PSORT

CBP is predicted by PSORT analysis to be a cytoplasmic protein (Horton et al. 2007) and to contain a steroidogenic acute regulatory protein (StAR)-related lipid transfer (START) domain of 200 amino acids at its C-terminus (Figure 24.2a). 

Horton, P., Park, K. J., Obayashi, T. et al. 2007. WoLF PSORT Protein localization predictor, Nucleic Acids Res., 35(Web Server issue) W585-W587. 

Nakai and Horton, 1999). Note that most of the subprograms in PSORT II have not been upgraded from the original PSORT. New discoveries on sorting signals stated in this review should be incorporated. 

Prediction of mitochondrial targeting signals is not an easy task. The proposed amphiphilic nature is not clear enough. Nakai and Kanehisa (1992) developed a simple method based on the amino acid composition of the segment of most amino-terminal 20 residues. In addition, a simple rule to discriminate the bipartite signal of intermembrane-space proteins was also included in PSORT. 

Nakai, K., and Horton, P. (1999). PSORT a program for detecting the sorting signals of proteins and predicting their subcellular localization. Trends Biochem. Sci. 24,34—35. 

Protein PSORT http //psort.nibb.ac.jp/ Prediction of protein... 

PSORT GenomeNet Prediction of protein sorting signals based on amino acid composition... 

Ghen, Y., Yu, P., Luo, J., Jiang, Y. (2003). Secreted protein prediction system combining GJ-SPHMM, TMHMM, PSORT. Mamm. Genome 14, 859-865. 

The PSORT system [61] predicts the localization of proteins from gram-negative bacteria, gram-positive bacteria, yeasts, animals, and plants. For a query sequence the program calculates the values of feature variables that reflect various characteristics of the sequence (table 10.2). Next, it uses the k-nearest-neighbor algorithm to interpret the set of values obtained and estimates the likelihood of the protein being sorted to each candidate site. Finally, it displays some of the most probable sites. 

Gardy, J. L., C. Spencer, K. Wang, M. Ester, G. E. Tusnady, I. Simon, S. Hua, et al. 2003. PSORT-B Improving protein subcellular localization prediction for Gramnegative bacteria Nucleic Acids Res 31 3613-7. 

Harvester collects two types of information pure text-based information and information rich in graphical elements. Text-based information is retrieved and indexed from the following public databases and prediction servers UNIPROT, SOURCE, SMART, SOSUI, PSORT, RZPD, Homo-logene, gfp-cDNA, IPI, CD ART, STRING. For optimal search engine indexing, redundant text information is removed by server-specific converter modules. 

Data from computing-intensive prediction servers is precomputed, collected, and indexed (e.g., PSORT II [Nakai and Horton, 1999] or SMART [Letunic et al, 2004]). Harvester provides these data instantaneously and thus increases the speed of searches drastically compared to searching the respective databases manually one after the other. Pages older than 21 days are continuously updated. The quality of the collected information automatically increases with the quality of the crawled data servers, which themselves collect their information from several sub sources but quality-check them prior to publication (Apweiler et al, 2004 Diehn et al, 2003). 

The PSORT server (Nakai and Horton, 1999) contributes a prediction algorithm for the subcellular localizations for all known proteins, which is complemented by experimental data on the subcellular localization of GFP-tagged human cDNAs (Simpson and Pepperkok, 2003). 

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