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Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

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Updated: Jun 2, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Improved protein binder design using β-pairing targeted RFdiffusion.

Isaac Sappington^1,2,3, Martin Toul^4,5, David S Lee^2,6

¹Department of Biochemistry, University of Washington, Seattle, WA, USA.

Nature Communications

|January 10, 2026

Summary

This summary is machine-generated.

This study introduces a new computational method using RFdiffusion to design proteins that bind strongly to specific targets. The approach successfully generated high-affinity binders for several proteins, improving upon previous methods.

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Area of Science:

Protein engineering
Computational biology
Structural biology

Background:

Designing high-affinity protein binders for hydrophilic targets is difficult.
Existing computational methods have limitations in targeting specific protein surfaces.

Purpose of the Study:

To develop and validate a conditioned RFdiffusion approach for designing protein binders.
To generate binders targeting edge-strand sites on various proteins, including KIT, PDGFRɑ, ALK-2, ALK-3, FCRL5, NRP1, and α-CTX.

Main Methods:

Utilized conditioned RFdiffusion to generate protein scaffolds with geometrically matched extended β-sheets.
Complemented polar groups on target proteins with hydrogen bonding groups on designed binders.
Tested designed binders against multiple protein targets.

Main Results:

Achieved higher binding affinities (pM to mid nM) and success rates compared to unconditioned RFdiffusion.
Demonstrated high specificity of designed binders due to precise geometric customization and additional interactions.
Validated the design approach with a co-crystal structure of a binder-KIT complex, showing high structural accuracy.

Conclusions:

Conditioned RFdiffusion enables robust generation of binders for hydrophilic, exposed β-strand surfaces.
This method significantly expands the scope of computational protein binder design.
The precise customization of binder-target interactions leads to high affinity and specificity.