Simulation-based inference of single-molecule force spectroscopy

TL;DR

This paper introduces a simulation-based inference framework combining mechanistic modeling and deep learning to analyze single-molecule force spectroscopy data, effectively disentangling molecular properties from experimental artifacts.

Contribution

It presents a novel computational approach that enables direct Bayesian inference of hidden molecular properties from complex, non-Markovian experimental data.

Findings

Successfully disentangled molecular properties from artifacts using synthetic data.

Demonstrated broad applicability of the method to biophysical experiments.

Provided a transparent and user-friendly integration of physical models with machine learning.

Abstract

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.