Computational Characterization of SARS-CoV-2 S Protein Variants

The ongoing COVID-19 pandemic is a global public health emergency requiring urgent development of highly efficacious vaccines. While concentrated research efforts are underway to develop antibody-based vaccines that would neutralize SARS-CoV-2, and several first-generation vaccine candidates are currently in Phase III clinical trials or have received emergency use authorization, it is forecasted that COVID-19 will become an endemic disease requiring second-generation vaccines. The SARS-CoV-2 surface Spike (S) glycoprotein represents a prime target for vaccine development because antibodies that block viral attachment and entry, i.e. neutralizing antibodies, bind almost exclusively to the receptor binding domain (RBD). We have developed computational models for a large subset of S proteins associated with SARS-CoV-2 (with available structures in the Protein Data Bank), implemented through coarse-grained elastic network models and normal mode analysis. We then analyzed local protein domain dynamics of the S protein systems and their thermal stability (via a novel deep learning model) to characterize structural and dynamical variability among them. These results were compared against existing experimental data and used to elucidate the impact and mechanisms of SARS-CoV-2 S protein mutations and their associated antibody binding behavior. We constructed a SARS-CoV-2 antigenic map and offered predictions about the neutralization capabilities of antibody and S mutant combinations based on protein dynamic signatures. We then compared SARS-CoV-2 S protein dynamics to SARS-CoV and MERS-CoV S proteins to investigate differing antibody binding and cellular fusion mechanisms that may explain the high transmissibility of SARS-CoV-2. Our results provide insights into the dynamics-driven mechanisms of immunogenicity associated with coronavirus S proteins, and present a new approach to characterize and screen potential mutant candidates for immunogen design, as well as to characterize emerging natural variants that may escape vaccine-induced antibody responses. As of this writing, results of this work are in revision for publication in the Biophysical Journal. In the proposed work, we will use a combination of fully atomistic molecular dynamics simulations and elastic network based coarse-graining approaches to characterize emerging S protein variants to deduce potential dynamic mechanisms of immune escape based on our existing framework.