title: Profile-based short linear protein motif discovery.
abstract: BACKGROUND
  Short linear protein motifs are attracting increasing attention as functionally independent sites, typically 3-10 amino acids in length that are enriched in disordered regions of proteins. Multiple methods have recently been proposed to discover over-represented motifs within a set of proteins based on simple regular expressions. Here, we extend these approaches to profile-based methods, which provide a richer motif representation.
  
  
  RESULTS
  The profile motif discovery method MEME performed relatively poorly for motifs in disordered regions of proteins. However, when we applied evolutionary weighting to account for redundancy amongst homologous proteins, and masked out poorly conserved regions of disordered proteins, the performance of MEME is equivalent to that of regular expression methods. However, the two approaches returned different subsets within both a benchmark dataset, and a more realistic discovery dataset.
  
  
  CONCLUSIONS
  Profile-based motif discovery methods complement regular expression based methods. Whilst profile-based methods are computationally more intensive, they are likely to discover motifs currently overlooked by regular expression methods.
authors.affiliation: Complex and Adaptive Systems Laboratory, University College Dublin, Ireland.
authors.forename: Niall J
authors.initials: NJ
authors.lastname: Haslam
authors.forename: Denis C
authors.initials: DC
authors.lastname: Shields
mesh_terms: D020816:Amino Acid Motifs; D017124:Conserved Sequence; D030562:Databases, Protein; D006801:Humans; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins