title: QSLiMFinder: improved short linear motif prediction using specific query protein data.
abstract: BACKGROUND
  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.
  
  
  METHODS
  All the tools and data used in this study, including QSLiMFinder and the SLiMBench benchmarking software, are freely available under a GNU license as part of SLiMSuite, at: http://bioware.soton.ac.uk.
authors: Centre for Biological Sciences, University of Southampton, Southampton, UK.
authors: Nicolas
authors: N
authors: Palopoli
authors: Centre for Biological Sciences, University of Southampton, Southampton, UK, Public Health England, London, UK.
authors: Kieren T
authors: KT
authors: Lythgow
authors: Centre for Biological Sciences, University of Southampton, Southampton, UK, Institute for Life Sciences, University of Southampton, Southampton, UK and School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia.
authors: Richard J
authors: RJ
authors: Edwards
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D006801:Humans; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software