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<XML><RECORDS>
<RECORD>
	<REFERENCE_TYPE>0</REFERENCE_TYPE>
	<AUTHORS>
		<AUTHOR>Davey N.E.*</AUTHOR>
		<AUTHOR>Shields D.C.</AUTHOR>
		<AUTHOR>Edwards R.J.*</AUTHOR>
	</AUTHORS>
	<YEAR>2009</YEAR>
	<TITLE>Masking residues using context-specific evolutionary conservation significantly improves short linear motif discovery**</TITLE>
	<SECONDARY_TITLE>Bioinformatics</SECONDARY_TITLE>
	<PUBLISHER>EPUB</PUBLISHER>
	<VOLUME>25</VOLUME>
	<NUMBER>4</NUMBER>
	<PAGES>443-50</PAGES>
	<DATE>01/2009</DATE>
	<ABSTRACT>&lt;p id=&quot;p-2&quot;&gt;&lt;strong&gt;Motivation:&lt;/strong&gt; Short linear motifs (SLiMs) are  important mediators of protein&amp;ndash;protein interactions. Their short and  degenerate nature presents                      a challenge for computational discovery. We sought  to improve SLiM discovery by incorporating evolutionary information,  since                      SLiMs are more conserved than surrounding residues.&lt;/p&gt;
&lt;p id=&quot;p-3&quot;&gt;&lt;strong&gt;Results:&lt;/strong&gt; We have  developed a new method that assesses the evolutionary signal of a  residue in its sequence and structural context.                      Under-conserved residues are masked out prior to  SLiM discovery, allowing incorporation into the existing statistical  model                      employed by SLiMFinder. The method shows  considerable robustness in terms of both the conservation score used for  individual                      residues and the size of the sequence  neighbourhood. Optimal parameters significantly improve return of known  functional motifs                      from benchmarking data, raising the return of  significant validated SLiMs from typical human interaction datasets from  20%                      to 60%, while retaining the high level of  stringency needed for application to real biological data. The success  of this regime                      indicates that it could be of general benefit to  computational annotation and prediction of protein function at the  sequence                      level.&lt;/p&gt;</ABSTRACT>
	<NOTES><p>* Non-Clique Members<br />
** Partially funded by Clique</p></NOTES>
	<URL>http://bioinformatics.oxfordjournals.org/content/25/4/443.abstract</URL>
</RECORD>
</RECORDS></XML>
