The SAMPL5 host-guest challenge: computing binding free energies and enthalpies from explicit solvent simulations by...

Area of Science:

• Computational Chemistry
• Molecular Modeling
• Supramolecular Chemistry

Background:

• The Attach-Pull-Release (APR) method has demonstrated success in retrospective studies for cucurbit[7]uril (CB7) and β-cyclodextrin (βCD) systems.
• Accurate prediction of binding thermodynamics is crucial for drug discovery and development.

Purpose of the Study:

• To compute absolute binding free energies and enthalpies for twelve host-guest systems in the SAMPL5 blind challenge using the APR approach.
• To evaluate the performance of the APR method with TIP3P and OPC water models for host octa acid (OA) and tetra-endo-methyl octa-acid (TEMOA) systems.
• To analyze binding poses and enthalpy-entropy compensation trends.

Main Methods:

• Application of the Attach-Pull-Release (APR) computational method.
• Utilized TIP3P and the OPC water models for simulations.
• Performed molecular dynamics (MD) simulations for 750 ns to analyze binding poses.
• Clustering analysis of MD trajectories to identify populated binding poses.

Main Results:

• Computed binding free energies for OA and TEMOA systems showed good agreement with prospective experimental data (R²=0.8, RMSE=1.7 kcal/mol) using TIP3P water.
• Binding enthalpy calculations demonstrated moderate correlations (R²=0.5, RMSE=2.5 kcal/mol) with TIP3P water.
• The OPC water model also yielded good performance.
• The study successfully captured experimental enthalpy-entropy compensation trends and predicted strong/weak binding guests.

Conclusions:

• Computational methods like APR, employing MD simulations and explicit solvent models within a statistical thermodynamic framework, can provide reliable predictions of binding thermodynamics.
• The APR approach shows significant potential for applications in hit identification and lead optimization within the drug discovery pipeline.
• Continued advancements in simulation force fields will further enhance the predictive power of these computational techniques.