Profile-based short linear protein motif discovery
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|Title:||Profile-based short linear protein motif discovery||Authors:||Haslam, Niall J.
Shields, Denis C.
|Permanent link:||http://hdl.handle.net/10197/3789||Date:||18-May-2012||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.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||BioMed Central||Copyright (published version):||2012 Haslam and Shields||Keywords:||Profile;Linear-motif;Slim||Subject LCSH:||Protein-protein interactions
|DOI:||10.1186/1471-2105-13-104||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Conway Institute Research Collection|
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