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A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation.

Sinuo LiuXiaojuan BanXiangrui ZengFengnian ZhaoYuan GaoWenjie WuHongpan ZhangFeiyang ChenThomas HallXin GaoMin Xu
Published in: BMC bioinformatics (2020)
The proposed multi-ball model can achieve more crowded packaging results and contains richer elements with different properties to obtain more realistic cryo-electron tomogram simulation. This enables users to simulate cryo-electron tomogram images with non-deformable macromolecular complexes and deformable ultrastructures under a unified framework. To illustrate the advantages of our framework in improving the compression ratio, we calculated the volume of simulated macromolecular under our multi-ball method and traditional single-ball method. We also performed the packing experiment of filaments and membranes to demonstrate the simulation ability of deformable structures. Our method can be used to do a benchmark by generating large labeled cryo-ET dataset and evaluating existing image processing methods. Since the content of the simulated cryo-ET is more complex and crowded compared with previous ones, it will pose a greater challenge to existing image processing methods.
Keyphrases
  • electron microscopy
  • high resolution
  • deep learning
  • virtual reality
  • mass spectrometry
  • machine learning
  • solar cells
  • computed tomography