title: PredGPI: a GPI-anchor predictor.
abstract: BACKGROUND
  Several eukaryotic proteins associated to the extracellular leaflet of the plasma membrane carry a Glycosylphosphatidylinositol (GPI) anchor, which is linked to the C-terminal residue after a proteolytic cleavage occurring at the so called omega-site. Computational methods were developed to discriminate proteins that undergo this post-translational modification starting from their aminoacidic sequences. However more accurate methods are needed for a reliable annotation of whole proteomes.
  
  
  RESULTS
  Here we present PredGPI, a prediction method that, by coupling a Hidden Markov Model (HMM) and a Support Vector Machine (SVM), is able to efficiently predict both the presence of the GPI-anchor and the position of the omega-site. PredGPI is trained on a non-redundant dataset of experimentally characterized GPI-anchored proteins whose annotation was carefully checked in the literature.
  
  
  CONCLUSIONS
  PredGPI outperforms all the other previously described methods and is able to correctly replicate the results of previously published high-throughput experiments. PredGPI reaches a lower rate of false positive predictions with respect to other available methods and it is therefore a costless, rapid and accurate method for screening whole proteomes.
authors.affiliation: Biocomputing Group, Department of Biology, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy. andrea@biocomp.unibo.it
authors.forename: Andrea
authors.initials: A
authors.lastname: Pierleoni
authors.forename: Pier Luigi
authors.initials: PL
authors.lastname: Martelli
authors.forename: Rita
authors.initials: R
authors.lastname: Casadio
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D017261:Glycosylphosphatidylinositols / Q000737:chemistry*; D008956:Models, Chemical; D008969:Molecular Sequence Data; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D017261:Glycosylphosphatidylinositols