@inproceedings{
levy2024mixture,
title={Mixture of neural fields for heterogeneous reconstruction in cryo-{EM}},
author={Axel Levy and Rishwanth Raghu and David Shustin and Adele Rui-Yang Peng and Huan Li and Oliver Biggs Clarke and Gordon Wetzstein and Ellen D Zhong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=TuspoNzIdB}
}
Cryo-electron microscopy (cryo-EM) is an experimental technique for protein structure determination that images an ensemble of macromolecules in near-physiological contexts. While recent advances enable the reconstruction of dynamic conformations of a single biomolecular complex, current methods do not adequately model samples with mixed conformational and compositional heterogeneity. In particular, datasets containing mixtures of multiple proteins require the joint inference of structure, pose, compositional class, and conformational states for 3D reconstruction. Here, we present Hydra, an approach that models both conformational and compositional heterogeneity fully ab initio by parameterizing structures as arising from one of K neural fields. We employ a hybrid optimization strategy and demonstrate the effectiveness of our approach on synthetic datasets composed of mixtures of proteins with large degrees of conformational variability. We additionally demonstrate Hydra on an experimental dataset imaged of a cellular lysate containing a mixture of different protein complexes. Hydra expands the expressivity of heterogeneous reconstruction methods and thus broadens the scope of cryo-EM to increasingly complex samples.
Mixture of neural fields for heterogeneous reconstruction in cryo-EM
Axel Levy Rishwanth Raghu David Shustin Adele Rui-Yang Peng Huan Li Oliver Biggs Clarke Gordon Wetzstein Ellen D Zhong