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Bioinformatics Advance Access published online on February 4, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti309
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received November 5, 2004
Revised February 2, 2005
Accepted February 3, 2005

Applications note

PSLpred: prediction of subcellular localization of bacterial proteins

Manoj Bhasin 1, Aarti Garg 1, and G. P. S. Raghava 1*

1 Institute of Microbial Technology, Sector - 39A, Chandigarh, India

* To whom correspondence should be addressed.
G. P. S. Raghava, E-mail: raghava{at}imtech.res.in


  Abstract

Summary: We developed a web server PSLpred for predicting subcellular localization of gram-negative bacterial proteins with an overall accuracy of 91.2%. PSLpred is a hybrid approach-based method that integrates PSI-BLAST and three SVM modules based on compositions of residues, dipeptides and physico-chemical properties. The prediction accuracies of 90.7, 86.8, 90.3, 95.2, and 90.6% were attained for cytoplasmic, extracellular, inner-membrane, outer-membrane and periplasmic proteins, respectively. Furthermore, PSLpred were able to predict ~74% of sequences with an average prediction accuracy of 98% at RI=5.

Availability: PSLpred is available at www.imtech.res.in/raghava/pslpred/.

Supplementary Information: http://www.imtech.res.in/raghava/pslpred/supl.html.


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