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SLiMDisc: short, linear motif discovery, correcting for common evolutionary descent
Norman E Davey1, Denis C Shields, Richard J Edwards
1Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin 4, Ireland.
Nucleic Acids Research
|July 21, 2006
Summary
This study introduces a novel method to identify functional protein motifs by distinguishing convergent evolution from shared ancestry. The approach effectively filters out motifs inherited from common ancestors, improving motif discovery accuracy.
Area of Science:
- Bioinformatics
- Computational Biology
- Molecular Biology
Background:
- Short, linear motifs (SLiMs) mediate crucial protein interactions.
- Discovering functional SLiMs is challenging due to shared motifs from common evolutionary descent.
Purpose of the Study:
- To develop robust computational methods for identifying functional protein motifs.
- To differentiate between motifs evolved by convergence and those inherited identically by descent.
Main Methods:
- Utilized the TEIRESIAS algorithm for motif prediction in protein sets.
- Normalized motif occurrences to account for evolutionary relatedness using BLAST alignments.
- Ranked motifs by a score combining normalized occurrences and information content.
Main Results:
- The developed method significantly outperformed existing approaches that do not account for evolutionary relatedness.
- Demonstrated superior performance on known SLiMs from the Eukaryotic Linear Motif (ELM) database.
- Multiple Spanning Tree weighting proved effective across various settings.
Conclusions:
- The new method provides a more accurate way to discover functional SLiMs by correcting for shared ancestry.
- This advancement aids in understanding protein function and interaction networks.
- The approach is valuable for motif discovery in biological sequence analysis.