Assessing potential inhibitors of SARS-CoV-2 main protease from available drugs using free energy perturbation simulations

Son Tung Ngo; Hung Minh Nguyen; Le Thi Thuy Huong; Pham Minh Quan; Vi Khanh Truong; Nguyen Thanh Tung; Van V. Vu

doi:10.1039/d0ra07352k

Assessing Potential Inhibitors for SARS-CoV-2 Main Protease from Available Drugs using Free Energy Perturbation Simulations

10.26434/chemrxiv.12771068 ◽

2020 ◽

Author(s):

Son Tung Ngo ◽

Hung Minh Nguyen ◽

Le Thi Thuy Huong ◽

Pham Minh Quan ◽

Vi Khanh Truong ◽

...

Keyword(s):

Free Energy ◽

Molecular Docking ◽

Binding Affinity ◽

Binding Free Energy ◽

Accurate Method ◽

Free Energy Perturbation ◽

Main Protease ◽

Energy Perturbation ◽

Novel Coronavirus ◽

Potential Inhibitors

<div> The main protease (Mpro) of the novel coronavirus SARS-CoV-2, which causes the COVID-19 pandemic, is responsible for the maturation of its key proteins. Thus, inhibiting SARS-CoV-2 Mpro could prevent SARS-CoV-2 from multiplying. Because new inhibitors require thorough validation, repurposing current drugs could help reduce the validation process. Many recent studies used molecular docking to screen large databases for potential inhibitors of SARS-CoV-2 Mpro. However, molecular docking does not consider molecular dynamics and thus can be prone to error. In this work, we developed a protocol using free energy perturbation (FEP) to assess the potential inhibitors of SARS-CoV-2 Mpro. We first tested both molecular docking and FEP on a set of 11 inhibitors of SARS-CoV-2 Mpro with experimentally determined inhibitory data. The experimentally deduced binding free energy exhibits significantly stronger correlation with that predicted by FEP (R = 0.94 ± 0.04) than with that predicted by molecular docking (R = 0.82 ± 0.08). This result clearly shows that FEP is the most accurate method available to estimate the binding affinity of a ligand to SARS-CoV-2 Mpro. We subsequently used FEP to validate the top 33 compounds screened with molecular docking from the ZINC15 database. Thirteen of these compounds were predicted to have strong binding affinity for SARS-CoV-2 Mpro, most of which are currently used as drugs for various diseases in humans. Notably, delamanid, an anti-tuberculosis drug, was predicted to inhibit SARS-CoV-2 Mpro in the nanomolar range. Because both COVID-19 and tuberculosis are lung diseases, delamanid has higher probability to be suitable for treating COVID-19 than other predicted compounds. Analysis of the interactions between SARS-CoV-2 Mpro and the top inhibitors revealed the key residues involved in the binding, including the catalytic dyad His14 and Cys145, which is consistent with the structural studies reported recently. </div>

Assessing Potential Inhibitors for SARS-CoV-2 Main Protease from Available Drugs using Free Energy Perturbation Simulations

10.26434/chemrxiv.12771068.v2 ◽

2020 ◽

Author(s):

Son Tung Ngo ◽

Hung Minh Nguyen ◽

Le Thi Thuy Huong ◽

Pham Minh Quan ◽

Vi Khanh Truong ◽

...

Keyword(s):

Free Energy ◽

Molecular Docking ◽

Binding Affinity ◽

Binding Free Energy ◽

Accurate Method ◽

Free Energy Perturbation ◽

Main Protease ◽

Energy Perturbation ◽

Novel Coronavirus ◽

Potential Inhibitors

<div> The main protease (Mpro) of the novel coronavirus SARS-CoV-2, which causes the COVID-19 pandemic, is responsible for the maturation of its key proteins. Thus, inhibiting SARS-CoV-2 Mpro could prevent SARS-CoV-2 from multiplying. Because new inhibitors require thorough validation, repurposing current drugs could help reduce the validation process. Many recent studies used molecular docking to screen large databases for potential inhibitors of SARS-CoV-2 Mpro. However, molecular docking does not consider molecular dynamics and thus can be prone to error. In this work, we developed a protocol using free energy perturbation (FEP) to assess the potential inhibitors of SARS-CoV-2 Mpro. We first tested both molecular docking and FEP on a set of 11 inhibitors of SARS-CoV-2 Mpro with experimentally determined inhibitory data. The experimentally deduced binding free energy exhibits significantly stronger correlation with that predicted by FEP (R = 0.94 ± 0.04) than with that predicted by molecular docking (R = 0.82 ± 0.08). This result clearly shows that FEP is the most accurate method available to estimate the binding affinity of a ligand to SARS-CoV-2 Mpro. We subsequently used FEP to validate the top 33 compounds screened with molecular docking from the ZINC15 database. Thirteen of these compounds were predicted to have strong binding affinity for SARS-CoV-2 Mpro, most of which are currently used as drugs for various diseases in humans. Notably, delamanid, an anti-tuberculosis drug, was predicted to inhibit SARS-CoV-2 Mpro in the nanomolar range. Because both COVID-19 and tuberculosis are lung diseases, delamanid has higher probability to be suitable for treating COVID-19 than other predicted compounds. Analysis of the interactions between SARS-CoV-2 Mpro and the top inhibitors revealed the key residues involved in the binding, including the catalytic dyad His14 and Cys145, which is consistent with the structural studies reported recently. </div>

