title: Computational approach for identification and characterization of GPI-anchored peptides in proteomics experiments.
abstract: Genes that encode glycosylphosphatidylinositol anchored proteins (GPI-APs) constitute an estimated 1-2% of eukaryote genomes. Current computational methods for the prediction of GPI-APs are sensitive and specific; however, the analysis of the processing site (omega- or omega-site) of GPI-APs is still challenging. Only 10% of the proteins that are annotated as GPI-APs have the omega-site experimentally verified. We describe an integrated computational and experimental proteomics approach for the identification and characterization of GPI-APs that provides the means to identify GPI-APs and the derived GPI-anchored peptides in LC-MS/MS data sets. The method takes advantage of sequence features of GPI-APs and the known core structure of the GPI-anchor. The first stage of the analysis encompasses LC-MS/MS based protein identification. The second stage involves prediction of the processing sites of the identified GPI-APs and prediction of the corresponding terminal tryptic peptides. The third stage calculates possible GPI structures on the peptides from stage two. The fourth stage calculates the scores by comparing the theoretical spectra of the predicted GPI-peptides against the observed MS/MS spectra. Automated identification of C-terminal GPI-peptides from porcine membrane dipeptidase, folate receptor and CD59 in complex LC-MS/MS data sets demonstrates the sensitivity and specificity of this integrated computational and experimental approach.
authors.affiliation: Department of Biochemistry and Molecular Biology, University of The Basque Country, Bilbao, Spain.
authors.forename: Miren J
authors.initials: MJ
authors.lastname: Omaetxebarria
authors.forename: Felix
authors.initials: F
authors.lastname: Elortza
authors.forename: Eva
authors.initials: E
authors.lastname: Rodríguez-Suárez
authors.forename: Kerman
authors.initials: K
authors.lastname: Aloria
authors.forename: Jesus M
authors.initials: JM
authors.lastname: Arizmendi
authors.forename: Ole N
authors.initials: ON
authors.lastname: Jensen
authors.forename: Rune
authors.initials: R
authors.lastname: Matthiesen
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D018957:CD59 Antigens / Q000378:metabolism; D002352:Carrier Proteins / Q000378:metabolism; D015815:Cell Adhesion Molecules / Q000737:chemistry*; D002853:Chromatography, Liquid; D019295:Computational Biology; D004150:Dipeptidases / Q000378:metabolism*; D058973:Folate Receptors, GPI-Anchored; D008969:Molecular Sequence Data; D010455:Peptides / Q000378:metabolism*; D021382:Protein Sorting Signals; D040901:Proteomics; D011956:Receptors, Cell Surface / Q000378:metabolism; D013552:Swine; D053719:Tandem Mass Spectrometry
chemical_list: D018957:CD59 Antigens; D002352:Carrier Proteins; D015815:Cell Adhesion Molecules; D058973:Folate Receptors, GPI-Anchored; D010455:Peptides; D021382:Protein Sorting Signals; D011956:Receptors, Cell Surface; D004150:Dipeptidases; C038937:dipeptidase