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Bioinformatics Advance Access published online on September 6, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti650
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 3, 2005
Revised August 26, 2005
Accepted August 26, 2005

Article

pTARGET: a new method for predicting protein subcellular localization in eukaryotes

Chittibabu Guda 1* and Shankar Subramaniam 2

1 Gen*NY*sis Center for Excellence in Cancer Genomics, University at Albany, State University of New York, One Discovery Drive, Rensselaer, NY 12144-3456, USA; Department of Epidemiology and Biostatistics, University at Albany, State University of New York, One University Place, Rensselaer, NY 12144, USA
2 San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; Department of Chemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; Department of Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA

* To whom correspondence should be addressed.
Chittibabu Guda, E-mail: cguda{at}albany.edu


  Abstract

Motivation: There is a scarcity of efficient computational methods for predicting protein subcellular localization in eukaryotes. Currently available methods are inadequate for genome-scale predictions with several limitations. Here, we present a new prediction method, pTARGET that can predict proteins targeted to 9 different subcellular locations in the eukaryotic animal species.

Results: The nine subcellular locations predicted by pTARGET include cytoplasm, endoplasmic reticulum, extracellular/secretory, golgi, lysosomes, mitochondria, nucleus, plasma membrane and peroxisomes. Predictions are based on the location-specific protein functional domains and the amino acid compositional differences across different subcellular locations. Overall, this method can predict 68-87% of the true positives at accuracy rates of 96-99%. Comparison of the prediction performance against PSORT showed that pTARGET prediction rates are higher by 11-60% in 6 of the 8 locations tested. Besides, pTARGET method is robust enough for genome-scale prediction of protein subcellular localizations since, it does not rely on the presence of signal or target peptides.

Availability: A public web server based on the pTARGET method is accessible at the URL http://bioinformatics.albany.edu/~ptarget. Datasets used for developing pTARGET can be downloaded from this web server. Source code will be available on request from the corresponding author.

Supplementary data: Accessible as online-only from the publisher.


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