Profile-based short linear protein motif discovery

BMC Bioinformatics

Niall J Haslam¹  Denis C Shields¹ 

[1] School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland

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

Others  :  1088266
DOI  :  10.1186/1471-2105-13-104

 received in 2011-12-14, accepted in 2012-04-04,  发布年份 2012

【 摘 要 】

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.

【 授权许可】

   
2012 Haslam and Shields; licensee BioMed Central Ltd.

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