Taming Rugged Free Energy Landscapes Using an Average Force.

Area of Science:

• Computational Chemistry and Physics
• Molecular Dynamics Simulations
• Biophysics

Background:

• Molecular dynamics (MD) simulations are limited by free energy barriers in observing complex molecular transitions.
• Existing importance-sampling methods struggle with rugged free energy surfaces and long timescales (milliseconds).

Purpose of the Study:

• To review and present recent developments in adaptive biasing force (ABF) algorithms for mapping complex free energy landscapes.
• To enhance the efficiency and applicability of ABF methods for simulating rare molecular events.

Main Methods:

• Development and application of advanced importance-sampling algorithms, including meta-eABF, well-tempered, and replica-exchange variants.
• Utilizing adaptive biasing force (ABF) methods to overcome free energy barriers in molecular simulations.
• Employing novel collective variables for rigorous free energy calculations.

Main Results:

• The meta-eABF algorithm shows up to a 5-fold increase in convergence rate compared to other methods.
• New ABF variants improve sampling efficiency and numerical stability for quantum-mechanical/molecular-mechanical calculations.
• Demonstrated application in simulating rotaxane shuttling and predicting protein-ligand binding thermodynamics.

Conclusions:

• The ABF family of algorithms offers a powerful and versatile approach for investigating complex molecular processes across physics, chemistry, and biology.
• These advanced methods significantly improve the ability to simulate millisecond-timescale events and determine binding free energies accurately.