Point transformer for protein structural heterogeneity analysis using CryoEM
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918305933033472 |
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| author | Chen, Muyuan Li, Muchen Liao, Renjie |
| author_facet | Chen, Muyuan Li, Muchen Liao, Renjie |
| contents | Structural dynamics of macromolecules is critical to their structural-function relationship. Cryogenic electron microscopy (CryoEM) provides snapshots of vitrified protein at different compositional and conformational states, and the structural heterogeneity of proteins can be characterized through computational analysis of the images. For protein systems with multiple degrees of freedom, it is still challenging to disentangle and interpret the different modes of dynamics. Here, by implementing Point Transformer, a self-attention network designed for point cloud analysis, we are able to improve the performance of heterogeneity analysis on CryoEM data, and characterize the dynamics of highly complex protein systems in a more human-interpretable way. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_18713 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Point transformer for protein structural heterogeneity analysis using CryoEM Chen, Muyuan Li, Muchen Liao, Renjie Quantitative Methods Artificial Intelligence Structural dynamics of macromolecules is critical to their structural-function relationship. Cryogenic electron microscopy (CryoEM) provides snapshots of vitrified protein at different compositional and conformational states, and the structural heterogeneity of proteins can be characterized through computational analysis of the images. For protein systems with multiple degrees of freedom, it is still challenging to disentangle and interpret the different modes of dynamics. Here, by implementing Point Transformer, a self-attention network designed for point cloud analysis, we are able to improve the performance of heterogeneity analysis on CryoEM data, and characterize the dynamics of highly complex protein systems in a more human-interpretable way. |
| title | Point transformer for protein structural heterogeneity analysis using CryoEM |
| topic | Quantitative Methods Artificial Intelligence |
| url | https://arxiv.org/abs/2601.18713 |