Quantifying binding stability by clustering conformations to enhance binding prediction accuracy.

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Predicting ligand-protein binding is a major hurdle in drug design.
• Current methods struggle to accurately incorporate bound-state dynamics, limiting prediction accuracy.
• The relationship between binding stability, affinity, and dynamics remains unclear.

Purpose of the Study:

• To introduce ShakeIt, a novel method for quantifying ligand-protein binding stability.
• To explore the role of dynamic conformational matching in ligand-protein recognition.
• To improve the accuracy of binding prediction and reduce false positives in drug discovery.

Main Methods:

• Developed ShakeIt, a portable, high-throughput method using molecular dynamics simulations and conformation clustering.
• Applied ShakeIt to the PDBbind-2020 dataset.
• Integrated ShakeIt stability scores into docking and free-energy calculations.

Main Results:

• Effective ligand-protein recognition relies on dynamic conformational matching.
• ShakeIt scores improved the ability to distinguish true ligands from decoys.
• Incorporating ShakeIt reduced false positives in binding predictions.
• Prospective screening identified two novel micromolar NMDA receptor antagonists.

Conclusions:

• ShakeIt provides an efficient way to leverage conformational dynamics for accurate binding prediction.
• The method accelerates drug discovery by improving ligand screening.
• Dynamic conformational matching is essential for successful ligand-protein interactions.