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Bioinformatics. 2010 Jul 1;26(13):1666-8. doi: 10.1093/bioinformatics/btq260. Epub 2010 May 26.

Predicting post-translational lysine acetylation using support vector machines.

Author information

1
Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany.

Abstract

MOTIVATION:

Lysine acetylation is a post-translational protein modification and a primary regulatory mechanism that controls many cell signaling processes. Lysine acetylation sites are recognized by acetyltransferases and deacetylases through sequence patterns (motifs). Recently, we used high-resolution mass spectrometry to identify 3600 lysine acetylation sites on 1750 human proteins covering most of the previously annotated sites and providing the most comprehensive acetylome so far. This dataset should provide an excellent source to train support vector machines (SVMs) allowing the high accuracy in silico prediction of acetylated lysine residues.

RESULTS:

We developed a SVM to predict acetylated residues. The precision of our acetylation site predictor is 78% at 78% recall on input data containing equal numbers of modified and non-modified residues.

AVAILABILITY:

The online predictor is available at http://www.phosida.com

CONTACT:

mmann@biochem.mpg.de

PMID:
20505001
PMCID:
PMC2887055
DOI:
10.1093/bioinformatics/btq260
[Indexed for MEDLINE]
Free PMC Article

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