Profile-based short linear protein motif discovery

Niall J Haslam,Denis C Shields

doi:10.1186/1471-2105-13-104

Abstract

BackgroundShort 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.ResultsThe 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.ConclusionsProfile-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.

Highlights

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
It is clear that the MEME default is not as efficient as SLiMFinder at recovering known motifs
After the inclusion of both evolutionary weighting and masking out nonconserved residues, the performance of both methods are approximately equivalent. They don’t give identical results: SLiMFinder returns 3 motifs that MEME with weighting and masking fails to identify in the top ten motifs, namely SH3, 14-3-3_1 and RB; the latter motif was identified by the default MEME programme but lost in the modified version

Summary

Introduction

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. We extend these approaches to profile-based methods, which provide a richer motif representation. Short linear motifs (SLiMs) are typically 3–10 residue stretches of a protein sequence, with two or more non-wildcard positions that independently mediate a range of functions. They may be involved in ligand binding, modification, targeting and cleavage [3], all of which are important in driving cell signaling [1,4]. The known repertoire of protein modules needs to be expanded to include smaller functional

Methods

Results

Conclusion