Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures.
Marco ZaccariaLuigi GenoveseWilliam DawsonViviana CristiglioTakahito NakajimaWelkin JohnsonMichael FarzanBabak MomeniPublished in: PNAS nexus (2022)
We employ a recently developed complexity-reduction quantum mechanical (QM-CR) approach, based on complexity reduction of density functional theory calculations, to characterize the interactions of the SARS-CoV-2 spike receptor binding domain (RBD) with ACE2 host receptors and antibodies. QM-CR operates via ab initio identification of individual amino acid residue's contributions to chemical binding and leads to the identification of the impact of point mutations. Here, we especially focus on the E484K mutation of the viral spike protein. We find that spike residue 484 hinders the spike's binding to the human ACE2 receptor (hACE2). In contrast, the same residue is beneficial in binding to the bat receptor Rhinolophus macrotis ACE2 (macACE2). In agreement with empirical evidence, QM-CR shows that the E484K mutation allows the spike to evade categories of neutralizing antibodies like C121 and C144. The simulation also shows how the Delta variant spike binds more strongly to hACE2 compared to the original Wuhan strain, and predicts that a E484K mutation can further improve its binding. Broad agreement between the QM-CR predictions and experimental evidence supports the notion that ab initio modeling has now reached the maturity required to handle large intermolecular interactions central to biological processes.
Keyphrases
- sars cov
- amino acid
- density functional theory
- molecular dynamics
- binding protein
- angiotensin ii
- angiotensin converting enzyme
- endothelial cells
- respiratory syndrome coronavirus
- magnetic resonance
- molecular dynamics simulations
- magnetic resonance imaging
- coronavirus disease
- high resolution
- dna binding
- small molecule
- monte carlo
- transcription factor
- induced pluripotent stem cells
- bioinformatics analysis
- pluripotent stem cells