Umbrella Sampling Workflows for Fast-Converging PMF Calculations without Artificial WHAM Constraints.

Area of Science:

• Computational chemistry
• Biophysics
• Molecular dynamics simulations

Background:

• Umbrella sampling is crucial for studying free-energy landscapes governing molecular permeation through lipid bilayers.
• Challenges exist in achieving converged potentials of mean force (PMFs) due to sampling limitations.
• Enhanced sampling methods are continuously developed to address these challenges.

Purpose of the Study:

• To benchmark umbrella sampling workflows for improved PMF convergence.
• To identify optimal strategies for window generation, sampling, and statistical estimation.
• To minimize computational resources required for accurate PMF calculations.

Main Methods:

• Benchmarking of different umbrella sampling parameters and workflows.
• Comparative analysis of window generation techniques.
• Evaluation of various statistical estimation methods for PMFs.
• Assessment of symmetry and periodicity constraints.

Main Results:

• Recommendations provided for enhancing PMF convergence speed.
• Strategies identified to minimize computational resource requirements.
• Significant errors highlighted when enforcing symmetry/periodicity with insufficient sampling.

Conclusions:

• Optimized umbrella sampling workflows can accelerate PMF convergence and reduce computational cost.
• Enforcing symmetry and periodicity constraints without adequate sampling can introduce substantial errors.
• Caution is advised against using these constraints in future studies with limited sampling.