Silico analysis of interaction between full-length SARS-CoV2 S protein with human Ace2 receptor: Modelling, docking, MD simulation.

Many key residues, which mediate the interaction between SARS-CoV2 spike glycoprotein (S protein) and human ACE2 receptor, have been reviewed using the SARS-CoV2 S spike protein with human ACE2 complex. The initial SARS-CoV2 S protein and ACE2 protein complex structure is formed by RBD structure of SARS-CoV2 S protein and ACE2 protein. However, the cryo-EM structure study targeting SARS-Cov S protein with human ACE2 complex has shown that there exist different binding conformations during the binding process facing ACE2 protein. It suggests the interaction between SARS-CoV2 S spike protein complex might have different binding conformations, which request full-length of SARS-CoV2 S protein complex in the structure-functional analysis. In this study, we built a full-length SARS-CoV2 S protein with human ACE2 complex by computational methods. Residues K31, H34, E35 in ACE2 protein were showed both in our full-length model and RBD structure model, which recognized as critical residues in previous studies. Surprisingly, ACE2 residues E564, R559, N556 were only found participating in the interaction of our full-length model, which suggested the full-length model has bigger binding interface. This finding was further supported by the interaction network of full-length model and RBD model. Meanwhile, the method bias was taken into consideration. Eventually, the MM-PBSA results showed the full-length model had a stronger binding free energy (almost 5-fold) than the RBD structure model of SARS-CoV2 S spike protein complex. In computational level, we present a stronger binding model containing a full-length structure of SARS-CoV2 S protein with ACE2 complex.

Strategic Hospital Runs (preprint)

Hospital runs are of primary concern in a pandemic and are especially devastating when nonsevere patients crowd out severe ones. We study a rushing game among patients for a given infection pattern and find that hospital crowding out can result from strategic behaviors. Even if immediate treatment is unnecessary, nonsevere patients may nonetheless seek treatment to obtain future priority of treatment in case their condition deteriorates. With SIR (susceptible-infectious-removed) dynamics, runs start long before the capacity is insufficient for severe patients because expecting a run later can trigger earlier runs. The strategic behaviors can even make hospital runs self-fulfilling and make hospital crowding out nonmonotonic in the capacity. However, rushing is not the only problem. Inefficient waiting may also happen as individuals ignore the social cost of future overload. While many health systems prevent hospital crowding out by excluding nonsevere patients, this policy is generally inefficient. We discuss alternative policies.