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

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 (preprint)

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.

Discovery of SARS-CoV-2 Mpro Peptide Inhibitors from Modelling Substrate and Ligand Binding (preprint)

The main protease (Mpro) of SARS-CoV-2 is central to its viral lifecycle and is a promising drug target, but little is known concerning structural aspects of how it binds to its 11 natural cleavage sites. We used biophysical and crystallographic data and an array of classical molecular mechanics and quantum mechanical techniques, including automated docking, molecular dynamics (MD) simulations, linear-scaling DFT, QM/MM, and interactive MD in virtual reality, to investigate the molecular features underlying recognition of the natural Mpro substrates. Analyses of the subsite interactions of modelled 11-residue cleavage site peptides, ligands from high-throughput crystallography, and designed covalently binding inhibitors were performed. Modelling studies reveal remarkable conservation of hydrogen bonding patterns of the natural Mpro substrates, particularly on the N-terminal side of the scissile bond. They highlight the critical role of interactions beyond the immediate active site in recognition and catalysis, in particular at the P2/S2 sites. The binding modes of the natural substrates, together with extensive interaction analyses of inhibitor and fragment binding to Mpro, reveal new opportunities for inhibition. Building on our initial Mpro-substrate models, computational mutagenesis scanning was employed to design peptides with improved affinity and which inhibit Mpro competitively. The combined results provide new insight useful for the development of Mpro inhibitors.

Microscopic Factors Modulating the Interactions Between the SARS-CoV-2 Main Protease and α−Ketoamide Inhibitors (preprint)

We performed 10 ns scale molecular dynamics simulations of 6 SARS-CoV-2 main protease/alpha-ketoamide inhibitor complexes in aqueous solution, in the phase before the inhibitor covalently binds to the protease's catalytic cysteine, using a polarizable multi-scale molecular modeling approach. For each simulation, 100 Mpro/inhibitor snapshots(about 4 800 atoms) were extracted along the last 2 ns simulation segments. They were post processed using a fully quantum mechanical O(N) approach to decompose the protease in sets of fragments from which we computed the mean local interaction energies between the inhibitors and the different pockets of the protease catalytic domain. Contrary to earlier results, our analysis shows that the protease pocket S2 to be a key anchoring site able to lock within the catalytic domain an alpha-ketoamide inhibitor even before covalent bonding to the protease catalytic cysteine occurs. To target that pocket our computations suggest to consider hydrophobic groups, like cyclo-propyl or cyclo-hexyl.