Dingeldein, L. | bioRxiv Science

bioRxiv2025

Counting particles could give wrong probabilities in Cryo-Electron Microscopy

Cryo-electron microscopy (cryo-EM) experiments take 2D snapshots of individual proteins. In principle, these snapshots contain not only the main biomolecular conformations but also scarcely populated states and rare transitions between intermediates. This makes cryo-EM a powerful tool, not only for investigating the structure of biomolecules at high resolution but also for inferring the entire conformational ensemble distribution. Some recent works have reported conformational state populations by counting particle-images from cryo-EM. We wish to caution the community that these measurements are highly susceptible to noise and should not be relied upon as a precise estimate of the thermodynamic landscape of a biomolecule for understanding its biological function. Here, we demonstrate that the extremely noisy nature of cryo-EM images and uncertainty in the viewing orientations of biomolecules lead to ambiguities when assigning images to structures. If ignored, this ambiguity can introduce inherent bias when determining the populations of conformational states through individual particle assignment. We further show that modeling the conformational probability distribution using the entire image dataset mitigates these biases.

biophysics↗

bioRxiv2024

Amortized template-matching of molecular conformations from cryo-electron microscopy images using simulation-based inference

AO_SCPLOWBSTRACTC_SCPLOWBiomolecules undergo conformational changes to perform their function. Cryo-electron microscopy (cryo-EM) can capture snapshots of biomolecules in various conformations. However, these images are noisy and display the molecule in unknown orientations, making it difficult to separate conformational differences from differences due to noise or projection directions. Here, we introduce cryo-EM simulation-based inference (cryoSBI) to infer the conformations of biomolecules and the uncertainties associated with the inference from individual cryo-EM images. CryoSBI builds on simulation-based inference, a combination of physics-based simulations and probabilistic deep learning, allowing us to use Bayesian inference even when likelihoods are too expensive to calculate. We begin with an ensemble of conformations, which can be templates from molecular simulations or modelling, and use them as structural hypotheses. We train a neural network approximating the Bayesian posterior using simulated images from these templates, and then use it to accurately infer the conformations of biomolecules from experimental images. Training is only done once, and after that, it takes just a few milliseconds to make inference on an image, making cryoSBI suitable for arbitrarily large datasets. CryoSBI eliminates the need to estimate particle pose and imaging parameters, significantly enhancing the computational speed in comparison to explicit likelihood methods. We illustrate and benchmark cryoSBI on synthetic data and showcase its promise on experimental single-particle cryo-EM data.

biophysics↗