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bioRxiv · 10.1101/2025.07.27.666683

Unsupervised learning of structural variability in cryo-EM data using normal mode analysis of deformable atomic models

Nashed, Y.·Martel, J.·Peck, A.·Levy, A.·Mai, H.·Wetzstein, G.·Miolane, N.·Ratner, D.·Poitevin, F.
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Abstract

Cryogenic electron microscopy (cryo-EM) has emerged as the method of choice to characterize the structural variability of biomolecules at near-atomic resolution. We present a reconstruction approach that eliminates the need for post-hoc atomic model fitting in 3D maps by deforming a given atomic model along its normal modes directly against the 2D data. This end-to-end approach inherently reduces the risk of error propagation while increasing interpretability of resulting structural ensembles.

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Nashed, Y., Martel, J., Peck, A., Levy, A., Mai, H., Wetzstein, G., Miolane, N., Ratner, D., Poitevin, F.. 2025-07-31. Unsupervised learning of structural variability in cryo-EM data using normal mode analysis of deformable atomic models. https://doi.org/10.1101/2025.07.27.666683

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