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

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.

FEP-based screening prompts drug repositioning against COVID-19 (preprint)

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and, thus, repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a new virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a new restraint energy distribution (RED) function designed to accelerate the FEP-ABFE calculations and make the practical FEP-ABFE-based virtual screening of the existing drug library possible for the first time. As a result, out of twenty-five drugs predicted, fifteen were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (Ki=0.04 M) which has showed promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki=0.36 M) and chloroquine (Ki=0.56 M) were also found to potently inhibit SARS-CoV-2 Mpro for the first time. We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts. Significance StatementDrug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. It has been demonstrated that a new virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions can reach an unprecedently high hit rate, leading to successful identification of 16 potent inhibitors of SARS-CoV-2 main protease (Mpro) from computationally selected 25 drugs under a threshold of Ki = 4 M. The outcomes of this study are valuable for not only drug repurposing to treat COVID-19, but also demonstrating the promising potential of the FEP-ABFE prediction-based virtual screening approach.