Assessing Potential Inhibitors for SARS-CoV-2 Main Protease from Available Drugs using Free Energy Perturbation Simulations

10.26434/chemrxiv.12771068.v1 ◽

2020 ◽

Author(s):

Son Tung Ngo ◽

Hung Minh Nguyen ◽

Le Thi Thuy Huong ◽

Pham Minh Quan ◽

Vi Khanh Truong ◽

...

Keyword(s):

Free Energy ◽

Virtual Screening ◽

Binding Affinity ◽

Free Energy Perturbation ◽

Screening Approach ◽

Main Protease ◽

Energy Perturbation ◽

Potential Inhibitors ◽

Virtual Screening Approach

A virtual screening approach using docking and free energy pertubation was successfully validated with previously characterized inhibitors of SARS-CoV-2 main protease (Mpro). This approach and then used to estimate the binding affinity to Mpro of more than 6300 compounds in the ZINC15 database. Delamanid, an anti-tuberculosis agent, has a predicted nanomolar binding affinity for SARS-CoV-2 Mpro and is thus a promissing drug candiate for COVID-19. In addition, several compounds including three antibiotics exhibits femtomolar affinity for SARS-CoV-2 Mpro. The residues around positions 24, 45, 143, 165, and 190 were found to be involved in the binding of the strongest inhibitors.

Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease

10.26434/chemrxiv.12111297 ◽

2020 ◽

Author(s):

Son Tung Ngo ◽

Ngoc Quynh Anh Pham ◽

Ly Le ◽

Duc-Hung Pham ◽

Van Vu

Keyword(s):

Free Energy ◽

Molecular Docking ◽

Drug Targets ◽

Binding Free Energy ◽

Natural Compounds ◽

Main Protease ◽

Energy Perturbation ◽

Novel Coronavirus ◽

Potential Inhibitors ◽

Hiv 1

The novel coronavirus (SARS-CoV-2) has infected over 850,000 people and caused more than 42000 deaths worldwide as of April 1st, 2020. As the disease is spreading rapidly all over the world, it is urgent to find effective drugs to treat the virus. The main protease (Mpro) of SARS-CoV-2 is one of the potential drug targets. In this work, we used rigorous computational methods, including molecular docking, fast pulling of ligand (FPL), and free energy perturbation (FEP), to investigate potential inhibitors of SARS-CoV-2 Mpro. We first tested our approach with three reported inhibitors of SARS-CoV-2 Mpro; and our computational results are in good agreement with the respective experimental data. Subsequently, we applied our approach on a databases of ~4600 natural compounds found in Vietnamese plants, as well as 8 available HIV-1 protease (PR) inhibitors and an aza-peptide epoxide. Molecular docking resulted in a short list of 35 natural compounds, which was subsequently refined using the FPL scheme. FPL simulations resulted in five potential inhibitors, including 3 natural compounds and two available HIV-1 PR inhibitors. Finally, FEP, the most accurate and precise method, was used to determine the absolute binding free energy of these five compounds. FEP results indicate that two natural compounds, cannabisin A and isoacteoside, and an HIV-1 PR inhibitor, darunavir, exhibit large binding free energy to SARS-CoV-2 Mpro, which is larger than that of 13b, the most reliable SARS-CoV-2 Mpro inhibitor recently reported. The binding free energy largely arises from van der Waals (vdW) interaction. We also found that Glu166 form H-bonds to all the inhibitors. Replacing Glu166 by an alanine residue leads to ~ 2.0 kcal/mol decreases in the affinity of darunavir to SARS-CoV-2 Mpro. Our results could contribute to the development of potentials drugs inhibiting SARS-CoV-2.

Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease

10.26434/chemrxiv.12111297.v2 ◽

2020 ◽

Author(s):

Son Tung Ngo ◽

Ngoc Quynh Anh Pham ◽

Ly Le ◽

Duc-Hung Pham ◽

Van Vu

Keyword(s):

Free Energy ◽

Molecular Docking ◽

Drug Targets ◽

Binding Free Energy ◽

Natural Compounds ◽

Main Protease ◽

Energy Perturbation ◽

Novel Coronavirus ◽

Potential Inhibitors ◽

Hiv 1

The novel coronavirus (SARS-CoV-2) has infected over 850,000 people and caused more than 42000 deaths worldwide as of April 1st, 2020. As the disease is spreading rapidly all over the world, it is urgent to find effective drugs to treat the virus. The main protease (Mpro) of SARS-CoV-2 is one of the potential drug targets. In this work, we used rigorous computational methods, including molecular docking, fast pulling of ligand (FPL), and free energy perturbation (FEP), to investigate potential inhibitors of SARS-CoV-2 Mpro. We first tested our approach with three reported inhibitors of SARS-CoV-2 Mpro; and our computational results are in good agreement with the respective experimental data. Subsequently, we applied our approach on a databases of ~4600 natural compounds found in Vietnamese plants, as well as 8 available HIV-1 protease (PR) inhibitors and an aza-peptide epoxide. Molecular docking resulted in a short list of 35 natural compounds, which was subsequently refined using the FPL scheme. FPL simulations resulted in five potential inhibitors, including 3 natural compounds and two available HIV-1 PR inhibitors. Finally, FEP, the most accurate and precise method, was used to determine the absolute binding free energy of these five compounds. FEP results indicate that two natural compounds, cannabisin A and isoacteoside, and an HIV-1 PR inhibitor, darunavir, exhibit large binding free energy to SARS-CoV-2 Mpro, which is larger than that of 13b, the most reliable SARS-CoV-2 Mpro inhibitor recently reported. The binding free energy largely arises from van der Waals (vdW) interaction. We also found that Glu166 form H-bonds to all the inhibitors. Replacing Glu166 by an alanine residue leads to ~ 2.0 kcal/mol decreases in the affinity of darunavir to SARS-CoV-2 Mpro. Our results could contribute to the development of potentials drugs inhibiting SARS-CoV-2.

Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease

10.26434/chemrxiv.12111297.v1 ◽

2020 ◽

Author(s):

Son Tung Ngo ◽

Ngoc Quynh Anh Pham ◽

Ly Le ◽

Duc-Hung Pham ◽

Van Vu

Keyword(s):

Free Energy ◽

Molecular Docking ◽

Drug Targets ◽

Binding Free Energy ◽

Natural Compounds ◽

Main Protease ◽

Energy Perturbation ◽

Novel Coronavirus ◽

Potential Inhibitors ◽

Hiv 1

The novel coronavirus (SARS-CoV-2) has infected over 850,000 people and caused more than 42000 deaths worldwide as of April 1st, 2020. As the disease is spreading rapidly all over the world, it is urgent to find effective drugs to treat the virus. The main protease (Mpro) of SARS-CoV-2 is one of the potential drug targets. In this work, we used rigorous computational methods, including molecular docking, fast pulling of ligand (FPL), and free energy perturbation (FEP), to investigate potential inhibitors of SARS-CoV-2 Mpro. We first tested our approach with three reported inhibitors of SARS-CoV-2 Mpro; and our computational results are in good agreement with the respective experimental data. Subsequently, we applied our approach on a databases of ~4600 natural compounds found in Vietnamese plants, as well as 8 available HIV-1 protease (PR) inhibitors and an aza-peptide epoxide. Molecular docking resulted in a short list of 35 natural compounds, which was subsequently refined using the FPL scheme. FPL simulations resulted in five potential inhibitors, including 3 natural compounds and two available HIV-1 PR inhibitors. Finally, FEP, the most accurate and precise method, was used to determine the absolute binding free energy of these five compounds. FEP results indicate that two natural compounds, cannabisin A and isoacteoside, and an HIV-1 PR inhibitor, darunavir, exhibit large binding free energy to SARS-CoV-2 Mpro, which is larger than that of 13b, the most reliable SARS-CoV-2 Mpro inhibitor recently reported. The binding free energy largely arises from van der Waals (vdW) interaction. We also found that Glu166 form H-bonds to all the inhibitors. Replacing Glu166 by an alanine residue leads to ~ 2.0 kcal/mol decreases in the affinity of darunavir to SARS-CoV-2 Mpro. Our results could contribute to the development of potentials drugs inhibiting SARS-CoV-2.

