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bioRxiv · 10.1101/2022.10.14.512203

Computational Prediction of Binding Affinities of Human Angiotensin Converting Enzyme-2 with SARS-CoV-2 Spike Protein Variants: Omicron Variants and Potentially Deleterious Mutations

Williams, A.·Zhan, C.-G.
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Abstract

The Omicron variant (BA.1) and its sub-variants of the SARS-CoV-2 virus which causes the COVID-19 disease continues to spread across the United States and the World at large. As new sub-variants of SARS-CoV-2 continue to proliferate, a reliable computational method of quickly determining the potential infectivity of these new variants is needed to assess their potential threat. In the present study, we have tested and validated an efficient computational protocol, which includes an efficient energy minimization and subsequent molecular mechanics/Poisson Boltzmann surface area (MM-PBSA) calculation of the binding free energy between the SARS-CoV-2 spike protein and human angiotensin converting enzyme-2 (ACE2), to predict the binding affinities of these spike/ACE2 complexes based upon the calculated binding free energies and a previously calibrated linear correlation relationship. The predicted binding affinities are in good agreement with available experimental data including those for Omicron variants, suggesting that the predictions based on this protocol should be reasonable. Further, we have investigated several hundred potential mutations of both the wildtype and Omicron variants of the SARS-CoV-2 spike protein. Based on the predicted binding affinity data, we have identified several mutations that have the potential to vastly increase the binding affinity of the spike protein to ACE2 within both the wildtype and Omicron variants. Author SummaryAs well known, the coronavirus responsible for COVID-19 disease enters human cells through its spike protein binding with a human receptor protein known as angiotensin converting enzyme-2. So, the binding affinity between the spike protein and angiotensin converting enzyme-2 contributes to the infectivity of the coronavirus and its variants. In this study, we demonstrated that a generally applicable, fast and easy-to-use computational protocol was able to accurately predict the binding affinity of angiotensin converting enzyme-2 with spike protein of the currently known variants of the coronavirus. Hence, we believe that this computational protocol may be used to reliably predict the binding affinity of angiotensin converting enzyme-2 with spike protein of new variants to be identified in the future. Using this computational protocol, we have further examined a number of possible single mutations on the spike protein of both the wildtype and Omicron variants and predicted their binding affinity with angiotensin converting enzyme-2, demonstrating that several mutations have the potential to vastly increase the binding affinity of the spike protein to angiotensin converting enzyme-2.

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Williams, A., Zhan, C.-G.. 2022-10-14. Computational Prediction of Binding Affinities of Human Angiotensin Converting Enzyme-2 with SARS-CoV-2 Spike Protein Variants: Omicron Variants and Potentially Deleterious Mutations. https://doi.org/10.1101/2022.10.14.512203

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