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Updated: Aug 6, 2026

Identifying Protein-protein Interaction Sites Using Peptide Arrays
Published on: November 18, 2014
An Atlas of Short Linear Motif-Mediated Human Protein-Protein Interactions
Priyanka Madhu1, Caroline Benz1, Leandro Simonetti1
1Department of Chemistry for Life Sciences, Uppsala University, Husargatan 3, Box 576, 751 23 Uppsala, Sweden.
This study maps over 20,000 human protein-protein interactions mediated by short linear motifs (SLiMs) in disordered regions. The Atlas of SLiM-mediated Human protein-protein Interactions (ASHI) reveals novel binding activities and interaction mechanisms.
Area of Science:
- Proteomics
- Molecular Biology
- Bioinformatics
Background:
- Short linear motifs (SLiMs) in intrinsically disordered protein regions are vital for transient cellular interactions.
- The global interaction network of human SLiMs is largely unknown, hindering our understanding of cell physiology.
Purpose of the Study:
- To create a comprehensive map of human SLiM-mediated protein-protein interactions.
- To explore novel binding modes and activities of protein domains interacting with SLiMs.
- To investigate the mechanisms governing interaction specificity within disordered regions.
Main Methods:
- Screening over 800 human protein domains against a library of one million peptides.
- Utilizing high-throughput screening to identify SLiM-protein interactions.
- Developing the Atlas of SLiM-mediated Human protein-protein Interactions (ASHI) database.
Main Results:
- Mapped over 20,000 human protein-protein interactions involving SLiMs.
- Identified novel binding activities in enzymes, chaperones, RNA-binding proteins, and modification-reader domains.
- Revealed that disordered regions act as densely encoded interaction platforms with diverse specificity mechanisms.
Conclusions:
- The ASHI resource significantly expands the known SLiM interactome.
- Intrinsically disordered regions employ complex strategies for interaction specificity.
- This data provides a foundation for modeling dynamic networks, interpreting disease variants, and understanding the 'dark proteome'.
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