A Mechanistic Basis for the Analysis of SARS-CoV-2 Omicron Variant Severity

Aarav Arora, Kavitha Mukund, Shankar Subramaniam

Abstract: The rise of SARS-CoV-2/COVID-19 and its many evolving variants has killed millions in recent years. SARS-CoV-2 is still regularly mutating and recent trends have shown the likelihood of many more variants in the future. In this paper, we present a novel methodology for addressing new viruses based on patient data and their respective clinical outcomes and apply it to the Omicron variant of COVID-19. Our strategy is three-fold: (1) utilize clinical severity to narrow the set of mutations in the protein through statistical tests and root-mean-square deviation (RMSD) in comparison to the wild type protein, (2) analyze Delta Delta G (DDG), electrostatic potential, and DOPE per-residue (Discrete Optimized Protein Energy) scores of individual mutations’ protein structures in comparison to the wild type protein to determine which mutations have the most destabilizing structural implications, and (3) extend the analysis to pairwise mutations to decipher additive effects. Our analysis produces a ranking of significant mutations, which has some mutations that align with current studies and also identifies new mutations that are less studied but have potential for immune evasion. These results can be applied to the development of a vaccine for the Omicron variant and show the promise of our methodology in addressing targets of immune evasion for future viruses.