The multiple-specificity landscape of modular peptide recognition domains
David Gfeller1, Frank Butty, Marta Wierzbicka
1Banting and Best Department of Medical Research, The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
Molecular Systems Biology
|April 29, 2011
Summary
Protein interaction domains exhibit surprising correlations between residue positions, revealing multiple binding specificities. This finding enhances our understanding of protein interactions and suggests new ways to encode specificity in signaling pathways.
Area of Science:
- Molecular Biology
- Biochemistry
- Structural Biology
Background:
- Modular protein domains are crucial for eukaryotic signaling pathways.
- Peptide recognition domains bind specific linear amino acid sequences, but binding contributions are often oversimplified.
- Current models assume independent residue contributions to binding specificity.
Purpose of the Study:
- To investigate correlations between residue positions in peptide recognition domains.
- To uncover novel binding modes and structural insights.
- To improve the accuracy of predicting protein interactions.
Main Methods:
- Analysis of large binding peptide datasets.
- Identification of significant residue position correlations in PDZ, SH3, and WW domain families.
- Structural prediction and experimental validation of binding modes.
Main Results:
- Identified widespread, significant correlations between residue positions in multiple domain families.
- Revealed extensive multiple binding specificities beyond current models.
- Predicted and validated a new binding mode for PDZ domains, exemplified by DLG1 PDZ1.
Conclusions:
- Protein recognition domains possess a rich and complex specificity landscape.
- Multiple binding specificities are common and provide deeper structural insights.
- This work suggests novel strategies for encoding specificity in protein interaction networks.
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