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.
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Date: 18-May-2012
Online since: 2012-09-05T15:37:09Z
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
Journal: BMC Bioinformatics
Volume: 13
Issue: 104
Copyright (published version): 2012 Haslam and Shields
Keywords: ProfileLinear-motifSlim
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
CASL Research Collection
Medicine Research Collection

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