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Software

Protein localization predicition software

Name	Year	Description	Target	References
APSLAP		Prediction of apoptosis protein sub cellular Localization		[1]
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.		[3]
CELLO		CELLO uses a two-level Support Vector Machine system to assign localizations to both prokaryotic and eukaryotic proteins.		[4] [5]
ClubSub-P		ClubSub-P is a database of cluster-based subcellular localization (SCL) predictions for Archaea and Gram negative bacteria.		[6]
Euk-mPLoc 2.0		Predicting the subcellular localization of eukaryotic proteins with both single and multiple sites.		[7]
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.		[8]
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.		[9]
KnowPredsite		A knowledge-based approach to predict the localization site(s) of both single-localized and multi-localized proteins for all eukaryotes.		[10]
LOCtree		Prediction based on mimicking the cellular sorting mechanism using a hierarchical implementation of SwissProt keywords .		[11]
LocTree2/3		Subcellular localization prediction for all proteins in all domains of life. LocTree2/3 predicts 3 classes for Archaea, 6 for Bacteria and 18 for Eukaryota		[12] [13]
MultiLoc		An SVM-based prediction engine for a wide range of subcellular locations.		[14]
PSORT		The first widely used method for protein subcellular localization prediction, developed under the leadership of Kenta Nakai. 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.		[15]
PSORTb		Prediction of bacterial protein localization.		[16] [17]
MetaLocGramN		Meta subcellular localization predictor of Gram-negative protein. MetaLocGramN is a gateway to a number of primary prediction methods (various types: signal peptide, beta-barrel, transmembrane helices and subcellular localization predictors). In author's benchmark, MetaLocGramN performed better in comparison to other SCL predictive methods, since the average Matthews correlation coefficient reached 0.806 that enhanced the predictive capability by 12% (compared to PSORTb3). MetaLocGramN can be run via SOAP.		[18]
PredictNLS		Prediction of nuclear localization signals .		[19]
Proteome Analyst		Prediction of protein localization for both prokaryotes and eukaryotes using a text mining approach.		[20]
SCLPred		SCLpred protein subcellular localization prediction by N-to-1 neural networks.		[21]
SecretomeP		Prediction of eukaryotic proteins that are secreted via a non-traditional secretory mechanism.		[22]
SherLoc		An SVM-based predictor combining MultiLoc with text-based features derived from PubMed abstracts.		[23]
SCLAP		An Adaptive Boosting Method for Predicting Subchloroplast Localization of Plant Proteins.		[24]
TargetP		Prediction of N-terminal sorting signals.		[25]
TMHMM		Prediction of transmembrane helices to identify transmembrane proteins.
WoLF PSORT		An updated version of PSORT/PSORT II for the prediction of eukaryotic sequences.		[26]

Application

Determining subcellular localization is important for understanding protein function and is a critical step in genome annotation.

Knowledge of the subcellular localization of a protein can significantly improve target identification during the

proteins are easily accessible by drug molecules due to their localization in the extracellular space or on the cell surface.

Bacterial cell surface and secreted proteins are also of interest for their potential as vaccine candidates or as diagnostic targets.

Aberrant subcellular localization of proteins has been observed in the cells of several diseases, such as cancer and Alzheimer's disease.

Secreted proteins from some archaea that can survive in unusual environments have industrially important applications.

Databases

Curated protein subcellular locations can be searched in UniProtKB. There are several computationally predicted protein subcellular location databases including the fungal secretome and subcellular proteome knowledgebase (FunSecKB2), the plant secretome and subcellular proteome knowledgebase (PlantSecKB), MetazSecKB for human and animals, and the lactic acid bacterial secretome database. Though there are some inaccuracies in the computational prediction, these databases provide useful resources for further characterizing the protein subcellular locations.

References

Further reading

• Bork P, Dandekar T, Diaz-Lazcoz Y, Eisenhaber F, Huynen M, Yuan Y (November 1998). "Predicting function: from genes to genomes and back". J. Mol. Biol. 283 (4): 707–25.
  PMID 9790834.{{cite journal}}: CS1 maint: multiple names: authors list (link
  )
• Nakai K (2000). "Protein sorting signals and prediction of subcellular localization". Adv. Protein Chem. 54: 277–344.
  PMID 10829231
  .
• Emanuelsson O (December 2002). "Predicting protein subcellular localisation from amino acid sequence information". Brief. Bioinformatics. 3 (4): 361–76.
  PMID 12511065
  .
• Schneider G, Fechner U (June 2004). "Advances in the prediction of protein targeting signals". Proteomics. 4 (6): 1571–80.
  PMID 15174127
  .
• Gardy JL, Brinkman FS (October 2006). "Methods for predicting bacterial protein subcellular localization". Nat. Rev. Microbiol. 4 (10): 741–51.
  PMID 16964270
  .
• Chou KC, Shen HB (November 2007). "Recent progress in protein subcellular location prediction". Anal. Biochem. 370 (1): 1–16.
  PMID 17698024
  .

Category:Protein methods Category:Cell biology Category:Bioinformatics Category:Computational science Category:Bioinformatics software Category:Protein targeting