Insights from a computational analysis of the SARS-CoV-2 Omicron variant: Host-pathogen interaction, pathogenicity, and possible drug therapeutics

Abstract

[Introduction] Prominently accountable for the upsurge of COVID-19 cases as the world attempts to recover from the previous two waves, Omicron has further threatened the conventional therapeutic approaches. The lack of extensive research regarding Omicron has raised the need to establish correlations to understand this variant by structural comparisons. Here, we evaluate, correlate, and compare its genomic sequences through an immunoinformatic approach to understand its epidemiological characteristics and responses to existing drugs. [Methods] We reconstructed the phylogenetic tree and compared the mutational spectrum. We analyzed the mutations that occurred in the Omicron variant and correlated how these mutations affect infectivity and pathogenicity. Then, we studied how mutations in the receptor-binding domain affect its interaction with host factors through molecular docking. Finally, we evaluated the drug efficacy against the main protease of the Omicron through molecular docking and validated the docking results with molecular dynamics simulation. [Results] Phylogenetic and mutational analysis revealed the Omicron variant is similar to the highly infectious B.1.620 variant, while mutations within the prominent proteins are hypothesized to alter its pathogenicity. Moreover, docking evaluations revealed significant differences in binding affinity with human receptors, angiotensin-converting enzyme 2 and NRP1. Surprisingly, most of the tested drugs were proven to be effective. Nirmatrelvir, 13b, and Lopinavir displayed increased effectiveness against Omicron. [Conclusion] Omicron variant may be originated from the highly infectious B.1.620 variant, while it was less pathogenic due to the mutations in the prominent proteins. Nirmatrelvir, 13b, and Lopinavir would be the most effective, compared to other promising drugs that were proven effective.