Amortized Inference for Heterogeneous Reconstruction in Cryo-EM
- URL: http://arxiv.org/abs/2210.07387v1
- Date: Thu, 13 Oct 2022 22:06:38 GMT
- Title: Amortized Inference for Heterogeneous Reconstruction in Cryo-EM
- Authors: Axel Levy, Gordon Wetzstein, Julien Martel, Frederic Poitevin, Ellen
D. Zhong
- Abstract summary: cryo-electron microscopy (cryo-EM) provides insights into the dynamics of proteins and other building blocks of life.
The algorithmic challenge of jointly estimating the poses, 3D structure, and conformational heterogeneity of a biomolecule remains unsolved.
Our method, cryoFIRE, performs ab initio heterogeneous reconstruction with unknown poses in an amortized framework.
We show that our method can provide one order of magnitude speedup on datasets containing millions of images without any loss of accuracy.
- Score: 36.911133113707045
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cryo-electron microscopy (cryo-EM) is an imaging modality that provides
unique insights into the dynamics of proteins and other building blocks of
life. The algorithmic challenge of jointly estimating the poses, 3D structure,
and conformational heterogeneity of a biomolecule from millions of noisy and
randomly oriented 2D projections in a computationally efficient manner,
however, remains unsolved. Our method, cryoFIRE, performs ab initio
heterogeneous reconstruction with unknown poses in an amortized framework,
thereby avoiding the computationally expensive step of pose search while
enabling the analysis of conformational heterogeneity. Poses and conformation
are jointly estimated by an encoder while a physics-based decoder aggregates
the images into an implicit neural representation of the conformational space.
We show that our method can provide one order of magnitude speedup on datasets
containing millions of images without any loss of accuracy. We validate that
the joint estimation of poses and conformations can be amortized over the size
of the dataset. For the first time, we prove that an amortized method can
extract interpretable dynamic information from experimental datasets.
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