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Quantitative and Predictive Folding Models from Limited Single-Molecule Data Using Simulation-Based Inference

Lars Dingeldein1,2, Aaron Lyons3, Pilar Cossio4,5, Michael T. Woodside3,6,7, and Roberto Covino8,2,*

  • *Contact author: covino@fias.uni-frankfurt.de
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Phys. Rev. Lett. 137, 088402 – Published 19 August, 2026

DOI: https://doi.org/10.1103/yltb-6jkj

Abstract

Single-molecule force spectroscopy resolves folding dynamics one molecule at a time, but extracting quantitative free-energy landscapes typically requires extensive datasets and careful instrument calibration to disentangle the molecule from linker and apparatus artifacts. We introduce a simulation-based inference framework that combines physics-based modeling with deep learning to recover the Bayesian posterior of a folding model directly from a single short trajectory. A 2-s constant-force measurement of a DNA hairpin is sufficient to reconstruct the folding landscape, matching deconvolution baselines that require 20–100 times more data and eliminating separate linker or instrument characterization. The same approach recovers four metastable states of a riboswitch aptamer from a single 5-s trajectory. Every parameter, including diffusion coefficients and linker stiffness, is included in the calibrated posterior. The current implementation assumes one-dimensional Markovian dynamics, but more complex models can be substituted within the same framework, opening single-molecule force spectroscopy to high-throughput parallel experiments and to systems where extensive data collection is impractical.

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Phys. Rev. Lett. 137, 088402– Published 19 August, 2026
  • Received 13 August 2025
  • Accepted 19 May 2026
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