SAMPL6 host-guest binding affinities and binding poses from spherical-coordinates-biased simulations.

Area of Science:

• Computational chemistry
• Molecular modeling
• Biophysical simulations

Background:

• Host-guest binding affinity prediction is crucial for drug discovery and molecular recognition.
• Accurate computational methods are needed to assess protein-ligand interactions, as highlighted by the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges.
• Existing simulation methods can suffer from initial configuration bias and incomplete exploration of binding poses.

Purpose of the Study:

• To apply a volume-based variant of well-tempered metadynamics for calculating host-guest binding affinities.
• To address the challenges of initial-configuration-induced bias in molecular simulations.
• To explore diverse binding poses and improve the accuracy of binding free energy predictions.

Main Methods:

• Utilized a volume-based variant of well-tempered metadynamics.
• Biased spherical coordinates describing the relative host-guest positions.
• Applied the method to host-guest systems within the SAMPL6 challenge framework.

Main Results:

• Successfully calculated binding affinities for SAMPL6 host-guest systems.
• Eliminated initial-configuration bias by biasing relative positions.
• Observed new and diverse binding poses, enhancing conformational sampling.
• Demonstrated good agreement between predicted and experimental binding affinities.

Conclusions:

• The volume-based metadynamics technique is a robust method for calculating binding free energies.
• This approach effectively searches for and identifies various binding poses.
• The technique shows promise as a valuable tool for host-guest binding studies and challenges like SAMPL.