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<pubDate>Fri, 25 Jul 2008 15:37:17 BST</pubDate>


	<title>CiteULike: neils artificial</title>
	<description>CiteULike: neils artificial</description>


	<link>http://www.citeulike.org/user/neils/tag/artificial</link>
	<dc:publisher>CiteULike.org</dc:publisher>
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	<dc:rights>Copyright &#169; 2004-2008 citeulike.org</dc:rights>
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        <rdf:li rdf:resource="http://www.citeulike.org/user/neils/article/2054436"/>
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<item rdf:about="http://www.citeulike.org/user/neils/article/2054436">
    <title>Prediction of phosphorylation sites using SVMs.</title>
    <link>http://www.citeulike.org/user/neils/article/2054436</link>
    <description>&lt;i&gt;Bioinformatics, Vol. 20, No. 17. (Nov 2004), pp. 3179-3184.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;MOTIVATION: Phosphorylation is involved in diverse signal transduction pathways. By predicting phosphorylation sites and their kinases from primary protein sequences, we can obtain much valuable information that can form the basis for further research. Using support vector machines, we attempted to predict phosphorylation sites and the type of kinase that acts at each site. RESULTS: Our prediction system was limited to phosphorylation sites catalyzed by four protein kinase families and four protein kinase groups. The accuracy of the predictions ranged from 83 to 95\% at the kinase family level, and 76-91\% at the kinase group level. The prediction system used-PredPhospho-can be applied to the functional study of proteins, and can help predict the changes in phosphorylation sites caused by amino acid variations at intra- and interspecies levels.</description>
    <dc:title>Prediction of phosphorylation sites using SVMs.</dc:title>

    <dc:creator>Jong Kim</dc:creator>
    <dc:creator>Juyoung Lee</dc:creator>
    <dc:creator>Bermseok Oh</dc:creator>
    <dc:creator>Kuchan Kimm</dc:creator>
    <dc:creator>Insong Koh</dc:creator>
    <dc:source>Bioinformatics, Vol. 20, No. 17. (Nov 2004), pp. 3179-3184.</dc:source>
    <dc:date>2007-12-04T03:22:10-00:00</dc:date>
    <prism:publicationYear>2004</prism:publicationYear>
    <prism:publicationName>Bioinformatics</prism:publicationName>
    <prism:volume>20</prism:volume>
    <prism:number>17</prism:number>
    <prism:startingPage>3179</prism:startingPage>
    <prism:endingPage>3184</prism:endingPage>
    <prism:category>algorithm</prism:category>
    <prism:category>alignment</prism:category>
    <prism:category>analysis</prism:category>
    <prism:category>article-predikin</prism:category>
    <prism:category>artificial</prism:category>
    <prism:category>binding</prism:category>
    <prism:category>chemical</prism:category>
    <prism:category>computer</prism:category>
    <prism:category>intelligence</prism:category>
    <prism:category>models</prism:category>
    <prism:category>molecular</prism:category>
    <prism:category>phosphorylation</prism:category>
    <prism:category>phosphotransferase</prism:category>
    <prism:category>protein</prism:category>
    <prism:category>relationship</prism:category>
    <prism:category>sequence</prism:category>
    <prism:category>simulation</prism:category>
    <prism:category>sites</prism:category>
    <prism:category>structure-activity</prism:category>
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<item rdf:about="http://www.citeulike.org/user/neils/article/2054413">
    <title>Scoring of predicted GRK2 phosphorylation sites in Nedd4-2.</title>
    <link>http://www.citeulike.org/user/neils/article/2054413</link>
    <description>&lt;i&gt;Bioinformatics, Vol. 22, No. 18. (Sep 2006), pp. 2192-2195.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;MOTIVATION: Epithelial Na(+) channels (ENaC) mediate the transport of sodium (Na) across epithelia in the kidney, gut and lungs and are required for blood pressure regulation. They are inhibited by ubiquitin protein ligases, such as Nedd4-2. These ligases bind to proline-rich motifs (PY motifs) present in the C-termini of ENaC subunits. Loss of this inhibition leads to hypertension. We have previously reported that ENaC channels are maintained in the active state by the G protein coupled receptor kinase, GRK2. The enzyme has been implicated in the development of essential hypertension [R. D. Feldman (2002) Mol. Pharmacol., 61, 707-709]. Additional findings in our lab pointed towards a possible role for GRK2 in the phosphorylation and inactivation of Nedd4-2. RESULTS: We have predicted GRK2 phosphorylation sites on Nedd4-2 by combining sequence analysis, homology modeling and surface accessibility calculations. A total of 24 potential phosphorylation sites were predicted by sequence analysis. Of these, 16 could be modeled using homology modeling and 6 of these were found to have sufficient surface exposure to be accessible to the GRK2 enzyme responsible for the phosphorylation of Nedd4-2. The method provides an ordered list of the most probable GRK2 phosphorylation sites on Nedd4-2 providing invaluable guidance to future experimental studies aimed at mutating certain Nedd4-2 residues in order to prevent phosphorylation by GRK2. The method developed could be applied in a wide variety of biological applications involving the binding of one molecule to a protein. The relative effectiveness of the technique is determined mainly by the quality of the homology model built for the protein of interest. Contact: jarthur@med.usyd.edu.au</description>
    <dc:title>Scoring of predicted GRK2 phosphorylation sites in Nedd4-2.</dc:title>

    <dc:creator>Jonathan Arthur</dc:creator>
    <dc:creator>Angeles Perez</dc:creator>
    <dc:creator>David Cook</dc:creator>
    <dc:source>Bioinformatics, Vol. 22, No. 18. (Sep 2006), pp. 2192-2195.</dc:source>
    <dc:date>2007-12-04T03:22:09-00:00</dc:date>
    <prism:publicationYear>2006</prism:publicationYear>
    <prism:publicationName>Bioinformatics</prism:publicationName>
    <prism:volume>22</prism:volume>
    <prism:number>18</prism:number>
    <prism:startingPage>2192</prism:startingPage>
    <prism:endingPage>2195</prism:endingPage>
    <prism:category>algorithm</prism:category>
    <prism:category>alignment</prism:category>
    <prism:category>amino-acid</prism:category>
    <prism:category>analysis</prism:category>
    <prism:category>article-predikin</prism:category>
    <prism:category>artificial</prism:category>
    <prism:category>beta-adrenergic</prism:category>
    <prism:category>binding</prism:category>
    <prism:category>chemical</prism:category>
    <prism:category>computer</prism:category>
    <prism:category>data</prism:category>
    <prism:category>g-protein-coupled</prism:category>
    <prism:category>homology</prism:category>
    <prism:category>intelligence</prism:category>
    <prism:category>interaction</prism:category>
    <prism:category>kinase</prism:category>
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    <prism:category>molecular</prism:category>
    <prism:category>phosphorylation</prism:category>
    <prism:category>protein</prism:category>
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    <prism:category>sequence</prism:category>
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