NetAcet: Prediction of N-terminal acetylation sites.
Lars Kiemer, Jannick Dyrløv Bendtsen and Nikolaj Blom.
Bioinformatics, 21(7):1269-70, 2005.
Center for Biological Sequence Analysis, BioCentrum-DTU,
The Technical University of Denmark, DK-2800 Lyngby, Denmark
We present here a neural network based method for prediction of
amino-terminal acetylation - by far the most abundant
post-translational modification in eukaryotes. The method was
developed on a yeast data set 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.