Complex Stability is Encoded in Binding Patch Softness: a Monomer-Based Approach to Predict Inter-Subunit Affinity of...

Area of Science:

• Structural biology
• Computational biophysics
• Protein-protein interactions

Background:

• Understanding protein-protein interactions is key to deciphering protein system behavior.
• Predicting the stability and affinity of protein complexes is a significant challenge in structural biology.
• Existing computational methods often require 3D structures of both subunits to predict dimer affinity.

Purpose of the Study:

• To develop a novel computational approach for predicting protein complex stability.
• To classify protein complexes into distinct affinity classes based on mechanical properties.
• To introduce the SEPAS software for predicting protein-protein interaction affinity using a single subunit's structure.

Main Methods:

• Classifying protein complexes with experimentally measured affinities into distinct classes.
• Predicting the mechanical stiffness of the protein binding patch (PBP) on a single protein subunit.
• Utilizing the computed mechanical stiffness of the PBP to predict the affinity class of the protein complex.

Main Results:

• Successfully predicted the affinity class of protein binding patches by analyzing mechanical stiffness.
• Developed the SEPAS software for predicting protein-protein interaction stability.
• Achieved a minimum accuracy of 0.72 in predicting dimer stability classwise.

Conclusions:

• The mechanical stiffness of a protein binding patch on a single subunit can predict protein complex stability.
• SEPAS software offers a simplified method for predicting protein-protein interaction affinity.
• The approach enables classwise prediction of experimentally measured affinities using only one subunit's 3D structure.