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Open Access Highly Accessed Methodology article

Profile-based short linear protein motif discovery

Niall J Haslam123 and Denis C Shields123*

Author Affiliations

1 Complex and Adaptive Systems Laboratory, University College Dublin, Dublin, Ireland

2 Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland

3 School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland

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BMC Bioinformatics 2012, 13:104  doi:10.1186/1471-2105-13-104

Published: 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.

Keywords:
Protein-protein interactions; Motif discovery; Peptide binding; Short linear motifs; Mini-motifs; SLiMs