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

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 holds great potential for structure determination of many targets simultaneously in a high-throughput fashion. However, existing methods typically focus on modeling \textit{conformational heterogeneity} within a single or very few structures and are not designed to resolve \textit{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 based on the input structure. Using CryoHype, we successfully reconstruct 1,000 distinct structures from cryo-EM imaging in the fixed-pose setting without any pre-existing knowledge of the structures present, which is beyond the capabilities of any existing algorithm.