Shtyrov, A. | Search bioRxiv

bioRxiv2026

Atomic modeling of radiation damage in cryoelectron microscopy datasets

Damage to biological specimens by the electron beam is the fundamental resolution-limiting factor in cryoelectron microscopy (cryo-EM) single particle analysis. There is, however, currently no method to accurately infer fluence-dependent changes to the specimen structure during electron irradiation. We develop a Bayesian framework to fit a sequence of atomic models to a series of cryo-EM reconstructions produced at increasing fluence. In particular, our algorithm is able to infer the ensemble average position and atomic displacement parameter of every atom in the macromolecule as a function of fluence. Application of the algorithm to cryo-EM datasets shows that the molecule expands during imaging and identifies environment-dependent variations in beam-induced damage. We use our results to propose a stochastic process model of this phenomenon. We envisage that our method will lead to a better mechanistic understanding of radiation damage to biological specimens and may contribute to efforts to mitigate its effects.

biophysics↗

bioRxiv2025

Measurement of atomic scattering factors by cryo-electron microscopy

Determination of specimen structure from cryo-electron microscopy (cryo-EM) experiments relies on an accurate model of the electrostatic potential of the specimen. For biological macromolecules, the potential is strongly influenced by the presence of chemical bonds between atoms, a fact unaccounted for by models of electron scattering that are currently standard in the field. We propose a Bayesian approach to the estimation of atomic scattering factors which incorporates the effect of the molecular environment while remaining fast, interpretable and transferable between molecules. Our algorithm infers atomic scattering factors directly from maps of the electrostatic potential determined by cryo-EM single particle analysis, bypassing the need for computationally-intensive theoretical calculations. The algorithm is used to infer empirical scattering factors from high-resolution reconstructions of catalase enzymes. To illustrate its broad applicability, the algorithm is also applied to a training set of publicly-available cryo-EM data. The empirical scattering factors show improved agreement with a test set of cryo-EM reconstructions. The predictions are further validated by comparison with magnetic susceptibility values of organic compounds, as well as by application to the refinement of atomic models. Significance StatementUnderstanding the structure of biomolecules is key to explaining their function. Cryo-electron microscopy is a method for reconstructing the electrostatic potential distribution of a biological macro-molecule, a quantity which contains information about atomic positions and the redistribution of charge due to chemical bonding. These factors should be modelled when inferring the structure of the molecule from its electrostatic potential. We develop an improved model of the potential that takes into account chemical bonding while remaining computationally tractable. The parameters of the model are inferred from a selection of cryo-electron microscopy datasets using Bayesian methods.

biophysics↗