Experimental umbrella sampling of a rotary nanomachine
- 1Technische Universitat Munchen
Abstract
The free-energy landscape of a molecular system, which describes its free energy as a function of a few collective coordinates, governs its dynamics and function, but is challenging to determine experimentally. In molecular simulations, enhanced-sampling methods such as umbrella sampling use bias potentials to access regions of this landscape that are rarely visited by equilibrium fluctuations. Here, we transfer this concept to a single-molecule experiment: we combine electric-field biasing, in situ calibration of the bias potentials, and statistical reweighting to reconstruct the angular free-energy landscapes of rotary DNA nanomachines. The method resolves barriers exceeding 10 kBT and detects changes in local landscape shape caused by small modifications of the stator design. A systematic analysis of bias calibration, sampling, and finite-exposure averaging establishes the conditions required for accurate reconstruction. Experimental umbrella sampling thus relates nanoscale structure to the underlying free-energy landscape and supports the physics-informed design of artificial molecular machinery.
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BibTeX
@misc{weiss2026experimental,
title = {{Experimental umbrella sampling of a rotary nanomachine}},
author = {L. J. K. Weiss and L. Daubner and N. Tedeschi and P. Sulc and F. C. Simmel},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.15.758702},
url = {https://www.biorxiv.org/content/10.64898/2026.10.15.758702v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Experimental umbrella sampling of a rotary nanomachine
AU - L. J. K. Weiss
AU - L. Daubner
AU - N. Tedeschi
AU - P. Sulc
AU - F. C. Simmel
PY - 2026
DA - 2026/10/15
PB - bioRxiv
DO - 10.64898/2026.10.15.758702
UR - https://www.biorxiv.org/content/10.64898/2026.10.15.758702v1
AB - The free-energy landscape of a molecular system, which describes its free energy as a function of a few collective coordinates, governs its dynamics and function, but is challenging to determine experimentally. In molecular simulations, enhanced-sampling methods such as umbrella sampling use bias potentials to access regions of this landscape that are rarely visited by equilibrium fluctuations. Here, we transfer this concept to a single-molecule experiment: we combine electric-field biasing, in situ calibration of the bias potentials, and statistical reweighting to reconstruct the angular free-energy landscapes of rotary DNA nanomachines. The method resolves barriers exceeding 10 kBT and detects changes in local landscape shape caused by small modifications of the stator design. A systematic analysis of bias calibration, sampling, and finite-exposure averaging establishes the conditions required for accurate reconstruction. Experimental umbrella sampling thus relates nanoscale structure to the underlying free-energy landscape and supports the physics-informed design of artificial molecular machinery.
ER -