Biophysical principles predict fitness of SARS-CoV-2 variants

Abstract 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, our understanding of how RBD’s biophysical properties contribute to SARS-CoV-2’s epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the discovery of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by binding constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto a epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low frequency variants. 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. Significance Statement This research presents a biophysical model that maps the molecular properties of SARS-CoV-2’s receptor binding domain into an epistatic fitness landscape. By linking the binding affinities of the virus to its epidemic fitness, we offer a powerful tool for understanding and predicting the emergence and success of new viral variants. Our model, validated with real-world data and informed by theoretical insights, provides a foundation for interpreting the evolutionary trajectory of past pandemics and predicting those of the future. The adaptability of this biophysical model extends to the key proteins of other viruses as well, signifying its potential in guiding public health interventions, and advancing our understanding of viral evolution.