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Subcellular Localization Resources

The following is a collection of links relevant to subcellular localization prediction. If you would like to see a link to a particular program or resource added to this page, please contact us.

At the bottom of the page, we have also provided a suggested reading list containing selected review articles describing SCL and SCL prediction.

Locally hosted resources:

  • PSORTdb A two-component searchable and browsable database. ePSORTdb contains bacterial proteins of experimentally verified localization used in training and testing of PSORTb. cPSORTdb contains predictions of localization for bacterial genomes.
  • Standalone PSORTb for Linux A downloadable version of PSORTb which can be run locally.
  • Datasets of Proteins of Known Localization Datasets of proteins used to train and evaluate PSORTb, as well as links to datasets created by other researchers. NB: the datasets used in PSORTb development can now be accessed through ePSORTdb.
  • Genomes Precomputed PSORTb results for available bacterial genomes. NB: these results are now available in a more powerful searchable and browsable form via cPSORTdb.
  • Motifs and Profiles Associated with Specific Localizations Motifs and Profiles characteristic of specific localization sites used in PSORTb's Motif, Profile, and OMPMotif modules.

Other subcellular localization predictors:

  • ESLPred (Bhasin and Raghava, 2004) uses Support Vector Machine and PSI-BLAST to assign eukaryotic proteins to the nucleus, mitochondrion, cytoplasm, or extracellular space.
  • Proteome Analyst's Subcellular Localization Server (Lu et al, 2004) The 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.
  • LOCnet and LOCtarget (Nair and Rost, 2004). LOCnet is a eukaryotic and prokaryotic loclaization prediction tool that uses several of CUBIC's services to generate a prediction. LOCtarget is a database of predictions generated using LOCnet for eukaryotic structural genomics targets.
  • 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.
  • 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.
  • NNPSL (Reinhardt and Hubbard, 1998) uses amino acid composition to assign a prokaryotic protein to the cytoplasmic, periplasmic, or extracellular sites, and a eukaryotic protein to the cytoplasmic, mitochondrial, nuclear, or extracellular sites.
  • 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.
  • Predotar is designed to predict the presence of mitochondrial and plastid targeting peptides in plant sequences.
  • MitoProt (Claros, 1995) predicts mitochondrial localization of a protein.
  • predictNLS (Cokol et al, 2000) uses nuclear localization signal motifs to predict whether a protein might be localized to the nucleus.

Transmembrane alpha-helix predictors:

Suggested reading:

Gisbert Schneider and Uli Fechner: "Advances in the prediction of protein targeting signals", Proteomics, 4(6):1571-1580 (2004).

Olof Emanuelsson: "Predicting protein subcellular localisation from amino acid sequence information", Briefings in Bioinformatics, 3 (4):361-376 (2002).

Kenta Nakai: "Protein sorting signals and prediction of subcellular localization", Adv. Protein Chem., 54:277-344 (2000).

 


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