IsoNet2 Determines Cellular Structures at Submolecular Resolution Without Averaging
Abstract
Abstract We introduce IsoNet2 , an end-to-end self-supervised deep-learning method that directly reconstructs high-quality 3D densities from cryogenic electron tomography. A unified network simultaneously performs denoising, contrast transfer function correction, and missing-wedge restoration, achieving ∼20 Å resolution without averaging. A feature-rich GUI enables rapid, dataset-specific fine-tuning for end-users. IsoNet2 resolves domain organization in HIV capsid proteins, tRNA occupancy in individual ribosomes, and in situ architectures of mitochondrial respiration-related complexes, enabling atomic-level interpretation of cellular environments.
Fields of Study & Subjects
Advanced Electron Microscopy Techniques and Applications
RNA and protein synthesis mechanisms
ATP Synthase and ATPases Research
Publication details
- Journal / source
- Microscopy and Microanalysis
- Publisher
- Oxford University Press (OUP)
- Volume
- 32
- Issue
- Supplement_1
- Publication date
- 1 July 2026
- Language
- EN