title: Sequence-based feature prediction and annotation of proteins.
abstract: A recent trend in computational methods for annotation of protein function is that many prediction tools are combined in complex workflows and pipelines to facilitate the analysis of feature combinations, for example, the entire repertoire of kinase-binding motifs in the human proteome.
authors.affiliation: Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800 Lyngby, Denmark.
authors.forename: Agnieszka S
authors.initials: AS
authors.lastname: Juncker
authors.forename: Lars J
authors.initials: LJ
authors.lastname: Jensen
authors.forename: Andrea
authors.initials: A
authors.lastname: Pierleoni
authors.forename: Andreas
authors.initials: A
authors.lastname: Bernsel
authors.forename: Michael L
authors.initials: ML
authors.lastname: Tress
authors.forename: Peer
authors.initials: P
authors.lastname: Bork
authors.forename: Gunnar
authors.initials: G
authors.lastname: von Heijne
authors.forename: Alfonso
authors.initials: A
authors.lastname: Valencia
authors.forename: Christos A
authors.initials: CA
authors.lastname: Ouzounis
authors.forename: Rita
authors.initials: R
authors.lastname: Casadio
authors.forename: Søren
authors.initials: S
authors.lastname: Brunak
mesh_terms: D020816:Amino Acid Motifs; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D011494:Protein Kinases / Q000378:metabolism; D011506:Proteins / Q000378:metabolism / Q000502:physiology*; D020543:Proteome
chemical_list: D011506:Proteins; D020543:Proteome; D011494:Protein Kinases