Subcellular Localization
eSLDB is a database of protein subcellular localization annotation
for eukaryotic organisms. It contains experimental annotations derived from
primary protein databases, homology based annotations and computational
predictions.
http://gpcr.biocomp.unibo.it/esldb/
DBSubLoc is a database of protein subcellular localization. This
database contains proteins from primary protein database SWISS-PROT and PIR.
http://www.bioinfo.tsinghua.edu.cn/dbsubloc.html
A database of organelle proteins, and subcellular structures /
complexes
http://organelledb.lsi.umich.edu/
LOCATE is a curated database that houses data describing the
membrane organization and subcellular localization of proteins from the RIKEN
FANTOM4 mouse and human protein sequence set.
http://locate.imb.uq.edu.au/
LocDB is a expert curated database that collects experimental
annotations for the subcellular localization of proteins in Human (Homo
sapiens) and Weed (Arabidopsis thaliana).
http://www.rostlab.org/services/locDB/
Predicts the subcellular location of eukaryotic proteins (use
results of ChloroP and SignalP). Secretory signal peptides, mitochondrial
targeting peptides and chloroplast transit peptides in eukaryotes.
http://www.cbs.dtu.dk/services/TargetP/
Protein Subcellular Localization Prediction (plant, animal,fungi)
http://wolfpsort.org/
bacterial protein subcellular localization prediction
http://www.psort.org/psortb/
The SecretomeP 2.0 server produces ab initio predictions of
non-classical i.e. not signal peptide triggered protein secretion. The method
queries a large number of other feature prediction servers to obtain
information on various post-translational and localizational aspects of the
protein, which are integrated into the final secretion prediction.
http://www.cbs.dtu.dk/services/SecretomeP/
Twin-arginine translocation signal peptides in bacteria.
http://www.cbs.dtu.dk/services/TatP/
BaCelLo is a predictor for the subcellular localization of
proteins in eukaryotes. It is based on a decision tree of several support
vector machines (SVMs), it classifies up to four localizations for Fungi and
Metazoan proteins and five localizations for Plant ones
http://gpcr.biocomp.unibo.it/bacello/
Subcellular Localisation Predictor (locations in plants and in
other eukaryotes (TargetP data))..
http://pprowler.imb.uq.edu.au/
Subcellular localization predictor; CELLO is a multi-class SVM
classification system.
http://cello.life.nctu.edu.tw/
The Proteome Analyst Specialized Subcellular Localization Server
(PA-SUB) is part of Proteome Analyst (PA). PA is a web server built to predict
protein properties, such as general function, in a high-throughput fashion.
PA-SUB is specialized to predict the subcellular localization of proteins using
established machine learning techniques.
http://www.cs.ualberta.ca/~bioinfo/PA/Sub/
Subcellular location in plants, other eukaryotes, fungi.
http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc/
PSLpred is a SVM based method to predict 5 major subcelullar
localization (cytoplam, inner-membrane, outermembrane, extracellular, and
periplasm) of Gram-negative bacteria.
http://www.imtech.res.in/raghava/pslpred/
pTARGET is a computational method to predict the subcellular
localization of only eukaryotic proteins from animal species that include fungi
and metazoans. Predictions are carried out based on the occurrence patterns of
protein functional domains and the amino acid compositional differences in
proteins from different subcellular locations. This method can predict proteins
targeted to nine distinct subcellular locations that include cytoplasm,
endoplasmic reticulum, extracellular/secreted, Golgi, lysosomes, mitochondria,
nucleus, peroxysomes and plasma membrane.
http://bioapps.rit.albany.edu/pTARGET//
SubLoc is a prediction system for protein subcellular localization
based on amino acid composition alone.
http://www.bioinfo.tsinghua.edu.cn/SubLoc/
ProTeus (PROtein TErminUS) is a tool for identification of short
linear signatures in protein termini. It is based on a positional-based search
method for revealing short significant signatures in termini of proteins.
http://www.proteus.cs.huji.ac.il/
SignalP 3.0 server predicts the presence and location of signal
peptide cleavage sites in amino acid sequences from different organisms:
Gram-positive prokaryotes, Gram-negative prokaryotes, and eukaryotes. The
method incorporates a prediction of cleavage sites and a signal
peptide/non-signal peptide prediction based on a combination of several
artificial neural networks and hidden Markov models.
