View source for Protein subcellular localization prediction

'''Protein subcellular localization prediction''' involves the computational prediction of where a [[protein]] resides in a [[cell (biology)|cell]]. Prediction of protein subcellular localization is an important component of [[bioinformatics]]-based prediction of protein function and [[genome project|genome annotation]], and it can aid the identification of drug targets.  

Most [[eukaryote|eukaryotic]] [[proteins]] are encoded in the nuclear [[genome]] and synthesized in the [[cytosol]], but many need to be further [[protein targeting|sorted]] before they reach their final destination. For [[prokaryote|prokaryotes]], proteins are synthesized in the cytoplasm and some must be targeted to other locations such as to a [[cell membrane]] or the [[extracellular]] environment. Proteins must be localized at their appropriate [[subcellular localization|subcellular compartment]] to perform their desired function.

Experimentally determining the [[subcellular localization]] of a [[protein]] is a laborious and time consuming task. Through the development of new approaches in computer science, coupled with an increased dataset of proteins of known localization, computational tools can now provide fast and accurate localization predictions for many organisms. This has resulted in subcellular localization prediction becoming one of the challenges being successfully aided by [[bioinformatics]]. Many protein subcellular localization prediction methods now exceed the accuracy of some high-throughput laboratory methods for the identification of protein subcellular localization.<ref>Rey, S., J.L. Gardy, and [[Fiona Brinkman|F.S.L. Brinkman]] (2005). "Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria." BMC Genomics 6:162</ref>

== Methods ==

Several computational tools for predicting the subcellular localization of a protein are publicly available, a few of which are listed below. Note that the number of different subcellular localizations predicted for each method varies, and the accuracy of methods varies, so different methods are suitable depending on what you want to predict and how sensitive or specific you wish your analysis to be. Methods for the prediction of bacterial localization predictors, and their accuracy, have been recently reviewed.<ref>Gardy, J.L., and [[Fiona Brinkman|F.S.L. Brinkman]] (2006). "Methods for predicting bacterial protein subcellular localization." Nature Reviews Microbiology 4:741-751.</ref> See also the [http://www.psort.org/ PSORT.org] portal for a more extensive list of localization predictors for both bacteria and eukaryotes: 

* [[PSORT]] [http://psort.nibb.ac.jp/]: The first widely used method for protein subcellular localization prediction, developed under the leadership of [[PSORT|Kenta Nakai]]. <ref>Nakai, K., and M. Kanehisa (1991). Expert system for predicting protein localization sites in gram-negative bacteria. Proteins. 11:95-110.</ref> 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.

* LOCtree [http://rostlab.org/services/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]]. <ref>Nair, R. & Rost,B. (2005). "Mimicking cellular sorting improves prediction of Subcellular Localization." J Mol Biol 348:85-100.</ref>

* BaCelLo [http://gpcr.biocomp.unibo.it/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.<ref>Pierleoni A., Martelli P.L., et al. (2006). "BaCelLo: a balanced subcellular localization predictor." Bioinformatics 22:e408-16.</ref>

* TargetP [http://www.cbs.dtu.dk/services/TargetP/]: Prediction of N-terminal [[sorting signals]]. <ref>Emanuelsson, O., H. Nielsen, et al. (2000). "Predicting subcellular localization of proteins based on their N-terminal amino acid sequence." J Mol Biol 300:1005-16.</ref>

* SecretomeP [http://www.cbs.dtu.dk/services/SecretomeP]: Prediction of eukaryotic proteins that are secreted via a non-traditional secretory mechanism.<ref>Bendtsen J.D., L.J. Jensen, et al. (2004). "Feature-based prediction of non-classical and leaderless protein secretion." Protein Eng Des Sel. 17:349-56.</ref>

* PredictNLS [http://rostlab.org/predictNLS/]: Prediction of [[nuclear localization signal]]s.<ref>Nair, R., P. Carter, et al. (2003). "NLSdb: database of nuclear localization signals." Nucleic Acids Res 31: 397-9.</ref>

* WoLF PSORT http://wolfpsort.org/: An updated version of PSORT/PSORT II for the prediction of eukaryotic sequences.<ref>Horton, P., K.-J. Park, T. Obayashi and K. Nakai (2006). "Protein Subcellular Localization Prediction with WoLF PSORT." Proceedings of Asian Pacific Bioinformatics Conference 2006, Taipei, Taiwan.</ref>

* PSORTb [http://www.psort.org/psortb/]: Prediction of bacterial protein localization.<ref>Gardy, J.L., C. Spencer, et al. (2003). "PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria." Nucleic Acids Res 31:3613-7.</ref> <ref>Gardy, J.L., M. Laird, F. Chen, S. Rey, C.J. Walsh, G.E. Tusnády, M. Ester, [[Fiona Brinkman|F.S.L. Brinkman]] (2005). "PSORT-B v.2.0: Expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis." Bioinformatics. 21:617-623.</ref> 

* Proteome Analyst [http://www.cs.ualberta.ca/%7Ebioinfo/PA/Sub/index.html]: Prediction of protein localization for both prokaryotes and eukaryotes using a text mining approach.<ref>Lu, Z., D. Szafron D, et al. (2004). "Predicting subcellular localization of proteins using machine-learned classifiers." Bioinformatics 20:547-56.</ref>

* CELLO [http://cello.life.nctu.edu.tw/]: CELLO uses a two-level Support Vector Machine system to assign localizations to both prokaryotic and eukaryotic proteins.<ref>Yu, C.S., Lin, C.J., Hwang, J.K. (2004). "Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions." Protein Sci 13:1402-6.</ref><ref>Yu, C.S., Chen, Y.C., Lu, C.H., Hwang, J.K. (2006). "Prediction of protein subcellular localization." Proteins 64:643-51.</ref>

== Relevance ==

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 [[drug discovery]] process. For example, [[secreted protein]]s and [[plasma membrane]] 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. 


== References == 

{{reflist|2}} 



== Further Reading == 

* Alberts, B., D. Bray, et al. (1994). Molecular Biology of the Cell. New York and London, Garland Publishing.

* Bork, P., T. Dandekar, et al. (1998). "Predicting function: from genes to genomes and back." J Mol Biol 283:707-25.

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

* Gardy, J.L., and [[Fiona Brinkman|F.S.L. Brinkman]] (2006). "Methods for predicting bacterial protein subcellular localization." Nature Reviews Microbiology 4:741-751.

* Schneider, G. and U. Fechner (2004). "Advances in the prediction of protein targeting signals" Proteomics 4:1571-1580.

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


==External Links==
* [http://www.psort.org/ PSORT.org] -  A portal for protein subcellular localization predictors
* [http://gpcr.biocomp.unibo.it/ BaCelLo] -  Balanced subCellular Localization predictor
 
* [[Protein Analysis Subcellular Localization Prediction|Protein Analysis Subcellular Localization Prediction]]

[[Category:Protein methods]]
[[Category:Cell biology]]