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April 3rd, 2025
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Fudan University
molecular biology
biorxiv

OPUS-TOMO: Deep Learning Framework for Structural Heterogeneity Analysis in Cryo-electron Tomography

Luo, Z.Open in Google Scholar•Chen, X.Open in Google Scholar•Wang, Q.Open in Google Scholar•Ma, J.Open in Google Scholar

Structural heterogeneity due to varying compostions and dynamic conformations of macromolecules in cellular environment in situ presents significant challenges to biological discovery by cryo-electron tomography (cryo-ET). In this paper, we present OPUS-TOMO, a unified deep learning framework for analyzing structural heterogeneity throughout the cryo-ET workflow. The method adopts a convolutional Encoder-Decoder architecture that adeptly maps real-space subtomograms onto a smooth low-dimensional latent space, allowing capture of the complete landscape of compositional and conformational variations of macromolecules in cryoET data. OPUS-TOMO also incorporates multiple effective techniques such as a per-particle 3D CTF model and an end-to-end pose correction network specifically for cryo-ET data. Applications of OPUS-TOMO to several real cryo-ET datasets demonstrate its outstanding capacities in resolving structural heterogeneity at various stages of cryo-ET reconstruction. The development of advanced methodologies such as OPUS-TOMO would potentially accelerate the quest for understanding biological mechanisms in situ on a proteomic scale at molecular levels in the broad tomography community. The software is available at https://github.com/alncat/opusTOMO.

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