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Background <p>Cryo-electron microscopy (cryo-EM) has emerged as a powerful technique for high-resolution structural determination of macromolecules. However, accurately classifying single-particle cryo-EM images remains challenging, especially when dealing with deformed particles. In traditional 2D classification methods, clustering algorithms are used for classification. This assumption leads to some deformed particles being misclassified in 2D images, which adversely affects downstream tasks. To address this challenge, we propose a point cloud–based deformation measurement model that integrates a Variational Autoencoder (VAE) with a heuristic point cloud matching algorithm to calculate particle deformation values.</p> Results <p>This model enables the identification and removal of particles with large deformations. Our experiments on simulated and real cryo-EM datasets, including Tobacco Mosaic Virus (TMV) and mixed capsids of MS2 virions (MS2). The model achieves robust classification (F1: 0.85–0.88) while preserving 93–95% of structural details, and can effectively filter out deformed particles after 2D classification.</p> Conclusion <p>The model identifies and removes deformed or misclassified particles to improve classification quality. It serves as a data-filtering post-processing step following 2D classification. By improving the quality of particle datasets, it enhances the reliability of subsequent analysis in cryo-EM.</p>

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Point cloud deformation modeling for particle selection following cryo-EM 2D classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月8日

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  • Xuan Wang,
  • Zhengao Mo,
  • Fuwei Li,
  • Fa Zhang,
  • Xiaohua Wan

摘要

Background

Cryo-electron microscopy (cryo-EM) has emerged as a powerful technique for high-resolution structural determination of macromolecules. However, accurately classifying single-particle cryo-EM images remains challenging, especially when dealing with deformed particles. In traditional 2D classification methods, clustering algorithms are used for classification. This assumption leads to some deformed particles being misclassified in 2D images, which adversely affects downstream tasks. To address this challenge, we propose a point cloud–based deformation measurement model that integrates a Variational Autoencoder (VAE) with a heuristic point cloud matching algorithm to calculate particle deformation values.

Results

This model enables the identification and removal of particles with large deformations. Our experiments on simulated and real cryo-EM datasets, including Tobacco Mosaic Virus (TMV) and mixed capsids of MS2 virions (MS2). The model achieves robust classification (F1: 0.85–0.88) while preserving 93–95% of structural details, and can effectively filter out deformed particles after 2D classification.

Conclusion

The model identifies and removes deformed or misclassified particles to improve classification quality. It serves as a data-filtering post-processing step following 2D classification. By improving the quality of particle datasets, it enhances the reliability of subsequent analysis in cryo-EM.