| Literature DB >> 15539450 |
Lars Kiemer1, Jannick Dyrløv Bendtsen, Nikolaj Blom.
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.Entities:
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Year: 2004 PMID: 15539450 DOI: 10.1093/bioinformatics/bti130
Source DB: PubMed Journal: Bioinformatics ISSN: 1367-4803 Impact factor: 6.937