Enhanced sampling and free energy calculations for protein simulations

Area of Science:

• Computational chemistry
• Biophysics
• Molecular modeling

Background:

• Molecular dynamics (MD) simulations provide atomic-level insights into biomolecular dynamics and function.
• Calculating free energy landscapes of conformational transitions is crucial for understanding biomolecule function.
• A key limitation of MD simulations is the challenge of overcoming free energy barriers, restricting accessible timescales.

Purpose of the Study:

• To review theoretical foundations of free energy estimation in biomolecular simulations.
• To survey common enhanced sampling methodologies for molecular dynamics and free energy calculations.
• To discuss recent advancements and the combination of different enhanced sampling approaches.

Main Methods:

• Review of theoretical basis for free energy estimation.
• Categorization and review of collective variable-based (CV-based) enhanced sampling methods.
• Categorization and review of collective variable-free (CV-free) enhanced sampling methods.

Main Results:

• Enhanced sampling methodologies significantly improve the efficiency of molecular dynamics simulations.
• CV-based methods utilize predefined collective variables to guide sampling.
• CV-free methods offer alternative approaches without requiring prior knowledge of collective variables.
• Recent developments and combinations of methods show promise for further advancements.

Conclusions:

• Enhanced sampling techniques are essential for bridging the timescale gap in molecular dynamics simulations.
• Both CV-based and CV-free methods offer valuable strategies for efficient biomolecular sampling.
• Future directions include the synergistic combination of diverse enhanced sampling techniques.