arXiv subjects

Lars Dingeldein

Publications and source records attributed to Lars Dingeldein.

5 recordsLinked to original sources

arXiv2026

A differentiable photon-by-photon likelihood for continuous free-energy landscapes and diffusion coefficients from single-molecule FRET

Single-molecule FRET probes the conformational dynamics of biomolecules by measuring the distance between two dyes. The experiment produces a stream of coloured photons, which is only an indirect readout of these dynamics. Recovering the free-energy landscape and diffusion coefficient from such a photon stream is a difficult inverse problem. Existing approaches assume a small number of discrete states, bin the photons, or are computationally expensive. Here we derive an exact likelihood for the recorded photon stream under a model in which the dye distance diffuses on a continuous free-energy landscape. It uses the raw inter-photon times and colours at full time resolution, and analytically integrates out all hidden trajectories. The likelihood is differentiable, so automatic differentiation returns exact gradients with respect to all model parameters. This lets us jointly infer the free-energy landscape, the diffusion coefficient, and the photophysical parameters by gradient-based optimization. On simulated data, we recover free-energy landscapes, including ones with a short-lived intermediate, together with the diffusion coefficients. Uncertainties follow from the curvature of the likelihood, computed directly from the same gradients. The framework also guides experimental design. Before any data are recorded, we can evaluate how much a given acquisition setting reduces the resulting uncertainty. Evaluation on the GPU is fast, and independent traces are processed in parallel, so a single fit converges in minutes and scales to large datasets. The likelihood extends photon-by-photon analysis to continuous free-energy landscapes and diffusion coefficients, putting fast quantitative inference with uncertainties within reach of the smFRET community.

physics.chem-ph↗

arXiv2025

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

The study of biomolecular folding has been greatly advanced by single-molecule force spectroscopy (SMFS), which enables the observation of the dynamics of individual molecules. However, extracting quantitative models of fundamental properties such as folding landscapes from SMFS data is very challenging due to instrumental noise, linker artifacts, and the inherent stochasticity of the process, often requiring extensive datasets and complex calibration. Here, we introduce a framework based on simulation-based inference (SBI) that overcomes these limitations by integrating physics-based modeling with deep learning. We first apply this framework to analyze constant-force measurements of a DNA hairpin. From a single experimental trajectory of only two seconds, we successfully reconstruct the hairpin's free energy landscape and folding dynamics, obtaining results in close agreement with established deconvolution methods that require 10-100 times more data. Furthermore, we demonstrate the generality of our approach by applying it to a riboswitch aptamer featuring multiple states and tertiary contacts, resolving the profile of a landscape featuring four metastable states from a single trajectory. The Bayesian nature of this approach robustly quantifies uncertainties for all inferred parameters, including diffusion coefficients and linker stiffness, without needing independent measurements of instrument properties. The inferred models are predictive, generating simulated trajectories that quantitatively reproduce experimental thermodynamics and kinetics. The ability to derive statistically robust models from minimal datasets is crucial for investigating complex biomolecular systems where extensive data collection is impractical, paving the way for novel applications of SMFS.

physics.chem-ph↗

arXiv2025

Cryo-em images are intrinsically low dimensional

Simulation-based inference provides a powerful framework for cryo-electron microscopy, employing neural networks in methods like CryoSBI to infer biomolecular conformations via learned latent representations. This latent space represents a rich opportunity, encoding valuable information about the physical system and the inference process. Harnessing this potential hinges on understanding the underlying geometric structure of these representations. We investigate this structure by applying manifold learning techniques to CryoSBI representations of hemagglutinin (simulated and experimental). We reveal that these high-dimensional data inherently populate low-dimensional, smooth manifolds, with simulated data effectively covering the experimental counterpart. By characterizing the manifold's geometry using Diffusion Maps and identifying its principal axes of variation via coordinate interpretation methods, we establish a direct link between the latent structure and key physical parameters. Discovering this intrinsic low-dimensionality and interpretable geometric organization not only validates the CryoSBI approach but enables us to learn more from the data structure and provides opportunities for improving future inference strategies by exploiting this revealed manifold geometry.

q-bio.QM↗

arXiv2024

Simulation-based inference of single-molecule experiments

Single-molecule experiments are a unique tool to characterize the structural dynamics of biomolecules. However, reconstructing molecular details from noisy single-molecule data is challenging. Simulation-based inference (SBI) integrates statistical inference, physics-based simulators, and machine learning and is emerging as a powerful framework for analysing complex experimental data. Recent advances in deep learning have accelerated the development of new SBI methods, enabling the application of Bayesian inference to an ever-increasing number of scientific problems. Here, we review the nascent application of SBI to the analysis of single-molecule experiments. We introduce parametric Bayesian inference and discuss its limitations. We then overview emerging deep-learning-based SBI methods to perform Bayesian inference for complex models encoded in computer simulators. We illustrate the first applications of SBI to single-molecule force-spectroscopy and cryo-electron microscopy experiments. SBI allows us to leverage powerful computer algorithms modeling complex biomolecular phenomena to connect scientific models and experiments in a principled way.

physics.chem-ph↗

arXiv2022

Simulation-based inference of single-molecule force spectroscopy

Single-molecule force spectroscopy (smFS) is a powerful approach to studying molecular self-organization. However, the coupling of the molecule with the ever-present experimental device introduces artifacts, that complicates the interpretation of these experiments. Performing statistical inference to learn hidden molecular properties is challenging because these measurements produce non-Markovian time-series, and even minimal models lead to intractable likelihoods. To overcome these challenges, we developed a computational framework built on novel statistical methods called simulation-based inference (SBI). SBI enabled us to directly estimate the Bayesian posterior, and extract reduced quantitative models from smFS, by encoding a mechanistic model into a simulator in combination with probabilistic deep learning. Using synthetic data, we could systematically disentangle the measurement of hidden molecular properties from experimental artifacts. The integration of physical models with machine learning density estimation is general, transparent, easy to use, and broadly applicable to other types of biophysical experiments.

physics.chem-ph↗