Investigating the mutational landscape of the SARS-CoV-2 Omicron variant via ab initio quantum mechanical modeling

SARS-CoV-2 variant "Omicron" B1.1.529 was first identified in South Africa in November 2021. Given the large number of mutations in Omicrons spike protein compared to the original Wuhan strain, its binding efficacy to the ACE2 receptor and its potential to escape antibodies are in the spotlight. Recently, we presented an ab initio quantum mechanical model to characterize the interactions of spike proteins Receptor Binding Domain (RBD) with select antibodies and ACE2 variants. The model identified weak links among the residues constituting interactions with the human ACE2 receptor (hACE2), and also enabled us to characterize in silico mutated RBDs to identify potential Variants of Concern (VOC). In particular, we focused on the role of RBD residue 484 in the interaction of the Delta variant with ACE2 and neutralizing antibodies (nAbs). In this report, we apply our model to the Omicron VOC, and characterize its interaction pattern with hACE2. Our results show that (i) binding affinity with hACE2, compared to Delta, is considerably increased, possibly contributing to increased infectivity. (ii) The interaction pattern between B1.1.529 and hACE2 differs from previous variants by shifting the hot-spot interaction residues on hACE2, and potentially affecting nAbs efficacy. (iii) A K mutation in the RBD residue 484 can further improve Omicrons binding of hACE2 and evasion of nAbs. Finally, we argue that a library of hot-spots for point-mutations can predict binding interaction energies of complex variants.

Anticipating future SARS-CoV-2 variants of concern through ab initio quantum mechanical modeling

Evolved SARS-CoV-2 variants are currently challenging the efficacy of first-generation vaccines, largely through the emergence of spike protein mutants. Among these variants, Delta is presently the most concerning. We employ an ab initio quantum mechanical model based on Density Functional Theory to characterize the spike protein Receptor Binding Domain (RBD) interaction with host cells and gain mechanistic insight into SARS-CoV-2 evolution. The approach is illustrated via a detailed investigation of the role of the E484K RBD mutation, a signature mutation of the Beta and Gamma variants. The simulation is employed to: predict the depleting effect of the E484K mutation on binding the RBD with select antibodies; identify residue E484 as a weak link in the original interaction with the human receptor hACE2; and describe SARS-CoV-2 Wuhan strand binding to the bat Rhinolophus macrotis ACE2 as more optimized than the human counterpart. Finally, we predict the hACE2 binding efficacy of a hypothetical E484K mutation added to the Delta variant RBD, identifying a potential future variant of concern. Results can be generalized to other mutations, and provide useful information to complement existing experimental datasets of the interaction between randomly generated libraries of hACE2 and viral spike mutants. We argue that ab initio modeling is at the point of being aptly employed to inform and predict events pertinent to viral and general evolution.