Cryo-Electron Tomogram Simulation for Macromolecules Crowding Using Molecular Dynamics

Cryo-electron tomography (cryo-ET) is an important technique used to explore the structure and position of macromolecular complexes in a cellular environment. However, it is extremely difficult to extract sufficient information due to missing-wedge effects, low signal-to-noise ratio (SNR), and the compactness of the particles. Currently, unsupervised cryoET analysis methods struggle to achieve the desired accuracy, while supervised methods require a large amount of labeled data, which costs a lot of manpower, material resources, and financial resources. Even so, Cryo-ET simulation is beneficial as it provides an effective solution to assist supervised analysis methods by providing a large amount of labeled data to train, test, and optimize the models. However, most simulation methods only focus on a single structure, which can only provide limited help. Few methods simulate macromolecular crowding, but they either difficult to achieve a sufficient crowding level or cannot avoid overlap. Therefore, we proposed a new cryo-ET simulation method that packs macromolecules based on molecular dynamics to generate realistic and compact macromolecular crowding without overlap. We also simplified each macromolecule into a small ball to accelerate the simulation process. Our mechanics-based model computes the non-specific interaction, electrostatic interaction, and an external force to obtain the packed macromolecule crowding. From there, the obtained simulated cryo-ET is more realistic according to the imaging principle based on the coordinates of macromolecules under an equilibrium state. In conclusion, our experiments show that our method is able to pack the structures more tightly without overlap, and the simulated cryo-ET is more biologically realistic. Our simulation data can be used to expand the real data set and test methods such as particle picking, protein classification, and protein segmentation. The experimental results show that using the simulation method in this paper, the accuracy of training with all real data can be achieved when only 30% of the real data is used. This simulation method solves the scarcity, expense, and difficult-to-label problems of cryo-ET data, and provides a large amount of simulation data for researchers in the field of computational biology.