WikiSysop: Created page with "== Citation == Levy, Axel / Poitevin, Frédéric / Martel, Julien / Nashed, Youssef / Peck, Ariana / Miolane, Nina / Ratner, Daniel / Dunne, Mike / Wetzstein, Gordon. Cryoai: Amortized inference of poses for ab initio reconstruction of 3d molecular volumes from real cryo-em images. 2022. European Conference on Computer Vision, p. 540-557 == Abstract == Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us unde..."

2024-09-06T06:56:16Z

Created page with "== Citation == Levy, Axel / Poitevin, Frédéric / Martel, Julien / Nashed, Youssef / Peck, Ariana / Miolane, Nina / Ratner, Daniel / Dunne, Mike / Wetzstein, Gordon. Cryoai: Amortized inference of poses for ab initio reconstruction of 3d molecular volumes from real cryo-em images. 2022. European Conference on Computer Vision, p. 540-557 == Abstract == Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us unde..."

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== Citation ==

Levy, Axel / Poitevin, Frédéric / Martel, Julien / Nashed, Youssef / Peck, Ariana / Miolane, Nina / Ratner, Daniel / Dunne, Mike / Wetzstein, Gordon. Cryoai: Amortized inference of poses for ab initio reconstruction of 3d molecular volumes from real cryo-em images. 2022. European Conference on Computer Vision, p. 540-557

== Abstract ==

Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us understand the basic building blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the unknown 3D poses and the 3D electron scattering potential of a biomolecule from millions of extremely noisy 2D images. Existing reconstruction algorithms, however, cannot easily keep pace with the rapidly growing size of cryo-EM datasets due to their high computational and memory cost. We introduce cryoAI, an ab initio reconstruction algorithm for homogeneous conformations that uses direct gradient-based optimization of particle poses and the electron scattering potential from single-particle cryo-EM data. CryoAI combines a learned encoder that predicts the poses of each particle image with a physics-based decoder to aggregate each particle image into an implicit representation of the scattering potential volume. This volume is stored in the Fourier domain for computational efficiency and leverages a modern coordinate network architecture for memory efficiency. Combined with a symmetric loss function, this framework achieves results of a quality on par with state-of-the-art cryo-EM solvers for both simulated and experimental data, one order of magnitude faster for large datasets and with significantly lower memory requirements than existing methods.

== Keywords ==

== Links ==

https://link.springer.com/chapter/10.1007/978-3-031-19803-8_32

== Related software ==

== Related methods ==

== Comments ==

2022Levy CryoAI - Revision history