Optimal reconstruction of the folding landscape using differential energy surface analysis
Arthur La Porta, Natalia A. Denesyuk, Michel de Messieres

TL;DR
This paper introduces differential energy surface analysis (DESA), a method to accurately reconstruct folding energy landscapes from biased experimental data by estimating the gradient of the energy surface, validated through simulations and single-molecule experiments.
Contribution
DESA provides a maximum likelihood estimate of the energy landscape gradient from biased data, offering a new approach that is comparable to WHAM but focuses on the energy surface derivative.
Findings
DESA accurately reconstructs energy landscapes from biased data.
The method detects when the energy is not a single-valued function.
Experimental data confirms DESA's effectiveness in real systems.
Abstract
In experiments and in simulations, the free energy of a state of a system can be determined from the probability that the state is occupied. However, it is often necessary to impose a biasing potential on the system so that high energy states are sampled with sufficient frequency. The unbiased energy is typically obtained from the data using the weighted histogram analysis method (WHAM). Here we present differential energy surface analysis (DESA), in which the gradient of the energy surface, dE/dx, is extracted from data taken with a series of harmonic biasing potentials. It is shown that DESA produces a maximum likelihood estimate of the folding landscape gradient. DESA is demonstrated by analyzing data from a simulated system as well as data from a single-molecule unfolding experiment in which the end-to-end distance of a DNA hairpin is measured. It is shown that the energy surface…
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