Computational prediction of subcellular localization.
It is widely recognized that much of the information for determining the final subcellular localization of proteins is found in their amino acid sequences. Thus the prediction of protein localization sites is of both theoretical and practical interest. In most cases, the prediction has been attempted in two ways: one is based on the knowledge of experimentally characterized targeting signals, while the other utilizes the statistical differences of general sequence characteristics, such as amino acid composition, between localization sites. Both approaches have limitations, and it is recommended to check the results of various prediction methods based on different principles as well as training data. Recently, increased proteomic analyses of localization sites have provided new data to assess the current status of predictive methods. In this chapter we discuss these issues and close with an example illustrating the use of the WoLF PSORT web server for localization prediction.
Nakai
Kenta
K
Laboratory of Functional Analysis in silico, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan.
Horton
Paul
P
D000595:Amino Acid Sequence; D001426:Bacterial Proteins / Q000032:analysis / Q000737:chemistry; D002736:Chloroplasts / Q000737:chemistry / Q000378:metabolism; D019295:Computational Biology; D030562:Databases, Protein; D008565:Membrane Proteins / Q000032:analysis / Q000737:chemistry; D020675:Peroxisomes / Q000737:chemistry / Q000378:metabolism; D011237:Predictive Value of Tests; D021382:Protein Sorting Signals; D011506:Proteins / Q000032:analysis* / Q000737:chemistry; D020543:Proteome / Q000032:analysis; D040901:Proteomics; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D013347:Subcellular Fractions / Q000378:metabolism
D001426:Bacterial Proteins; D008565:Membrane Proteins; D021382:Protein Sorting Signals; D011506:Proteins; D020543:Proteome