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Resolving the structural variability of proteins is often key tounderstanding the structure-function relationship of those macromolecularmachines. Single particle analysis using Cryogenic electron microscopy(CryoEM), combined with machine learning algorithms, provides a way to revealthe dynamics within the protein system from noisy micrographs. Here, weintroduce an improved computational method that uses Gaussian mixture modelsfor protein structure representation and deep neural networks for conformationspace embedding. By integrating information from molecular models into theheterogeneity analysis, we can resolve complex protein conformational changesat near atomic resolution and present the results in a more interpretable form.

Integrating molecular models into CryoEM heterogeneity analysis using scalable high-resolution deep Gaussian mixture models