Quantitatively Accounting for Protein Reorganization in Computer-Aided Drug Design.

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Structure-based drug design often assumes a single relevant protein conformation.
• Crystallographic data reveal that proteins can adopt multiple conformations.

Purpose of the Study:

• To develop a computational method for quantifying protein reorganization free energy.
• To enable accurate prediction of ligand binding free energies considering protein flexibility.

Main Methods:

• Developed a novel computational approach to calculate protein reorganization free energies.
• Applied the method to retrospective drug design cases involving Abl kinase and HSP90.

Main Results:

• The method successfully quantifies protein reorganization free energy.
• Demonstrated that alternative holo conformations can be leveraged to enhance ligand binding affinity and selectivity.
• Showcased significant affinity boosts in Abl kinase and HSP90 drug design examples.

Conclusions:

• Protein reorganization free energy is crucial for accurate binding free energy prediction.
• The developed computational method supports computer-aided drug design for complex protein targets by accounting for conformational flexibility.
• This approach facilitates the design of more potent and selective ligands by exploiting alternative protein conformations.