From molecular dynamics to cryo-EM: Imaging liposomes in silico

Cryogenic electron microscopy (cryo-EM) has recently emerged as a powerful single-particle approach for characterizing the structure and organization of lipid bilayers at nanometer resolution. In parallel, the ongoing refinement of force fields for molecular dynamics (MD) simulations of lipid bilayers has enabled accurate descriptions of lipid packing, thickness, and electrostatic structure in biologically relevant membrane compositions. This chapter describes a computational framework that links these two methodologies by generating realistic synthetic cryo-EM projection images directly from MD simulations of flat bilayer patches through a forward modeling approach. We outline the theoretical basis of electron-matter interactions relevant for bilayer imaging, detail the construction of electron scattering and phase-shift profiles from simulated atomic number and charge densities, and describe how these profiles are mapped onto spherical vesicles, projected, and convolved with a contrast transfer function to reproduce experimental imaging conditions. Strategies for incorporating noise, analyzing radially resolved intensity profiles, and extracting structural descriptors (e.g., bilayer thickness, leaflet contrast, and lateral heterogeneity) are presented, together with benchmarks against experimental cryo-EM data. We further discuss extensions of the method for phase-separated membranes and highlight the utility of machine learning approaches for segmenting coexisting domains. By enabling controlled, ground-truth comparisons between simulated and experimental images, this MD-to-cryo-EM workflow provides a robust route for validating analysis tools, probing the limits of resolution for bilayer features, and refining our understanding of membrane organization at the single-vesicle level.