Binding Affinity Determination in Drug Design: Insights from Lock and Key, Induced Fit, Conformational Selection, and...

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Binding affinity quantifies molecule-target protein interaction strength, vital for therapeutic development.
• Existing protein-ligand recognition models (lock and key, induced fit, conformational selection) are incomplete.
• Current computational methods for predicting binding affinity yield unsatisfactory results.

Purpose of the Study:

• To explore the link between binding affinity and protein-ligand interaction models.
• To highlight the limitations of current models in explaining binding affinity.
• To introduce ligand trapping as a concept to address dissociation mechanisms.

Main Methods:

• Review of existing protein-ligand interaction models.
• Analysis of computational strategies for binding affinity prediction.
• Introduction and conceptualization of ligand trapping.

Main Results:

• Current models inadequately capture the full mechanism of binding affinity.
• Models focus on ligand binding but neglect ligand dissociation.
• Ligand trapping offers a mechanism for understanding dissociation.

Conclusions:

• A unified theoretical framework combining existing models with ligand trapping is proposed.
• This framework may enable accurate prediction of binding affinity.
• Addressing both binding and dissociation is key for improved drug design.