Efficient Cryo-Electron Tomogram Simulation of Macromolecular Crowding with Application to SARS-CoV-2

Sinuo Liu, Yan Ma, Xiaojuan Ban, Xiangrui Zeng, Vamsi Nallapareddy, Ajinkya Chaudhari, Min Xu
2020 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
We propose an efficient method for simulating a cryo-Electron Tomography (cryo-ET) image of a target macromolecule with several neighbor macromolecules packed to achieve a realistic crowded cytoplasm content. The simulated results are subtomograms with corresponding noise-free 3D density maps and pre-specified labels (PDB ID, center locations, and orientations) to assist bioimage analysis. They can serve as benchmark datasets for testing developing cryo-ET analysis algorithms and as training
more » ... asets with readily available ground truth labels for learning neural network models. The COVID-19 pandemic has sparked a global health crisis that severely impacting lives worldwide. As an important application, we simulated the scene of SARS-CoV-2 interacting with the host cell. The simulated cryo-ET images clearly showed the binding domain of the virus and the host cell to facilitate the research of SARS-CoV-2' infection. We also trained two different classification models to demonstrate that our simulated cryo-ET data is able to assist the cryo-ET analysis task and to validate the performance between different methods.
doi:10.1109/bibm49941.2020.9313185 fatcat:fyjmy3sic5efvb4rajxwooow5q