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<p>CryoEMNet introduces a symmetry-aware deep learning framework for molecular reconstruction in cryo-electron microscopy (cryo-EM), achieving high-resolution and structurally consistent 3D reconstructions. By incorporating molecular symmetry constraints within deep learning-based reconstruction, CryoEMNet effectively mitigates challenges associated with noise, structural heterogeneity, and particle misalignment. The framework employs unsupervised learning and transfer learning techniques to refine molecular details and optimize particle orientations, resulting in improved reconstruction accuracy. Multi-trial evaluations demonstrate that CryoEMNet achieves an average resolution of 3.78 Å to 3.81 Å, consistently outperforming existing methods such as EMPIAR. This improvement enhances the interpretability of reconstructed density maps and facilitates more precise structural analysis. This advancement significantly improves the interpretability of the resulting density maps and enables more precise structural analyses. By harnessing symmetry-aware deep learning, CryoEMNet establishes a reliable and scalable methodology for cryo-EM reconstruction, further advancing progress in structural biology and biomedical research.</p>

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CryoEMNet driven symmetry-aware molecular reconstruction through deep learning enhanced electron microscopy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月3日

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  • Saksham Arora,
  • Shin-Hung Pan,
  • Sudhakar Kumar,
  • Sunil K. Singh,
  • Utkarsh Chauhan,
  • Wadee Alhalabi,
  • Varsha Arya,
  • Bassma Saleh Alsulami,
  • Ching-Hsien Hsu,
  • Kwok Tai Chui,
  • Brij B. Gupta

摘要

CryoEMNet introduces a symmetry-aware deep learning framework for molecular reconstruction in cryo-electron microscopy (cryo-EM), achieving high-resolution and structurally consistent 3D reconstructions. By incorporating molecular symmetry constraints within deep learning-based reconstruction, CryoEMNet effectively mitigates challenges associated with noise, structural heterogeneity, and particle misalignment. The framework employs unsupervised learning and transfer learning techniques to refine molecular details and optimize particle orientations, resulting in improved reconstruction accuracy. Multi-trial evaluations demonstrate that CryoEMNet achieves an average resolution of 3.78 Å to 3.81 Å, consistently outperforming existing methods such as EMPIAR. This improvement enhances the interpretability of reconstructed density maps and facilitates more precise structural analysis. This advancement significantly improves the interpretability of the resulting density maps and enables more precise structural analyses. By harnessing symmetry-aware deep learning, CryoEMNet establishes a reliable and scalable methodology for cryo-EM reconstruction, further advancing progress in structural biology and biomedical research.