title: Feature-based prediction of non-classical and leaderless protein secretion.
abstract: We present a sequence-based method, SecretomeP, for the prediction of mammalian secretory proteins targeted to the non-classical secretory pathway, i.e. proteins without an N-terminal signal peptide. So far only a limited number of proteins have been shown experimentally to enter the non-classical secretory pathway. These are mainly fibroblast growth factors, interleukins and galectins found in the extracellular matrix. We have discovered that certain pathway-independent features are shared among secreted proteins. The method presented here is also capable of predicting (signal peptide-containing) secretory proteins where only the mature part of the protein has been annotated or cases where the signal peptide remains uncleaved. By scanning the entire human proteome we identified new proteins potentially undergoing non-classical secretion. Predictions can be made at http://www.cbs.dtu.dk/services/SecretomeP.
authors.affiliation: Center for Biological Sequence Analysis BioCentrum-DTU, Building 208, Technical University of Denmark, DK-2800 Lyngby, Denmark.
authors.forename: Jannick Dyrløv
authors.initials: JD
authors.lastname: Bendtsen
authors.forename: Lars Juhl
authors.initials: LJ
authors.lastname: Jensen
authors.forename: Nikolaj
authors.initials: N
authors.lastname: Blom
authors.forename: Gunnar
authors.initials: G
authors.lastname: Von Heijne
authors.forename: Søren
authors.initials: S
authors.lastname: Brunak
mesh_terms: D020816:Amino Acid Motifs; D006801:Humans; D016571:Neural Networks, Computer; D011506:Proteins / Q000378:metabolism*; D020543:Proteome
chemical_list: D011506:Proteins; D020543:Proteome