Using the Semiempirical Quantum Mechanics in Improving the Molecular Docking: A Case Study with CDK2.

Area of Science:

• Computational Chemistry
• Structural Biology
• Drug Discovery

Background:

• Molecular docking is crucial for predicting ligand-protein interactions.
• Accurate geometry optimization of docked poses is essential for reliable results.
• Semiempirical quantum mechanics (SQM) methods offer a balance between accuracy and computational cost.

Purpose of the Study:

• To evaluate modified SQM methods for improving molecular docking.
• To compare the performance of AutoDock and AutoDock Vina using SQM.
• To assess the utility of different evaluation metrics for docking accuracy.

Main Methods:

• Geometry optimization of docked ligand poses using PM6, PM6-D3H4X, and PM7 SQM Hamiltonians.
• Docking ligands into human cyclin-dependent kinase 2 (CDK2) using AutoDock and AutoDock Vina.
• Analysis of results using symmetry-corrected heavy-atom RMSD and fraction of recovered ligand-protein contacts.

Main Results:

• The fraction of recovered contacts is a more reliable metric for structural similarity than RMSD.
• AutoDock outperformed AutoDock Vina in generating correct ligand poses and ranking.
• SQM optimization significantly improved docking accuracy and ligand-protein fit compared to crystal structures.

Conclusions:

• Modified SQM methods enhance molecular docking accuracy for CDK2.
• Ligand optimization at the SQM level leads to better pose prediction and reduced steric clashes.
• SQM-optimized structures provide a superior representation of ligand-protein interactions relevant to drug discovery.