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Bioinformatics Advance Access published online on October 31, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm524
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A computational strategy for the prediction of functional linear peptide motifs in proteins

Holger Dinkel 1 and Heinrich Sticht 1,*

1Abteilung für Bioinformatik, Institut für Biochemie, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany

*To whom correspondence should be addressed. Prof. Heinrich Sticht, E-mail: h.sticht{at}biochem.uni-erlangen.de


  Abstract

Motivation: Short linear peptide motifs mediate protein-protein interaction, cell compartment targeting and represent the sites of post-translational modification. The identification of functional motifs by conventional sequence searches, however, is hampered by the short length of the motifs resulting in a large number of hits of which only a small portion is functional.

Results: We have developed a procedure for the identification of functional motifs, which scores pattern conservation in homologous sequences by taking explicitly into account the sequence similarity to the query sequence. For a further improvement of this method, sequence filters have been optimized to mask those sequence regions containing little or no linear motifs. The performance of this approach was verified by measuring its ability to identify 576 experimentally validated motifs among a total of 15563 instances in a set of 415 protein sequences. Compared to a random selection procedure, the joint application of sequence filters and the novel scoring scheme resulted in a nine fold enrichment of validated functional motifs on the first rank. In addition, only half as many hits need to be investigated to recover 75% of the functional instances in our dataset. Therefore, this motif scoring approach should be helpful to guide experiments because it allows focusing on those short linear peptide motifs which have a high probability to be functional.

Contact: h.sticht{at}biochem.uni-erlangen.de

Supplementary information: Supplementary data is available at Bioinformatics online

Associate Editor: Dr. Alex Bateman

Received on August 10, 2007; revised on October 8, 2007; accepted on October 12, 2007

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