title: NetAcet: prediction of N-terminal acetylation sites.
abstract: We present here a neural network based method for prediction of N-terminal acetylation-by far the most abundant post-translational modification in eukaryotes. The method was developed on a yeast dataset for N-acetyltransferase A (NatA) acetylation, which is the type of N-acetylation for which most examples are known and for which orthologs have been found in several eukaryotes. We obtain correlation coefficients close to 0.7 on yeast data and a sensitivity up to 74% on mammalian data, suggesting that the method is valid for eukaryotic NatA orthologs.
authors.affiliation: Center for Biological Sequence Analysis, BioCentrum-DTU Building 208 Technical University of Denmark, DK-2800 Lyngby, Denmark.
authors.forename: Lars
authors.initials: L
authors.lastname: Kiemer
authors.forename: Jannick Dyrløv
authors.initials: JD
authors.lastname: Bendtsen
authors.forename: Nikolaj
authors.initials: N
authors.lastname: Blom
mesh_terms: D000107:Acetylation; D000123:Acetyltransferases / Q000737:chemistry* / Q000378:metabolism; D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry* / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D029701:Saccharomyces cerevisiae Proteins; D000123:Acetyltransferases