SARS-CoV-2: lessons in virus mutation prediction and pandemic preparedness

Abstract

The COVID-19 pandemic has prompted an unprecedented global response. In particular, extraordinary efforts have been dedicated toward monitoring and predicting variant emergence due to its huge impact, particularly for vaccine escape. Broadly, we classify such methods into two categories: forward mutation prediction, where phenotypes are first observed and the responsible genotypes traced, and reverse mutation prediction, which starts with selected pathogen genetic profiles and characterizes their associated phenotypes. Reverse mutation prediction strategies have advantages in being able to sample a more complete evolutionary space since sequences that do not yet exist can be sampled. The rapid improvement in the maturity and scale of reverse mutation prediction strategies, such as deep mutational scanning, has led to significant amounts of data for machine learning, with concomitant improvement in the prediction results from computational tools. Such integrated prediction approaches are generalizable and offer significant opportunities for anticipating viral evolution and for pandemic preparedness.