Structural Stability Predicts the Binding Mode of Protein-Ligand Complexes.

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Predicting ligand binding modes is crucial for structure-based drug discovery.
• Current methods primarily focus on binding free energy, with limited success.
• A need exists for alternative approaches to improve binding mode prediction accuracy.

Purpose of the Study:

• To develop and evaluate a novel computational workflow for predicting ligand binding modes.
• To explore structural stability as a criterion for binding mode prediction.
• To compare the performance of this new method against traditional docking scoring functions.

Main Methods:

• A novel computational workflow was implemented.
• The workflow quantifies the steepness of the local energy minimum for potential binding modes.
• Structural stability was used as the primary prediction criterion, replacing binding free energy.

Main Results:

• The novel protocol outperforms standard docking scoring functions for fragment-like molecules (MW < 300 Da).
• The method achieves performance comparable to docking for drug-like molecules.
• Specific substructures acting as structural anchors were accurately identified, improving binding mode prediction.

Conclusions:

• Focusing on structural stability offers a new physical perspective for binding mode prediction.
• This approach provides an effective alternative or complement to thermodynamic-based methods.
• The findings enhance the accuracy of predicting ligand-macromolecular interactions in drug discovery.