CryoHyper: Toward Reliable Conformational Spaces in Cryo-EM and Cryo-ET

Resolving conformational heterogeneity is essential for understanding the functional mechanisms of macromolecular complexes. Existing continuous heterogeneity methods can generate smoothly varying structures but do not necessarily organize them according to structural relationships. As a result, structurally dissimilar conformations may become neighbors in the learned representation, while distinct motions can become entangled, complicating recovery of continuous conformational changes. This problem is compounded by noise and missing-wedge effects in cryo-electron tomography (cryo-ET), hindering atomic conformational analysis. We present CryoHyper, a framework for learning reliable conformational spaces from cryo-electron microscopy (cryo-EM) and cryo-ET. Guided by a reference atomic model, CryoHyper models domain motions in a hyperbolic subspace and local deformations in a Euclidean subspace. The learned conformational coordinates condition density generation, placing atomic models and density maps in the same conformational coordinate system. For cryo-ET, the framework combines learnable spectral weighting with reconstruction training using random rotations and artificial wedge masking to reduce the influence of noise and missing-wedge effects on conformational inference. Experiments on simulated and public datasets show that CryoHyper recovers physically plausible conformational changes and organizes them in closer agreement with the underlying structural relationships.