorescence-based assay and by manually reviewing peer-reviewed publications.

Protein Subcellular Localization Databases for Plants (and Arabidopsis):

LOCHom  database of subcellular localization predictions based on sequence homology.  Currently Predicts Subcellular Localization of proteins from the following Arabidopsis thaliana (plant).

PSORT plant sequence protein subcellular localization database for plants.

Arabidopsis SubCellular Proteomic Database (SUBA)

The Plant Specific Database Search by Gene Family

Prokaryotic Protein Subcellular Localization Databases for Bacteria:

PSORT PSLpred (Bhasin et al, 2005) is a localization prediction tool for Gram-negative bacteria which utilizes support vector machine and PSI-BLAST to generate predictions for 5 localization sites.

LOCtree (Nair and Rost, 2005). LOCtree is a eukaryotic and prokaryotic localization prediction tool available at the CUBIC site. Databases of localization predictions made by CUBIC's servers are also available and are described below.


CELLO (Yu et al, 2004) uses Support Vector Machine based on n-peptide composition to assign a Gram-negative protein to the cytoplasm, inner membrane, periplasm, outer membrane or extracellular space.

SubLoc (Hua and Sun, 2001) uses Support Vector Machine to assign a prokaryotic protein to the cytoplasmic, periplasmic, or extracellular sites, and a eukaryotic protein to the cytoplasmic, mitochondrial, nuclear, or extracellular sites. A modified version of SubLoc was used in PSORT-B v.1.1 to differentiate cytoplasmic and non-cytoplasmic proteins.

SignalP (Bendtsen et al, 2004) predicts traditional N-terminal signal peptides in both prokaryotic and eukaryotic proteins.
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