Work

IsoNet2 Determines Cellular Structures at Submolecular Resolution Without Averaging

Year: 2026

Type: article

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.

Source: Microscopy and Microanalysis

DOI: 10.1093/mam/ozag053.442

Authors: Yun-Tao Liu, Hongcheng Fan, Jonathan Jih, Liam Tran, Xiaoying Zhang, Z Hong Zhou

Institutions: California NanoSystems Institute, University of California, Los Angeles

Language: English

Topic: Advanced Electron Microscopy Techniques and Applications

Subfield: Structural Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of General Medical Sciences

Award: High-Resolution CryoEM Reconstruction of Large Complexes