Predicting post-translational lysine acetylation using support vector machines
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: mmannbiochem.mpg.de.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2010 |
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| Erschienen: |
2010 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:26 |
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| Enthalten in: |
Bioinformatics (Oxford, England) - 26(2010), 13 vom: 01. Juli, Seite 1666-8 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gnad, Florian [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 26.08.2010 Date Revised 20.10.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1093/bioinformatics/btq260 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM198417799 |
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| 245 | 1 | 0 | |a Predicting post-translational lysine acetylation using support vector machines |
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| 500 | |a Date Completed 26.08.2010 | ||
| 500 | |a Date Revised 20.10.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a AVAILABILITY: The online predictor is available at http://www.phosida.com | ||
| 520 | |a CONTACT: mmannbiochem.mpg.de | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
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| 650 | 7 | |a Lysine |2 NLM | |
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| 700 | 1 | |a Ren, Shubin |e verfasserin |4 aut | |
| 700 | 1 | |a Choudhary, Chunaram |e verfasserin |4 aut | |
| 700 | 1 | |a Cox, Jürgen |e verfasserin |4 aut | |
| 700 | 1 | |a Mann, Matthias |e verfasserin |4 aut | |
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