http://www.cbs.dtu.dk/services/SignalP/
PrediSi (PREDIction of SIgnal peptides) is a software tool for
predicting signal peptide sequences and their cleavage positions in bacterial
and eukaryotic proteins.
http://www.predisi.de/
Signal-3L is an automated method for predicting signal peptide
sequences and their cleavage sites in eukaryotic and bacterial protein
sequences that uses a 3-layer approach for predicting signal peptides.
http://www.csbio.sjtu.edu.cn/bioinf/Signal-3L/
Signal Peptide Prediction.
http://bmbpcu36.leeds.ac.uk/prot_analysis/Signal.html
Online tool to find and list homologies of published signal
sequences with the input DNA sequence.
http://www-bimas.cit.nih.gov/molbio/signal/
iPSORT is a subcellular localization site predictor for N-terminal
sorting signals. Given a protein sequence , it will predict whether it contains
a Signal Peptide (SP), Mitochondrial Targeting Peptide (mTP), or Chloroplast
Transit Peptide (cTP).
http://hc.ims.u-tokyo.ac.jp/iPSORT/
Prediction of Signal Peptides in Archaea with Hidden Markov
Models.
http://bioinformatics.biol.uoa.gr/PRED-SIGNAL/
Prediction of Lipoprotein and Secretory Signal Peptides in
Gram-positive Bacteria with Hidden Markov Models.
http://bioinformatics.biol.uoa.gr/PRED-LIPO/
High performance system for signal peptide prediction.
http://bp.nuap.nagoya-u.ac.jp/sosui/sosuisignal/sosuisignal_submit.html
MITOPROT: Prediction of mitochondrial targeting sequences
http://ihg.gsf.de/ihg/mitoprot.html
Prediction of N-terminal sequence for mitochondrial, plastid and
ER targeting sequences
http://urgi.versailles.inra.fr/predotar/predotar.html
Prediction of chloroplast transit peptides
http://www.cbs.dtu.dk/services/ChloroP/
Prediction of Golgi Type II membrane proteins based on their
transmembrane domains.
http://ccb.imb.uq.edu.au/golgi/golgi_predictor.shtml
Prediction of lipoproteins and signal peptides in Gram negative
bacteria.
http://www.cbs.dtu.dk/services/LipoP/
Prediction of Lipoprotein and Secretory Signal Peptides in
Gram-positive Bacteria with Hidden Markov Models.
http://bioinformatics.biol.uoa.gr/PRED-LIPO/
Prediction of peroxisomal signal
http://mendel.imp.ac.at/mendeljsp/sat/pts1/PTS1predictor.jsp
Predicts peroxisomal proteins and Pfam domains
http://www.bioinfo.se/PeroxiP/
Predicting Nuclear Localization of Proteins
http://www.sbc.su.se/~maccallr/nucpred/
NetNES 1.1 server predicts leucine-rich nuclear export signals
(NES) in eukaryotic proteins using a combination of neural networks and hidden
Markov models.
