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Published on: July 14, 2015
The SLiMDisc server: short, linear motif discovery in proteins
Norman E Davey1, Richard J Edwards, Denis C Shields
1UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.
Short, linear motifs (SLiMs) are crucial for protein interactions. SLiMDisc software identifies novel SLiMs by analyzing convergent evolution, aiding in the discovery of functional protein motifs.
Area of Science:
- Bioinformatics
- Computational Biology
- Molecular Biology
Background:
- Short, linear motifs (SLiMs) are vital for biological processes, especially protein-protein interactions.
- Identifying SLiMs computationally is challenging due to the need to distinguish convergent evolution from common ancestry.
Purpose of the Study:
- To introduce SLiMDisc, a web server for discovering convergently evolved Short, linear motifs (SLiMs).
- To provide a tool that corrects for common ancestry when identifying shared motifs.
Main Methods:
- The SLiMDisc server analyzes protein sequences to find overrepresented, convergently evolved motifs.
- It employs an information content-based scoring scheme and allows interactive masking of input sequences.
- Scoring incorporates evolutionary relationships using BLAST local alignments.
Main Results:
- SLiMDisc returns a ranked list of potentially novel SLiMs found in unmasked regions.
- Visualizations, including alignments and schematics, aid in understanding motif context and significance.
- Users can filter and re-rank motifs, and compare results with known SLiMs.
Conclusions:
- SLiMDisc offers an effective computational approach for discovering functionally relevant, convergently evolved Short, linear motifs (SLiMs).
- The tool enhances understanding of protein-protein interactions and biological regulation through motif discovery.
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