A Multiscale and Comparative Model for Receptor Binding of 2019 Novel Coronavirus and the Implication of its Life Cycle in Host Cells

Zhaoqian Su; Yinghao Wu

The copyright holder for this preprint (which was not peer-reviewed) is the . https: //doi.org/10.1101 //doi.org/10. /2020 brief, within a three-dimensional simulation box, the plasma membrane is represented by 1 a flat surface below the extracellular region. The area of the square is 1 µm 2 , while the 2 height of the simulation box is 500 nm. A number of ACE2 receptors (200) are initially 3 placed on the membrane surface (pink in Figure 1a ). They are represented by rigid 4 bodies of cylinders and their binding sites are located on top of the cylinders (red dots in 5 Figure 1a ). The height of each cylinder is 10nm and its radius is 5nm. On the other hand, 6 space above the plasma membrane represents the extracellular region. A number of 7 coronaviruses are located in this area (golden in Figure 1a) . Each virus is simplified as a 8 spherical rigid body with a given radius (40nm). Trimeric S-proteins are uniformly 9 distributed on the spherical surface of each virus (green dots in Figure 1a ). Each S-10 protein can interact with an ACE2 receptor on plasma membrane. After any S-protein on 11 one virus forms an encounter complex with a receptor, we assume that the host cell is 12 captured by the virus. The dissociation between the virus and the receptor is not 13 considered in the system, because we also assume that, after the association between S-14 protein and ACE2, the virus can enter the cell through membrane fusion. Following the 15 initial random configuration, the diffusion of receptors and viruses, as well as their 16 association, were simulated by a diffusion-reaction algorithm until the system reached 17 equilibrium. The detail process of the simulation is specified in the Methods. 18 Before the rigid-body simulation, in order to provide a more realistic estimation 19 on the binding between ACE2 and different coronaviruses, we specifically compared the 20 S-protein from 2019-nCoV with the S-protein from SARS. We applied our previously 21 developed residue-based kinetic Monte-Carlo (KMC) method to simulate the associate 22 processes of these two systems. In detail, the atomic coordinates of the complex between The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10. 1101 the distance between their binding interfaces is fallen within a given cutoff value d c of 20 1 Å. At the end of each trajectory, receptor and viral protein either form an encounter 2 complex through their binding interface observed in the complex structure, or diffuse 3 away from each other. Based on the simulation results collected from all the 500 4 trajectories, we counted how many times an encounter complex can be formed by the end 5 of the simulation time, which gives the probability of association. As a result, the 6 comparison of calculated probabilities of association for both systems is plotted in Figure 7 2a. 8 The figure shows that probability of association between ACE2 and the S-protein 9 from 2019-nCoV is remarkably lower than the probability of association between ACE2 10 and the S-protein from SARS. Specifically, among the 500 simulation trajectories of which is quantitatively consistent with our simulation results. 23 We then fed the information derived from the structure-based simulations into the 24 rigid-body model. Two specific simulation systems were compared. A relatively fast rate 25 of association between receptors and S-proteins on viral surfaces was adopted in the first 26 system to represe