http://www.cbs.dtu.dk/services/NetNES-1.1/index.php
GPI lipid anchor predictor in animals
http://mendel.imp.ac.at/sat/gpi/gpi_server.html
GPI lipid anchor predictor in plants
http://mendel.imp.ac.at/sat/gpi/plant_server.html
GPI lipid anchor predictor in fungi
http://mendel.imp.ac.at/sat/gpi/fungi_server.html
Identification of GPI-anchor signals by a Kohonen Self Organizing
Map
http://gpi.unibe.ch/
Prediction Tools
- WoLF
PSORT (Horton
et al., 2007)
: General Eukaryotic Localization Prediction (based on PSORTII, iPSORT) - subnuclear (Lei
and Dai, 2005)
: Predict subnuclear localizations - pTARGET (Guda
and Subramaniam, 2005)
: Based on the occurrence patterns of protein functional domains and the amino acid compositional differences. - pSLIP (Sarda
et al., 2005)
: SVM based using multiple physicochemical properties - P2SL (Atalay
and Cetin-Atalay, 2005)
: Implicit motif distribution based hybrid computational kernel - PSLpred (Bhasin
et al., 2005)
: A svm based method for prokaryotic proteins - PPROWLER (Boden et al., 2005)
: Detecting residues in targeting peptides - CoupleLoc (Guo et al., 2005)
: Combining residue-couple model and SVM - SVMtm
Predictor (Yuan
et al., 2004)
: SVM for Transmembrane Segments Prediction - PrediSi (Hiller
et al., 2004)
: Prediction of signal peptides and their cleavage positions - ESLpred (Bhasin
and Raghava, 2004)
: SVM for Eukaryotics using Dipeptide composition & PSI-BLAST - LOCtarget (Nair
and Rost, 2004)
: Database for structrual genomics targets - PA-Sub (Lu
et al., 2004)
: Proteome Analyst Specialized Subcellular Localication Server - PSORT-B (Gardy
et al., 2003)
: An updated version of PSORT for Gram-negative bacteria - BPROMPT (Taylor
et al., 2003)
: A consensus server for membrane protein prediction - SubLoc (Hua
et al., 2002)
: SVM prediction system based on amino acid composition alone - TMHMM (Krogh
et al, 2001)
: Prediction of transmembrane helics in proteins - TargetP (Emanuelsson
et al., 2000)
: Prediction for eukaryotics based on N-terminal signal sequences - PredictNLS (Cokol
et al., 2000)
: Tool for Nuclear Localization Signals Analysis - NNPSL (Reinhardt
et al., 1998)
: Prediction of Subcellular Localization by Neural Networks - ChloroP (Emanuelsson
et al., 1999)
: Predict the presence of chloroplast transit peptides - HMMTOP (Tusnady
et al., 1998)
: Prediction of transmembrane helics and topology of proteins - SignalP (Nielsen
et al., 1997)
: Predict the presence and location of signal peptides - PSORT (Nakai et al., 1990s)
: First computer program for subcellular location prediction
- Cell-PLoc: A
package of web-servers for predicting subcellular localization of proteins
in various organisms.[2]
- BaCelLo: Prediction of
eukaryotic protein subcellular localization. Unlike other methods, the
predictions are balanced among different classes and all the localizations
that are predicted are considered as equiprobable, to avoid
mispredictions.[9]
- CELLO: CELLO uses a two-level
Support Vector Machine system to assign localizations to both prokaryotic
and eukaryotic proteins.[10][11]
- ClubSub-P: ClubSub-P
is a database of cluster-based subcellular localization (SCL) predictions
for Archaea and Gram negative bacteria. [12]
- Euk-mPLoc 2.0:
Predicting the subcellular localization of eukaryotic proteins with both
single and multiple sites.[13]
- CoBaltDB:
CoBaltDB is a novel powerful platform that provides easy access to the
results of multiple localization tools and support for predicting
prokaryotic protein localizations.[14]
- HSLpred: This method
allow to predict subcellular localization of human proteins. This method
combines power of composition based SVM models and similarity search
techniques PSI-BLAST.[15]
- KnowPredsite: A
knowledge-based approach to predict the localization site(s) of both
single-localized and multi-localized proteins for all eukaryotes.[16]
- LOCtree: Prediction based
on mimicking the cellular sorting mechanism using a hierarchical
implementation of support vector machines. LOCtree is a
comprehensive predictor incorporating predictions based on PROSITE/PFAM signatures as well as SwissProt
keywords.[17]
- MultiLoc:
An SVM-based prediction engine for a wide range of subcellular locations.[18]
- PSORT:
The first widely used method for protein subcellular localization
prediction, developed under the leadership of Kenta Nakai.[19]
Now researchers are also encouraged to use other PSORT programs such as
WoLF PSORT and PSORTb for making predictions for certain types of
organisms (see below). PSORT prediction performances are lower than those of
recently developed predictors.
