Genetic Algorithm-Based Prediction of Emerging SARS-CoV-2 Variants: A Computational Biology Perspective

The emergence of Variants of Concern in infectious diseases, particularly in the context of viruses like SARS-CoV-2, has highlighted the critical importance of continuous prediction and monitoring, showcasing the pivotal role of computational biology in addressing the challenges posed by these emerging infectious diseases. This study advocates for implementing a computational approach able to predict the next SARS-CoV-2 variant of concern (VOC). To that end, inspired by natural selection principles, we used the Genetic Algorithm (GA) as it offers a potent framework for optimizing complex problems. We initiated our investigation with the Wuhan spike protein sequence since it is critical target for variant surveillance and used as reference input. Subsequently, we systematically introduced specific mutations into this sequence to make the initial population. Computational modeling generated three-dimensional structures of the mutated spike within the SARS-CoV-2 ACE2 to evaluate the best candidate of each generation. These were later evaluated by predicting their Gibbs free energy (ΔG values) to evaluate the stability and interactions of these mutants, providing insights into their potential effects on viral behavior and the emergence of VOC. Our analysis demonstrates that the ΔG of our predicted variant closely compares to the delta variant, indicating a similar thermodynamic profile in their interactions. Moreover, our finding indicates that the transmission potential of the new variant is nearly on par with that of the delta variants. Additional factors will be taken into account to evaluate the overall importance of our predicted variant, and we will undertake further research and analysis to comprehend its real-world consequences and potential advantages or drawbacks.