Predicting post-translational lysine acetylation using support vector machines

Florian Gnad; Shubin Ren; Chunaram Choudhary; Jürgen Cox; Matthias Mann

doi:10.1093/bioinformatics/btq260

Predicting post-translational lysine acetylation using support vector machines

Florian Gnad, Shubin Ren, Chunaram Choudhary, Jürgen Cox, Matthias Mann

The NNF Center for Protein Research

53 Citationer (Scopus)

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: [email protected].

Originalsprog	Engelsk
Tidsskrift	Bioinformatics
Vol/bind	26
Udgave nummer	13
Sider (fra-til)	1666-8
Antal sider	3
ISSN	1367-4803
DOI	https://doi.org/10.1093/bioinformatics/btq260
Status	Udgivet - 26 maj 2010

Adgang til dokumentet

10.1093/bioinformatics/btq260

Citationsformater

@article{f41f09072a6b4ee895507ddef62f7662,

title = "Predicting post-translational lysine acetylation using support vector machines",

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: [email protected].",

keywords = "Acetylation, Algorithms, Humans, Lysine, Protein Processing, Post-Translational, Proteins",

author = "Florian Gnad and Shubin Ren and Chunaram Choudhary and J{\"u}rgen Cox and Matthias Mann",

year = "2010",

month = may,

day = "26",

doi = "10.1093/bioinformatics/btq260",

language = "English",

volume = "26",

pages = "1666--8",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "13",

}

TY - JOUR

T1 - Predicting post-translational lysine acetylation using support vector machines

AU - Gnad, Florian

AU - Ren, Shubin

AU - Choudhary, Chunaram

AU - Cox, Jürgen

AU - Mann, Matthias

PY - 2010/5/26

Y1 - 2010/5/26

N2 - 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: [email protected].

AB - 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: [email protected].

KW - Acetylation

KW - Algorithms

KW - Humans

KW - Lysine

KW - Protein Processing, Post-Translational

KW - Proteins

U2 - 10.1093/bioinformatics/btq260

DO - 10.1093/bioinformatics/btq260

M3 - Journal article

C2 - 20505001

SN - 1367-4803

VL - 26

SP - 1666

EP - 1668

JO - Bioinformatics

JF - Bioinformatics

IS - 13

ER -

Predicting post-translational lysine acetylation using support vector machines

Abstract

Adgang til dokumentet

Fingeraftryk

Citationsformater