| 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:
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