CryoDRGN-AI: Neural <i>ab initio</i> reconstruction of challenging cryo-EM and cryo-ET datasets.

ABSTRACT: Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind, and perform chemistry. Cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) can access the intrinsic heterogeneity of these complexes and are therefore key tools for understanding their function. However, 3D reconstruction of the collected imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here, we introduce cryoDRGN-AI, a method leveraging an expressive neural representation and combining an exhaustive search strategy with gradient-based optimization to process challenging heterogeneous datasets. Using cryoDRGN-AI, we reveal new conformational states in l

SUBMITTER: Levy A 

PROVIDER: S-EPMC11160740 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature