NCSI: Computational Characterization of SARS-CoV-2 S Protein Variants

Status	Completed
Mentor Name	Anna Tarakanova
Mentor's XSEDE Affiliation	Research Allocation
Mentor Has Been in XSEDE Community	3-4 years
Project Title	Computational Characterization of SARS-CoV-2 S Protein Variants
Summary	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. 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.
Job Description	The student will participate in model development, simulation production, and data analysis related to molecular dynamics modeling and deep learning. The student will work closely with a graduate student in the group.
Computational Resources	XSEDE Stampede2, Bridges2
Contribution to Community	This is an ongoing project that received an allocation through the Covid-19 HPC Consortium.
Position Type	Learner
Training Plan	The student will work closely with the graduate student in the group on the project, to develop a research plan, receive training in the use of XSEDE resources and data analysis, and perform research.
Student Prerequisites/Conditions/Qualifications	Familiarity with HPC resource use, molecular dynamics, deep learning, and/or programming are suggested skills but not required.
Duration	Semester
Start Date	01/24/2022
End Date	05/20/2022