Bridging Computational Predictions and Empirical Data: Insights into SARS-CoV-2 Mutational Impacts and Evolution

Abstract

The emergence of SARS-CoV-2 has unleashed a global health crisis, demanding advanced research into its genomic mutations and their consequences. Our study combines computational models and empirical validation to predict the effects of these mutations, aiming to understand their impact on the virus's behaviour, including its transmissibility and immune escape mechanisms. Utilising advanced prediction tools, we analysed mutations across the virus's genome, focusing on changes to both structural and non-structural proteins. This approach identified key mutations likely to influence protein functionality and the virus's evolution. Our findings, integrating computational predictions with real-world data, offer insights into SARS-CoV-2's evolutionary path and its implications for developing vaccines and therapies. We highlight the necessity of ongoing genomic surveillance and the combined use of computational and empirical methods to stay ahead of viral mutations. This study not only deepens our understanding of SARS-CoV-2 but also lays groundwork for future research on viral evolution and pandemic response strategies, emphasizing our approach's potential to inform public health decisions and research priorities.