Protein-ligand interaction energies with dispersion corrected density functional theory and high-level wave function...

Area of Science:

• Computational Chemistry
• Molecular Modeling
• Drug Discovery

Background:

• Protein-ligand interactions are central to pharmacology and drug design.
• Accurate calculation of binding energies is crucial for virtual screening and lead optimization.
• Traditional density functional theory (DFT) methods often struggle to accurately capture non-covalent interactions, particularly dispersion forces.

Purpose of the Study:

• To evaluate the performance of dispersion-corrected density functional theory (DFT-D3) for calculating intermolecular interaction energies in protein-ligand complexes.
• To benchmark DFT-D3 results against high-level wave function-based methods and other computational approaches.
• To assess the necessity of dispersion corrections for realistic modeling of protein-ligand binding.

Main Methods:

• Calculation of intermolecular interaction energies for protein-ligand complexes using DFT-D3 with various functionals (GGA, meta-GGA, hybrid).
• Benchmarking against estimated complete basis set (CBS) limits from local pair natural orbital coupled-electron pair approximation (LPNO-CEPA/1), MP2, and semiempirical methods.
• Inclusion of basis set effects using extended triple- to quadruple-ζ quality basis sets.

Main Results:

• DFT-D3 interaction energies show good correlation with the dispersion contribution, even in complexes with numerous hydrogen bonds.
• Dispersion correction significantly reduces the dependence of interaction energies on the choice of density functional.
• DFT-D3 results are highly consistent with LPNO-CEPA/1 and MP2, with average differences of 1-2 kcal/mol.

Conclusions:

• Accurate treatment of dispersion interactions is essential for realistic modeling of protein-ligand binding.
• DFT-D3 provides reliable and consistent results for protein-ligand interactions, comparable to high-level methods.
• DFT-D3 is proposed as a key component for QM/MM approaches in advanced virtual screening of protein-ligand interactions.