No abstract

The binding affinity of the SARS-CoV-2 spike (S)-protein ΔΔGbind to the human membrane protein ACE2 is critical for virus function and evolution. Computational structure-based screening of new S-protein mutations for ACE2 binding lends promise to rationalize virus function directly from protein structure and ideally aid early detection of potentially concerning variants. We used a computational protocol based on cryo-electron microscopy structures of the S-protein to estimate the ACE2-binding that gave good trend agreement with experimental ACE2 affinities. We then expanded predictions to all possible S-protein mutations in 21 different S-protein-ACE2 complexes (400,000 ΔΔGbind data points in total), using mutation group comparisons to reduce systematic errors. We show that mutations that have arisen in major variants as a group maintain ACE2 affinity significantly more than random mutations in the total protein, at the interface, and at evolvable sites, with differences between variant mutations being small relative to these effects. Omicron mutations as a group had a modest change in binding affinity compared to mutations in other major variants. The single-mutation effects are consistent with ACE2 binding being optimized and maintained in omicron, despite increased importance of other selection pressures (antigenic drift). As epistasis, glycosylation and in vivo conditions will modulate these effects, computational predictive SARS-CoV-2 evolution remains far from achieved, but the feasibility of large-scale computation is substantially aided by using many structures and comparison of mutation groups rather than single mutation effects, which are very uncertain. Our results demonstrate substantial challenges but indicate ways to improve the quality of computer models for assessing SARS-CoV-2 mutation effects.