On the binding affinity of macromolecular interactions: daring to ask why proteins interact

Area of Science:

• Biochemistry and Molecular Biology
• Structural Biology
• Computational Biology

Background:

• Protein-protein interactions are crucial for cellular functions and are quantified by binding affinity, measured as the dissociation constant (Kd).
• Accurate prediction of binding affinity from protein structures is vital for drug development but remains a significant challenge.
• Existing computational methods are limited in predicting binding affinity solely from structural coordinates, especially for complex systems.

Purpose of the Study:

• To examine the theoretical and experimental limitations in deriving structure-affinity relationships for protein-protein interactions.
• To highlight the shortcomings of current approaches in predicting binding affinity from protein structures.
• To propose future directions for modeling protein-protein recognition.

Main Methods:

• Review of existing theoretical and experimental approaches for predicting protein-protein binding affinity.
• Analysis of the relationship between structural features (e.g., buried surface area) and binding affinity.
• Discussion of limitations related to complex and flexible protein interactions.

Main Results:

• Current methods fail to accurately predict binding affinity for protein-protein complexes using only structural data.
• The buried surface area is a relevant measure for rigid complexes but is insufficient for flexible ones.
• Flexible complexes likely involve significant entropic contributions that are difficult to approximate.

Conclusions:

• Deriving accurate structure-affinity relationships for protein-protein interactions faces fundamental theoretical and experimental hurdles.
• Future theoretical modeling must adopt an integrative approach, incorporating biological, chemical, and physical principles.
• Understanding and predicting binding affinity in complex biological systems requires a multidisciplinary perspective.