Protein Graph Neural Networks for Heterogeneous Cryo-EM Reconstruction
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914374362333184 |
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| author | Krook, Jonathan Janson, Axel Andén, Joakim Weber, Melanie Öktem, Ozan |
| author_facet | Krook, Jonathan Janson, Axel Andén, Joakim Weber, Melanie Öktem, Ozan |
| contents | We present a geometry-aware method for heterogeneous single-particle cryogenic electron microscopy (cryo-EM) reconstruction that predicts atomic backbone conformations. To incorporate protein-structure priors, we represent the backbone as a graph and use a graph neural network (GNN) autodecoder that maps per-image latent variables to 3D displacements of a template conformation. The objective combines a data-discrepancy term based on a differentiable cryo-EM forward model with geometric regularization, and it supports unknown orientations via ellipsoidal support lifting (ESL) pose estimation. On synthetic datasets derived from molecular dynamics trajectories, the proposed GNN achieves higher accuracy compared to a multilayer perceptron (MLP) of comparable size, highlighting the benefits of a geometry-informed inductive bias. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_21915 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Protein Graph Neural Networks for Heterogeneous Cryo-EM Reconstruction Krook, Jonathan Janson, Axel Andén, Joakim Weber, Melanie Öktem, Ozan Computer Vision and Pattern Recognition We present a geometry-aware method for heterogeneous single-particle cryogenic electron microscopy (cryo-EM) reconstruction that predicts atomic backbone conformations. To incorporate protein-structure priors, we represent the backbone as a graph and use a graph neural network (GNN) autodecoder that maps per-image latent variables to 3D displacements of a template conformation. The objective combines a data-discrepancy term based on a differentiable cryo-EM forward model with geometric regularization, and it supports unknown orientations via ellipsoidal support lifting (ESL) pose estimation. On synthetic datasets derived from molecular dynamics trajectories, the proposed GNN achieves higher accuracy compared to a multilayer perceptron (MLP) of comparable size, highlighting the benefits of a geometry-informed inductive bias. |
| title | Protein Graph Neural Networks for Heterogeneous Cryo-EM Reconstruction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.21915 |