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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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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