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. ### Competing Interest Statement The authors have declared no competing interest.

IsoNet at higher resolution. Contribute to IsoNet-cryoET/IsoNet2 development by creating an account on GitHub.

The SBGrid Consortium is an innovative global research computing group operated out of Harvard Medical School. SBGrid provides the global structural biology community with support for research…

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 perf...

Relion Webbased GUI that is user built. Contribute to schiracha/RELION-US development by creating an account on GitHub.

Relion Webbased GUI that is user built. Contribute to schiracha/RELION-US development by creating an account on GitHub.

Relion Webbased GUI that is user built. Contribute to schiracha/RELION-US development by creating an account on GitHub.

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 perfo...