Journal Article

Masking residues using context-specific evolutionary conservation significantly improves short linear motif discovery

Norman E. Davey, Denis C. Shields and Richard J. Edwards

in Bioinformatics

Volume 25, issue 4, pages 443-450
Published in print February 2009 | ISSN: 1367-4803
Published online January 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn664
Masking residues using context-specific evolutionary conservation significantly improves short linear motif discovery

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Motivation: Short linear motifs (SLiMs) are important mediators of protein–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.

Results: 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.

Availability: All data and tools in this article are available at http://bioware.ucd.ie/~slimdisc/slimfinder/conmasking/.

Contact: r.edwards@southampton.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6586 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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