Reversibly Sampling Conformations and Binding Modes Using Molecular Darting.

Area of Science:

• Computational chemistry
• Molecular modeling
• Biophysics

Background:

• Sampling multiple ligand binding modes in molecular dynamics (MD) simulations is challenging due to high energy barriers.
• Ligands possess numerous internal degrees of freedom and varied orientations within binding sites.
• Existing methods struggle to efficiently explore diverse ligand conformations and positions.

Purpose of the Study:

• To develop a novel computational method for enhanced sampling of ligand binding modes.
• To improve the efficiency and accuracy of molecular simulations involving ligand-receptor interactions.
• To address the limitations of traditional MD in exploring complex binding landscapes.

Main Methods:

• Introduced a new Monte Carlo move: molecular darting (MolDarting), for reversible sampling between predefined ligand binding modes.
• Integrated MolDarting with nonequilibrium candidate Monte Carlo (NCMC) to enhance the acceptance rate of sampling moves.
• Applied and validated the combined technique on systems including a dipeptide, T4 lysozyme L99A, and HIV integrase.

Main Results:

• Observed significant increases in the acceptance rate of sampling moves compared to uniform sampling.
• Demonstrated the effectiveness of MolDarting coupled with NCMC across different molecular systems.
• Successfully sampled predefined binding modes more efficiently than conventional approaches.

Conclusions:

• The MolDarting method, combined with NCMC, provides a powerful tool for enhanced sampling in molecular simulations.
• This approach overcomes limitations in exploring ligand binding modes separated by large energy barriers.
• The technique shows promise for advancing drug discovery and understanding molecular recognition processes.