2020Gupta MultiCryoGAN
Citation
Gupta, H.; Phan, T. H.; Yoo, J. & Unser, M. Multi-CryoGAN: Reconstruction of Continuous Conformations in Cryo-EM Using Generative Adversarial Networks. European Conference on Computer Vision, 2020, 429-444
Abstract
We propose a deep-learning-based reconstruction method for
cryo-electron microscopy (Cryo-EM) that can model multiple conformations
of a nonrigid biomolecule in a standalone manner. Cryo-EM
produces many noisy projections from separate instances of the same
but randomly oriented biomolecule. Current methods rely on pose and
conformation estimation which are inefficient for the reconstruction of
continuous conformations that carry valuable information. We introduce
Multi-CryoGAN, which sidesteps the additional processing by casting
the volume reconstruction into the distribution matching problem. By
introducing a manifold mapping module, Multi-CryoGAN can learn continuous
structural heterogeneity without pose estimation nor clustering.
We also give a theoretical guarantee of recovery of the true conformations.
Our method can successfully reconstruct 3D protein complexes on
synthetic 2D Cryo-EM datasets for both continuous and discrete structural
variability scenarios. Multi-CryoGAN is the first model that can
reconstruct continuous conformations of a biomolecule from Cryo-EM
images in a fully unsupervised and end-to-end manner.
Keywords
Links
https://link.springer.com/chapter/10.1007/978-3-030-66415-2_28
