%0 Journal Article
%T Profile-based short linear protein motif discovery
%A Niall J Haslam
%A Denis C Shields
%J BMC Bioinformatics
%D 2012
%I BioMed Central
%R 10.1186/1471-2105-13-104
%X 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.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.
%K Protein-protein interactions
%K Motif discovery
%K Peptide binding
%K Short linear motifs
%K Mini-motifs
%K SLiMs
%U http://www.biomedcentral.com/1471-2105/13/104/abstract