Calculating Binding Free Energies in Model Host-Guest Systems with Unrestrained Advanced Sampling

Area of Science:

• Computational chemistry
• Molecular modeling
• Supramolecular chemistry

Background:

• Host-guest interactions are crucial for designing pharmaceuticals and soft materials due to their specific binding capabilities.
• Simulating these interactions at atomic scales often involves rare events, posing challenges for traditional molecular dynamics.
• Resolving these rare events is essential for obtaining accurate binding conformations and dissociation constants.

Purpose of the Study:

• To apply the adaptive biasing force (ABF) method to a coarse-grained host-guest model.
• To compute the potential of mean force and associated thermodynamic properties.
• To identify and address dynamic bottlenecks limiting simulation sampling.

Main Methods:

• Utilized adaptive biasing force (ABF) simulations.
• Employed a coarse-grained model of a rod-cavitand host-guest system.
• Calculated the potential of mean force and free energy landscape.

Main Results:

• ABF successfully enabled the computation of configurational and thermodynamic properties for bound and unbound states.
• The free energy landscape of the host-guest system was accurately determined.
• Key dynamic bottlenecks that hinder efficient sampling were identified.

Conclusions:

• Adaptive biasing force (ABF) is an effective method for studying host-guest interactions in molecular simulations.
• ABF facilitates the calculation of critical thermodynamic properties and the free energy landscape.
• Understanding dynamic bottlenecks is vital for optimizing sampling strategies in complex molecular systems.