ArticleBMC bioinformatics2012
Profile-based short linear protein motif discovery.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A simple workflow to identify novel small linear motif (SLiM)-mediated interactions with AlphaFold.Briefings in bioinformatics · 2025Article
- The predictive performance of short-linear motif features in the prediction of calmodulin-binding proteins.BMC bioinformatics · 2018Article
- Large-scale protein function prediction using heterogeneous ensembles.F1000Research · 2018Article
- Article
- MFSPSSMpred: identifying short disorder-to-order binding regions in disordered proteins based on contextual local evolutionary conservation.BMC bioinformatics · 2013Article
- Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.PloS one · 2013Article
- SeqNLS: nuclear localization signal prediction based on frequent pattern mining and linear motif scoring.PloS one · 2013Article
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Authors and funding
2 authors.
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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.
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