WikiSysop: Created page with "== Citation == Shi, Bin / Zhang, Kevin / Fleet, David J. / McLeod, Robert A. / Miller, R.J. Dwayne / Howe, Jane Y. Deep generative priors for biomolecular 3D heterogeneous reconstruction from cryo-EM projections. 2024. J. Structural Biology, Vol. 216, No. 2, p. 108073 == Abstract == Cryo-electron microscopy has become a powerful tool to determine three-dimensional (3D) structures of rigid biological macromolecules from noisy micrographs with single-particle reconstruc..."

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Created page with "== Citation == Shi, Bin / Zhang, Kevin / Fleet, David J. / McLeod, Robert A. / Miller, R.J. Dwayne / Howe, Jane Y. Deep generative priors for biomolecular 3D heterogeneous reconstruction from cryo-EM projections. 2024. J. Structural Biology, Vol. 216, No. 2, p. 108073 == Abstract == Cryo-electron microscopy has become a powerful tool to determine three-dimensional (3D) structures of rigid biological macromolecules from noisy micrographs with single-particle reconstruc..."

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== Citation ==

Shi, Bin / Zhang, Kevin / Fleet, David J. / McLeod, Robert A. / Miller, R.J. Dwayne / Howe, Jane Y. Deep generative priors for biomolecular 3D heterogeneous reconstruction from cryo-EM projections. 2024. J. Structural Biology, Vol. 216, No. 2, p. 108073

== Abstract ==

Cryo-electron microscopy has become a powerful tool to determine three-dimensional (3D) structures of rigid
biological macromolecules from noisy micrographs with single-particle reconstruction. Recently, deep neural
networks, e.g., CryoDRGN, have demonstrated conformational and compositional heterogeneity of complexes.
However, the lack of ground-truth conformations poses a challenge to assess the performance of heterogeneity
analysis methods. In this work, variational autoencoders (VAE) with three types of deep generative priors were
learned for latent variable inference and heterogeneous 3D reconstruction via Bayesian inference. More specifically,
VAEs with “Variational Mixture of Posteriors” priors (VampPrior-SPR), non-parametric exemplar-based
priors (ExemplarPrior-SPR) and priors from latent score-based generative models (LSGM-SPR) were quantitatively
compared with CryoDRGN. We built four simulated datasets composed of hypothetical continuous
conformation or discrete states of the hERG K + channel. Empirical and quantitative comparisons of inferred
latent representations were performed with affine-transformation-based metrics. These models with more
informative priors gave better regularized, interpretable factorized latent representations with better conserved
pairwise distances, less deformed latent distributions and lower within-cluster variances. They were also tested
on experimental datasets to resolve compositional and conformational heterogeneity (50S ribosome assembly,
cowpea chlorotic mottle virus, and pre-catalytic spliceosome) with comparable high resolution. Codes and data
are available: https://github.com/benjamin3344/DGP-SPR.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S1047847724000133

== Related software ==

== Related methods ==

== Comments ==

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