# IsoNet2 determines cellular structures at submolecular resolution without averaging

## TL;DR

IsoNet2 is a deep-learning tool that reconstructs high-resolution 3D cellular structures from electron tomograms without averaging.

## Contribution

IsoNet2 introduces a unified self-supervised deep-learning framework for submolecular resolution imaging without averaging.

## Key findings

- IsoNet2 achieves ~20 Å resolution in 3D reconstructions from cryogenic electron tomograms.
- The method resolves tRNA occupancy in individual ribosomes and mitochondrial complex architectures.
- A GUI allows dataset-specific fine-tuning for end-users.

## Abstract

We introduce IsoNet2, an end-to-end self-supervised deep-learning method that directly reconstructs high-quality 3D densities from cryogenic electron tomograms. 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.