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SLiMAn: An Integrative Web Server for Exploring Short Linear Motif-Mediated Interactions in Interactomes
1Centre de Biologie Structurale, CNRS, INSERM, Univ. Montpellier, Montpellier 34090, France.
Journal of Proteome Research
|June 1, 2022
Summary
Analyzing protein-protein interactions is crucial for understanding cell function. The SLiMAn web server aids in identifying Short Linear Motifs (SLiMs) and their recognition domains, improving interactomics data analysis.
Area of Science:
- Molecular Biology
- Bioinformatics
- Systems Biology
Background:
- Cells utilize complex networks of intermolecular interactions for function.
- Protein-protein interactions are fundamental, often involving domains and Short Linear Motifs (SLiMs).
- Detecting SLiMs is challenging due to their small size, low conservation, and disordered nature.
Purpose of the Study:
- To introduce SLiMAn, a novel web server for analyzing interactomics data.
- To facilitate the identification and analysis of Short Linear Motifs (SLiMs) and their cognate domains.
- To aid in the high-throughput analysis of protein-protein interaction data.
Main Methods:
- SLiMAn extracts known SLiMs (from ELM) and SLiM-recognition domains (from Pfam) from user-provided interactant lists.
- Predicted SLiM-domain pairings are displayed and can be filtered using motif E-values, disorder scores (IUPred2), or interaction databases (BioGRID).
- Structural modeling of SLiM-domain complexes is possible when structural templates are available, using SCWRL.
Main Results:
- The SLiMAn server successfully identifies potential SLiM-domain interactions within interactomics datasets.
- Filtering options allow for refined analysis and prioritization of putative interactions.
- The tool's utility is demonstrated through various examples, including a real-case study.
Conclusions:
- SLiMAn provides a valuable resource for the comprehensive analysis of protein-protein interactions.
- The web server supports large-scale interactomics studies by simplifying SLiM and domain analysis.
- SLiMAn is freely accessible, promoting wider research in molecular interaction networks.
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