QSLiMFinder: Improved short linear motif prediction using specific query protein data

Nicolas Palopoli, Kieren T. Lythgow, Richard J. Edwards

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

Motivation: The sensitivity of de novo short linear motif (SLiM) prediction is limited by the number of patterns (the motif space) being assessed for enrichment. QSLiMFinder uses specific query protein information to restrict the motif space and thereby increase the sensitivity and specificity of predictions. Results: QSLiMFinder was extensively benchmarked using known SLiM-containing proteins and simulated protein interaction datasets of real human proteins. Exploiting prior knowledge of a query protein likely to be involved in a SLiM-mediated interaction increased the proportion of true positives correctly returned and reduced the proportion of datasets returning a false positive prediction. The biggest improvement was seen if a short region of the query protein flanking the interaction site was known.

Original languageEnglish
Pages (from-to)2284-2293
Number of pages10
JournalBioinformatics
Volume31
Issue number14
DOIs
Publication statusPublished - 15 Jul 2015
Externally publishedYes

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