PSLpred: prediction of subcellular localization of bacterial proteins

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 was able to predict ∼74% of sequences with an average prediction accuracy of 98% at RI=5. Availability: PSLpred is available at http://www.imtech.res.in/raghava/pslpred/.