A predicted structural interactome reveals binding interference from intrinsically disordered regions
1Laboratory of Evolutionary Genetics and Genomics, The Rockefeller University, New York, NY 10065, USA.
Biorxiv : the Preprint Server for Biology
|September 2, 2025
Summary
This study predicts protein-protein interactions in Drosophila using AlphaFold2, revealing that functional data and disordered regions are key to accurate predictions. An interactive web interface is provided for further research.
Area of Science:
- Molecular Biology
- Structural Biology
- Bioinformatics
Background:
- Cellular processes rely on complex protein-protein interaction networks.
- Understanding these interactions is crucial, yet many remain poorly characterized, especially in non-mammalian species like Drosophila.
- Deep learning advances offer new avenues for predicting molecular interactions.
Purpose of the Study:
- To predict protein-protein interactions in Drosophila using AlphaFold2 multimer.
- To investigate the contribution of physical and functional datasets to prediction accuracy.
- To analyze the role of intrinsically disordered regions in high-confidence interactions.
Main Methods:
- Utilized AlphaFold2 multimer for predicting protein-protein interactions.
- Integrated both physical and functional association datasets for Drosophila.
- Performed detailed structural analysis of predicted interactions.
Main Results:
- Functional associations significantly improved the confidence of predicted protein-protein interactions.
- Intrinsically disordered regions were identified as important in high-confidence predicted interactions.
- An interactive web interface was developed to present interaction predictions.
Conclusions:
- Functional data integration enhances the accuracy of physical protein-protein interaction predictions.
- Disordered regions play a critical role in mediating protein-protein interactions.
- The developed interface facilitates further research into Drosophila protein interactions.
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