Biophysical principles predict fitness of SARS-CoV-2 variants

SARS-CoV-2 employs its spike protein's receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, understanding how RBD's biophysical properties contribute to SARS-CoV-2 epidemiological fitness remains largely unexplored. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the discovery of a fitness function based on protein folding and binding thermodynamics, we unravel the relationship between the fitness contribution of the RBD and its biophysical properties. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by binding constants to cell receptors and antibodies, onto the fitness landscape for variants ranging from the ancestral Wuhan Hu-1 to the Omicron BA.1. We validate our findings through experimentally measured binding affinities and population data on frequencies of variants. Our model forms the basis for a comprehensive epistatic map, relating the genotype space to fitness. Our study thus delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low frequency variants, and sheds light on the impact of specific mutations on viral fitness. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics.