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Protein Subcellular Localization Protein Subcellular Localization

Protein localization is important as protein function may be localized to specific areas inside the cell or within cellular organelles. Protein lokalisering er vigtigt, da protein funktion kan være lokaliseret til bestemte områder inde i cellen eller inden for cellulær 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. Disse Bioinformatic programmer og databaser indeholder oplysninger og er i stand til at forudsige, hvor et protein kan være lokaliseret baseret på signal sekvenser eller lokalisering sekvenser indeholdt inden for protein. Also see our Link Directory - category Protein Subcellular Localization Bioinformatic Tools Se også vores Link Directory - kategori Protein Subcellular Localization Bioinformatic Tools

Interesting protein localization papers: Predicting protein subcellular localization: past, present, and future. Interessante protein localization papers: Forudsigelse af protein subcellular localization: fortid, nutid og fremtid.

Protein Subcellular Localization Databases and Subcellular Prediction for: Protein Subcellular Localization Databaser og Subcellular Fremskrivning for:

Eukaryotes Eukaryoter

Mouse (Mus Muscularis) Mus (Mus Muscularis)

Plants (Arabidopsis) Planter (Arabidopsis)

Bacteria (Prokaryotes - Gram positive and Negative) Bakterier (prokaryoter - Gram positive og negative)

Also see our Link Directory - category Protein Subcellular Localization Se også vores Link Directory - kategori Protein Subcellular Localization

General Eukaryotic Protein Subcellular localization Databases: Almindelige eukaryot Protein Subcellular localization Databaser:

DBSubLoc - Database of Protein Subcellular Localization DBSubLoc - Database over 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. ESLPred (Bhasin og Raghava, 2004) anvendelser Support Vector Machine og PSI-BLAST at tildele eukaryotiske proteiner til kernen, mitochondrion, cytoplasma, eller ekstracellulære rum.

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. LOCHom database over subcellular localization forudsigelser baseret på sekvenshomologi. Øjeblikket forudsiger Subcellular Localization af proteiner fra følgende database: schweizisk-PROT proteiner, Arabidopsis thaliana (anlæg), Caenorhabditis elegans (ormen), Drosophila melanogaster (flyve), Mus musculus (mus) , Og Homosapiens (menneskelige) subcellular protein localization databaser.

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. HSLpred (Bhasin et al, 2005) er en lokalisering forudsigelse redskab for menneskets proteiner, som udnytter støtte vektor maskine og PSI-BLAST til at generere forudsigelser for 4 localization websteder.

LOCSVMPSI (Xie et al, 2005, NAR in press) is a eukaryotic localization prediction method that incorporates evolutionary information into its predictions. LOCSVMPSI (Xie et al, 2005, NAR i pressen) er en eukaryot localization forudsigelse metode, der indarbejder evolutionære oplysninger i sine forudsigelser. The method uses PSI-BLAST and support vector machine to generate predictions for up to 12 localization sites. Den metode bruger PSI-BLAST og støtte vektor maskine til at generere forudsigelser for op til 12 localization websteder.

LOC3d database of predicted subcellular localization for eukaryotic PDB chains. LOC3d database over forventet subcellular localization for eukaryot FBF kæder. 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). Subcellular localization er i øjeblikket forudsagt ved hjælp af fire forskellige metoder: predictNLS (nuklear localization signal), LOChom (vha. Homology), LOCkey (ved hjælp af søgeord) og LOC3d (neurale netværk baseret forudsigelse). The reported localization is based on the method which predicts localization of a given protein with the highest confidence. De indberettede lokalisering er baseret på den metode, der forudsiger lokalisering af et bestemt protein med den højeste tillid.

LOCtree (Nair and Rost, 2005). LOCtree (Nair og Rost, 2005). LOCtree is a eukaryotic and prokaryotic localization prediction tool available at the CUBIC site. LOCtree er en eukaryot og prokaryote localization forudsigelse værktøj til rådighed på Cubic websted. Databases of localization predictions made by CUBIC's servers are also available and are described below. Databaser af lokalisering forudsigelser foretaget af Cubic's servere er også tilgængelige, og som er beskrevet nedenfor.

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. NucPred (Heddad et al, 2004) bruger tilstedeværelsen af nukleart localization signaler identificeres ved hjælp af en genetisk programmering algoritme som grundlag for sin klassificering metode.

Predotar is designed to predict the presence of mitochondrial and plastid targeting peptides in plant sequences. Predotar er designet til at forudsige tilstedeværelsen af mitokondrier og Plastid målretning peptider i plante-sekvenser.

predictNLS (Cokol et al, 2000) uses nuclear localization signal motifs to predict whether a protein might be localized to the nucleus predictNLS (Cokol et al, 2000) bruger nukleare localization signal motiver til at forudsige, om et protein kan være lokaliseret til kernen

PSLT (Scott et al, 2004) is a Bayesian network-based method that predicts human protein localization based on motif/domain co-occurence. PSLT (Scott et al, 2004) er en Bayesianske net-baseret metode, der forudsiger menneskelige protein localization baseret på motiv / domæne 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. Værktøjet er endnu ikke tilgængelig online, dog sine prognoser for 9793 humane proteiner i schweizisk-PROT er tilgængelige for download fra PSLT websted.

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. pSLIP (Sarda et al, 2005) bruger støtte vektor maskine og flere fysisk-kemiske egenskaber af aminosyrer til at tildele en eukaryot protein til én af seks localization websteder.

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. Proteome Analyst's Subcellular Localization Server (Lu et al, 2004) Dette specialiserede server til rådighed på pence Proteome Analyst netsted er i stand til at klassificere Gram-negative, Gram-positive, svampe, vegetabilske og animalske proteiner til mange localization websteder. A database of predictions is also available and is described below. En database med forudsigelser er også tilgængelig og er beskrevet nedenfor.

