Prediction of Prospective Mutational Landscape of SARS-CoV-2 Spike ssRNA and Evolutionary Basis of Its Host Interaction.

Booster doses are crucial against severe COVID-19, as rapid virus mutations and variant emergence prolong the pandemic crisis. The virus's quick evolution, short generation-time, and adaptive changes impact virulence and evolvability, helping predictions about variant of concerns' (VOCs') landscapes. Here, in this study, we used a new computational algorithm, to predict the mutational pattern in SARS-CoV-2 ssRNA, proteomics, structural identification, mutation stability, and functional correlation, as well as immune escape mechanisms. Interestingly, the sequence diversity of SARS Coronavirus-2 has demonstrated a predominance of G- > A and C- > U substitutions. The best validation statistics are explored here in seven homologous models of the expected mutant SARS-CoV-2 spike ssRNA and employed for hACE2 and IgG interactions. The interactome profile of SARS-CoV-2 spike with hACE2 and IgG revealed a strong correlation between phylogeny and divergence time. The systematic adaptation of SARS-CoV-2 spike ssRNA influences infectivity and immune escape. Data suggest higher propensity of Adenine rich sequence promotes MHC system avoidance, preferred by A-rich codons. Phylogenetic data revealed the evolution of SARS-CoV-2 lineages' epidemiology. Our findings may unveil processes governing the genesis of immune-resistant variants, prompting a critical reassessment of the coronavirus mutation rate and exploration of hypotheses beyond mechanical aspects.