CryoDyna: Multiscale end-to-end modeling of cryo-EM macromolecule dynamics with physics-aware neural network

Abstract

Single-particle cryo-EM has transformed structural biology but still faces challenges in resolving conformational heterogeneity at atomic resolution. Existing cryo-EM heterogeneity analysis methods either lack atomic details or tend to subject to overfitting due to image noise and limited information in single views. To obtain atomic detailed multiple conformations and make full use of particle images of different orientations, we present here CryoDyna, a deep learning framework to infer macromolecular dynamics directly from 2D projections by integrating cross-view attention and multi-scale deformation modeling. Combining coarse-grained MARTINI representation with atomic backmapping, CryoDyna achieves near-atomic interpretation of protein conformational landscapes. Validated on multiple simulated and experimental datasets, CryoDyna demonstrates improved modeling accuracy and robustly recovers multi-scale complex structure changes hidden in the cryo-EM particle stacks. As examples, we generated protein-RNA coordinated motions, resolved dynamics in the unseen region of RAG signal end complex, mapped translocating ribosome states in a one-shot manner, and revealed step-wise closure of a membrane-anchored protein multimer. This work bridges the gap between cryo-EM heterogeneity analysis and atomic-scale structural dynamics, offering a promising tool for exploration of complex biological mechanisms.