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. pTARGET (Guda og Subramaniam, 2005) bruger aminosyre sammensætning og lokalisering-specifikke Pfam domæner for at tildele en eukaryot protein til én af ni localization websteder.

Protein Prowler (Boden and Hawkins, 2005) classifies eukaryotic targeting signals as secretory, mitochondrion, chloroplast or other. Protein Prowler (Boden og Hawkins, 2005) klassificerer eukaryot målretning signaler secretory, mitochondrion, chloroplast eller andre.

PSORTII PSORTII

SecretomeP (Bendtsen et al, 2004) predicts eukaryotic proteins which are secreted via a non-traditional secretory mechanism. SecretomeP (Bendtsen et al, 2004) forudsiger eukaryotiske proteiner, som udskilles via et ikke-traditionelle secretory mekanisme.

SignalP (Bendtsen et al, 2004) predicts traditional N-terminal signal peptides in both prokaryotic and eukaryotic proteins. SignalP (Bendtsen et al, 2004) forudsiger traditionelle N-terminal signal peptider i både prokaryote og eukaryotiske proteiner.

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. SubLoc (Hua og Sun, 2001) anvendelser Support Vector Machine til at tildele et prokaryotisk protein til cytoplasmic, periplasmic, eller ekstracellulære websteder, og en eukaryot protein til cytoplasmic, mitokondrier, nukleare, eller ekstracellulære websteder. A modified version of SubLoc was used in PSORT-B v.1.1 to differentiate cytoplasmic and non-cytoplasmic proteins. En modificeret version af SubLoc blev benyttet i PSORT-B V.1.1 at differentiere cytoplasmic og ikke-cytoplasmic proteiner.

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. TargetP (Emanuelsson et al, 2000) forudsiger tilstedeværelse af signal peptider, chloroplast-transit peptider, og mitokondrier målretning peptider for vegetabilske proteiner, og tilstedeværelsen af signal peptider og mitokondrier målretning peptider for eukaryotiske proteiner.

Mouse Protein Subcellular localization Databases: Mouse Protein Subcellular localization Databaser:

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. LOCATE er en kurateret database, huse data, der beskriver membranen organisation og subcellular lokalisering af proteiner fra RIKEN FANTOM3 musen protein sekvens sæt. The membrane organization is predicted by the high-throughput, computational pipeline MemO. Membranen organisation er forudsagt af høj overførselshastighed, computerstøttet rørledning notat. The subcellular locations were determined by a high-throughput, immunofluorescence-based assay and by manually reviewing peer-reviewed publications. De subcellular steder blev bestemt ved en høj overførselshastighed, immunofluorescens-baserede assay og ved manuelt at gennemgå peer-reviewede publikationer.

Protein Subcellular Localization Databases for Plants (and Arabidopsis): Protein Subcellular Localization databaser for Planter (og Arabidopsis):

LOCHom database of subcellular localization predictions based on sequence homology.  Currently Predicts Subcellular Localization of proteins from the following Arabidopsis thaliana (plant). LOCHom database over subcellular localization forudsigelser baseret på sekvenshomologi. Øjeblikket forudsiger Subcellular Localization af proteiner fra følgende Arabidopsis thaliana (anlæg).

PSORT plant sequence protein subcellular localization database for plants. PSORT plante sekvens protein subcellular localization database for planter.

Arabidopsis SubCellular Proteomic Database (SUBA) Arabidopsis SubCellular proteomiske Database (SUBA)

The Plant Specific Database Search by Gene Family De Plant særlig database Søg på Gene Family

Prokaryotic Protein Subcellular Localization Databases for Bacteria: Prokaryote Protein Subcellular Localization databaser til Bakterier:

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. PSORT PSLpred (Bhasin et al, 2005) er en lokalisering forudsigelse redskab for Gram-negative bakterier, som udnytter støtte vektor maskine og PSI-BLAST til at generere forudsigelser for 5 localization websteder.

LOCtree (Nair and Rost, 2005). LOCtree (Nair og Rost, 2005). LOCtree is a eukaryotic and prokaryotic localization prediction tool available at the CUBIC site. LOCtree er en eukaryot og prokaryote localization forudsigelse værktøj til rådighed på Cubic websted. Databases of localization predictions made by CUBIC's servers are also available and are described below. Databaser af lokalisering forudsigelser foretaget af Cubic's servere er også tilgængelige, og som er beskrevet nedenfor.

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. Cello (Yu et al, 2004) anvendelser Support Vector Machine baseret på n-peptid sammensætning for at tildele en Gram-negative protein til cytoplasma, inderste membran, periplasm, i det ydre membran eller ekstracellulære rum.

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. SubLoc (Hua og Sun, 2001) anvendelser Support Vector Machine til at tildele et prokaryotisk protein til cytoplasmic, periplasmic, eller ekstracellulære websteder, og en eukaryot protein til cytoplasmic, mitokondrier, nukleare, eller ekstracellulære websteder. A modified version of SubLoc was used in PSORT-B v.1.1 to differentiate cytoplasmic and non-cytoplasmic proteins. En modificeret version af SubLoc blev benyttet i PSORT-B V.1.1 at differentiere cytoplasmic og ikke-cytoplasmic proteiner.

SignalP (Bendtsen et al, 2004) predicts traditional N-terminal signal peptides in both prokaryotic and eukaryotic proteins. SignalP (Bendtsen et al, 2004) forudsiger traditionelle N-terminal signal peptider i både prokaryote og eukaryotiske proteiner.