title: PSLpred: prediction of subcellular localization of bacterial proteins.
abstract: CONCLUSIONS
  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 approximately 74% of sequences with an average prediction accuracy of 98% at RI = 5.
  
  
  BACKGROUND
  PSLpred is available at http://www.imtech.res.in/raghava/pslpred/
authors.affiliation: Institute of Microbial Technology, Sector 39A, Chandigarh, India.
authors.forename: Manoj
authors.initials: M
authors.lastname: Bhasin
authors.forename: Aarti
authors.initials: A
authors.lastname: Garg
authors.forename: G P S
authors.initials: GP
authors.lastname: Raghava
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000145:classification* / Q000378:metabolism*; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D020407:Internet; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D013347:Subcellular Fractions / Q000378:metabolism*
chemical_list: D001426:Bacterial Proteins