CryoHype: Reconstructing a thousand cryo-EM structures with transformer-based hypernetworks
This enables high-throughput cryo-EM structure determination for dynamic biomolecular complexes, addressing a bottleneck in structural biology.
The authors tackled the challenge of reconstructing many distinct molecular structures from cryo-EM images, which existing methods struggle with due to compositional heterogeneity, and achieved state-of-the-art results on a benchmark with 100 structures and scaled to 1,000 structures.
Cryo-electron microscopy (cryo-EM) is an indispensable technique for determining the 3D structures of dynamic biomolecular complexes. While typically applied to image a single molecular species, cryo-EM has the potential for structure determination of many targets simultaneously in a high-throughput fashion. However, existing methods typically focus on modeling conformational heterogeneity within a single or a few structures and are not designed to resolve compositional heterogeneity arising from mixtures of many distinct molecular species. To address this challenge, we propose CryoHype, a transformer-based hypernetwork for cryo-EM reconstruction that dynamically adjusts the weights of an implicit neural representation. Using CryoHype, we achieve state-of-the-art results on a challenging benchmark dataset containing 100 structures. We further demonstrate that CryoHype scales to the reconstruction of 1,000 distinct structures from unlabeled cryo-EM images in the fixed-pose setting.