NetAcet: prediction of N-terminal acetylation sites

Lars Kiemer, Jannick Dyrløv Bendtsen, Nikolaj Blom

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    Summary: 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.
    Original languageEnglish
    JournalBioinformatics
    Volume21
    Issue number7
    Pages (from-to)1269-1270
    ISSN1367-4803
    Publication statusPublished - 2005

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