Multidimensional Global Optimization and Robustness Analysis in the Context of Protein-Ligand Binding

Area of Science:

• Computational chemistry
• Molecular modeling
• Biophysics

Background:

• Implicit solvent models are crucial for calculating protein-ligand binding free energies.
• Accuracy is highly dependent on dielectric boundary parameters, specifically atomic and water probe radii.
• Existing radii sets, optimized for small molecules, may not be optimal for protein-ligand systems.

Purpose of the Study:

• To develop and apply a global optimization pipeline for determining optimal atomic radii in implicit solvent models for protein-ligand binding.
• To improve the accuracy of electrostatic binding free energy calculations.
• To explore the energy landscape of solvation models using a novel connectivity approach.

Main Methods:

• A global multidimensional optimization pipeline combining a deterministic algorithm (VTDIRECT95) and a generalized Born model (GBNSR6).
• A novel robustness metric to distinguish between local and global minima.
• A graph-based 'kT-connectivity' approach for energy landscape visualization.

Main Results:

• Optimized radii (ρW=1.37 Å, ρC=1.40 Å, ρH=1.55 Å, ρN=2.35 Å, ρO=1.28 Å) were determined for atomic and water probe parameters.
• These optimized radii yielded improved agreement with explicit solvent reference calculations for electrostatic binding free energies.
• The study identified fundamental limitations of the two-dielectric implicit solvation model, with remaining discrepancies of ~4 kcal/mol.

Conclusions:

• The developed optimization pipeline effectively finds optimal atomic radii for implicit solvent models in protein-ligand binding.
• The optimized radii significantly enhance the accuracy of binding free energy predictions.
• The approach provides a pathway for further refinement of computational protocols in drug discovery and molecular simulations.