CryoHype: Reconstructing a thousand cryo-EM structures with transformer-based hypernetworks

TL;DR

CryoHype introduces a transformer-based hypernetwork that enables high-throughput cryo-EM structure reconstruction of hundreds to thousands of molecular species, surpassing previous methods in scalability and accuracy.

Contribution

This work presents CryoHype, a novel hypernetwork approach that dynamically adapts neural representations for large-scale cryo-EM structure determination, addressing compositional heterogeneity.

Findings

Achieves state-of-the-art results on 100-structure benchmark

Successfully reconstructs 1,000 structures from unlabeled images

Demonstrates scalability and effectiveness of the hypernetwork approach

Abstract

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.