Related Experiment Video
Updated: May 7, 2026

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
Published on: September 17, 2021
Umbrella Sampling Workflows for Fast-Converging PMF Calculations without Artificial WHAM Constraints
Bjarne Feddersen1, Philip C Biggin1
1Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford, South Parks Road, OxfordOX1 3QU, U.K.
Abstract:
Umbrella sampling is a powerful tool to investigate the underlying free-energy landscapes that govern the permeation properties of small molecules through complex lipid bilayers. However, obtaining converged potentials of mean force (PMFs) remains an issue due to sampling limitations, and as a result, enhanced sampling approaches are constantly being developed. Here, we benchmark umbrella sampling workflows and test different options for window generation, sampling, and statistical estimation of PMFs against each other to arrive at recommendations to improve PMF convergence speeds and minimize required computational resources. We also report large errors introduced into PMFs by enforcing their symmetry and/or periodicity when sampling is insufficient, leading us to caution against the use of these constraints in future works.
Related Concept Videos
Sampling Plans
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Cluster Sampling Method
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

