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arXiv · 2311.06194

CESPED: a new benchmark for supervised particle pose estimation in Cryo-EM

Ruben Sanchez-Garcia·Michael Saur·Javier Vargas·Carl Poelking·Charlotte M Deane
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Abstract

Cryo-EM is a powerful tool for understanding macromolecular structures, yet current methods for structure reconstruction are slow and computationally demanding. To accelerate research on pose estimation, we present CESPED, a new dataset specifically designed for Supervised Pose Estimation in Cryo-EM. Alongside CESPED, we provide a PyTorch package to simplify Cryo-EM data handling and model evaluation. We evaluated the performance of a baseline model, Image2Sphere, on CESPED, which showed promising results but also highlighted the need for further improvements. Additionally, we illustrate the potential of deep learning-based pose estimators to generalise across different samples, suggesting a promising path toward more efficient processing strategies. CESPED is available at https://github.com/oxpig/cesped.

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Ruben Sanchez-Garcia, Michael Saur, Javier Vargas, Carl Poelking, Charlotte M Deane. 2023-11-10. CESPED: a new benchmark for supervised particle pose estimation in Cryo-EM. https://arxiv.org/abs/2311.06194

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