- PSORTb:
Prediction of bacterial protein localization.[20][21]
- MetaLocGramN: Meta
subcellular localization predictor of Gram-negative protein.[22]
(submitted)
- PredictNLS: Prediction of nuclear localization signals.[23]
- Proteome Analyst:
Prediction of protein localization for both prokaryotes and eukaryotes
using a text mining approach.[24]
- SecretomeP:
Prediction of eukaryotic proteins that are secreted via a non-traditional
secretory mechanism.[25]
- SherLoc:
An SVM-based predictor combining MultiLoc with text-based features derived
from PubMed abstracts.[26]
- TargetP: Prediction of
N-terminal sorting signals.[27]
- WoLF
PSORT: An updated version of PSORT/PSORT II for the prediction of
eukaryotic sequences.[28]
|
Tools for Prediction
of Protein Localization
|
Protein sorting
ChloroP »
Chloroplast transit peptides and their cleavage sites in plant proteins
Chloroplast transit peptides and their cleavage sites in plant proteins
LipoP »
Signal peptidase I & II cleavage sites in gram- bacteria
Signal peptidase I & II cleavage sites in gram- bacteria
NetNES »
Leucine-rich nuclear export signals (NES) in eukaryotic proteins
Leucine-rich nuclear export signals (NES) in eukaryotic proteins
SecretomeP »
Non-classical and leaderless secretion of proteins
Non-classical and leaderless secretion of proteins
SignalP »
Signal peptide and cleavage sites in gram+, gram- and eukaryotic amino acid sequences
Signal peptide and cleavage sites in gram+, gram- and eukaryotic amino acid sequences
TargetP »
Subcellular location of proteins: mitochondrial, chloroplastic, secretory pathway, or other
Subcellular location of proteins: mitochondrial, chloroplastic, secretory pathway, or other
TatP »
Twin-arginine signal peptides
Twin-arginine signal peptides
Post-translational modifications of proteins
DictyOGlyc
O-(alpha)-GlcNAc glycosylation sites (trained on Dictyostelium discoideum proteins)
O-(alpha)-GlcNAc glycosylation sites (trained on Dictyostelium discoideum proteins)
NetAcet
N-terminal acetylation in eukaryotic proteins
N-terminal acetylation in eukaryotic proteins
NetCGlyc »
C-mannosylation sites in mammalian proteins
C-mannosylation sites in mammalian proteins
NetCorona
Coronavirus 3C-like proteinase cleavage sites in proteins
Coronavirus 3C-like proteinase cleavage sites in proteins
NetGlycate »
Glycation of ε amino groups of lysines in mammalian proteins
Glycation of ε amino groups of lysines in mammalian proteins
NetNGlyc »
N-linked glycosylation sites in human proteins
N-linked glycosylation sites in human proteins
NetNGlyc »
N-linked glycosylation sites in human proteins
N-linked glycosylation sites in human proteins
NetOGlyc »
O-GalNAc (mucin type) glycosylation sites in mammalian proteins
O-GalNAc (mucin type) glycosylation sites in mammalian proteins
NetOGlyc »
O-GalNAc (mucin type) glycosylation sites in mammalian proteins
O-GalNAc (mucin type) glycosylation sites in mammalian proteins
NetPhorest
Linear motif atlas for phosphorylation-dependent signaling
Linear motif atlas for phosphorylation-dependent signaling
NetPhos »
Generic phosphorylation sites in eukaryotic proteins
Generic phosphorylation sites in eukaryotic proteins
NetPhosBac
Generic phosphorylation sites in bacterial proteins
Generic phosphorylation sites in bacterial proteins
NetPhosK
Kinase specific phosphorylation sites in eukaryotic proteins
Kinase specific phosphorylation sites in eukaryotic proteins
NetPhosYeast
Serine and threonine phosphorylation sites in yeast proteins
Serine and threonine phosphorylation sites in yeast proteins
NetPicoRNA
Posttranslational cleavage by picornaviral proteases
Posttranslational cleavage by picornaviral proteases
NetworKIN
In vivo kinase-substrate relationships
In vivo kinase-substrate relationships
ProP »
Arginine and lysine propeptide cleavage sites in eukaryotic protein sequences
Arginine and lysine propeptide cleavage sites in eukaryotic protein sequences
YinOYang »
O-(beta)-GlcNAc glycosylation and Yin-Yang sites (intracellular/nuclear proteins)
O-(beta)-GlcNAc glycosylation and Yin-Yang sites (intracellular/nuclear proteins)
|