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Updated: Aug 30, 2025

Engineering Antiviral Agents via Surface Plasmon Resonance
Published on: June 14, 2022
Molecular dynamics simulations highlight the altered binding landscape at the spike-ACE2 interface between the Delta
Eleni Pitsillou1, Julia J Liang1, Raymond C Beh2
1Epigenomic Medicine, Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia; School of Science, STEM College, RMIT University, VIC, 3001, Australia.
Abstract:
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.529 variant (Omicron), represents a significant deviation in genetic makeup and function compared to previous variants. Following the BA.1 sublineage, the BA.2 and BA.3 Omicron subvariants became dominant, and currently the BA.4 and BA.5, which are quite distinct variants, have emerged. Using molecular dynamics simulations, we investigated the binding characteristics of the Delta and Omicron (BA.1) variants in comparison to wild-type (WT) at the interface of the spike protein receptor binding domain (RBD) and human angiotensin converting enzyme-2 (ACE2) ectodomain. The primary aim was to compare our molecular modelling systems with previously published observations, to determine the robustness of our approach for rapid prediction of emerging future variants. Delta and Omicron were found to bind to ACE2 with similar affinities (-39.4 and -43.3 kcal/mol, respectively) and stronger than WT (-33.5 kcal/mol). In line with previously published observations, the energy contributions of the non-mutated residues at the interface were largely retained between WT and the variants, with F456, F486, and Y489 having the strongest energy contributions to ACE2 binding. Further, residues N440K, Q498R, and N501Y were predicted to be energetically favourable in Omicron. In contrast to Omicron, which had the E484A and K417N mutations, intermolecular bonds were detected for the residue pairs E484:K31 and K417:D30 in WT and Delta, in accordance with previously published findings. Overall, our simplified molecular modelling approach represents a step towards predictive model systems for rapidly analysing arising variants of concern.
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