bioRxiv · 10.64898/2025.12.09.693325
IsoNet2 determines cellular structures at submolecular resolution without averaging
Abstract
AbstractWe 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 [A] 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.
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Liu, Y., Fan, H., Jih, J., Tran, L., Zhang, X., Zhou, Z. H.. 2025-12-11. IsoNet2 determines cellular structures at submolecular resolution without averaging. https://doi.org/10.64898/2025.12.09.693325
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