Potent Noncovalent Inhibitors of the Main Protease of SARS-CoV-2 from Molecular Sculpting of the Drug Perampanel Guided by Free Energy Perturbation Calculations

ACS Central Science ◽

10.1021/acscentsci.1c00039 ◽

2021 ◽

Vol 7 (3) ◽

pp. 467-475 ◽

Cited By ~ 1

Author(s):

Chun-Hui Zhang ◽

Elizabeth A. Stone ◽

Maya Deshmukh ◽

Joseph A. Ippolito ◽

Mohammad M. Ghahremanpour ◽

...

Keyword(s):

Free Energy ◽

Free Energy Perturbation ◽

Main Protease ◽

Energy Perturbation

Quinoline and Quinazoline Alkaloids against COVID-19: An In Silico Multitarget Approach

Journal of Chemistry ◽

10.1155/2021/3613268 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Esraa M. O. A. Ismail ◽

Shaza W. Shantier ◽

Mona S. Mohammed ◽

Hassan H. Musa ◽

Wadah Osman ◽

...

Keyword(s):

Antimalarial Activity ◽

The Novel ◽

Socioeconomic Impacts ◽

Health Crisis ◽

Natural Sources ◽

Angiotensin Converting Enzyme 2 ◽

Main Protease ◽

Recent Outbreak ◽

Novel Coronavirus ◽

Potential Inhibitors

The recent outbreak of the highly contagious coronavirus disease 2019 (COVID-19) caused by the novel coronavirus SARS-CoV-2 has created a global health crisis with socioeconomic impacts. Although, recently, vaccines have been approved for the prevention of COVID-19, there is still an urgent need for the discovery of more efficacious and safer drugs especially from natural sources. In this study, a number of quinoline and quinazoline alkaloids with antiviral and/or antimalarial activity were virtually screened against three potential targets for the development of drugs against COVID-19. Among seventy-one tested compounds, twenty-three were selected for molecular docking based on their pharmacokinetic and toxicity profiles. The results identified a number of potential inhibitors. Three of them, namely, norquinadoline A, deoxytryptoquivaline, and deoxynortryptoquivaline, showed strong binding to the three targets, SARS-CoV-2 main protease, spike glycoprotein, and human angiotensin-converting enzyme 2. These alkaloids therefore have promise for being further investigated as possible multitarget drugs against COVID-19.

Optimal Designs of Pairwise Calculation: an Application to Free Energy Perturbation in Minimizing Prediction Variability

10.26434/chemrxiv.7965140 ◽

2019 ◽

Author(s):

Qingyi Yang ◽

Woodrow W. Burchett ◽

Gregory S. Steeno ◽

David L. Mobley ◽

Xinjun Hou

Keyword(s):

Free Energy ◽

Least Squares ◽

Binding Affinity ◽

Binding Free Energy ◽

The Other ◽

Computer Hardware ◽

Free Energy Perturbation ◽

Implicit Assumption ◽

Reference Ligand ◽

Energy Perturbation

Predicting binding free energy of ligand-protein complexes has been a grand challenge in the field of computational chemistry since the early days of molecular modeling. Multiple computational methodologies exist to predict ligand binding affinities. Pathway-based Free Energy Perturbation (FEP), Thermodynamic Integration (TI) as well as Linear Interaction Energy (LIE), and Molecular Mechanics-Poisson Boltzmann/Generalized Born Surface Area (MM-PBSA/GBSA) have been applied to a variety of biologically relevant problems and achieved different levels of predictive accuracy. Recent advancements in computer hardware and simulation algorithms of molecular dynamics and Monte Carlo sampling, as well as improved general force field parameters, have made FEP a principal approach for calculating the free energy differences, especially when calculating the host-guest binding affinity differences upon chemical modification. Since the FEP-calculated binding free energy difference, denoted ddGFEP only characterizes the difference in free energy between pairs of ligands or complexes, not the absolute binding free energy value of each individual host-guest system, denoted dG, we examine two rarely asked questions in FEP application: 1) Which values would be more appropriate as the prediction to assess the ligands prospectively: the calculated pairwise free energy differences, ddGFEP, or the estimated absolute binding energies, d^G, transformed from ddGFEP? 2) In the situation where only a limited number of ligand pairs can be calculated in FEP, can the perturbation pairs be optimally selected with respect to the reference ligand(s) to maximize the prediction precision? These two questions underline the viability of an often-neglected assumption in pairwise comparisons: that the pairwise value is sufficient to make a quantitative and reliable characterization of an individual ligand's properties or activities. This implicit assumption would be true if there was no error in each pairwise calculation. Recently pair designs such as multiple pathways or cycle closure analyses provided calculation error estimation but did not address the statistical impact of the two questions above. The error impact is fully minimized by conducting an exhaustive study that obtains all NC2 = N(N-1)/2 pairs for a set N molecules; more if there is directionality (dGi,j != dGj,i). Obviously, that study design is impractical and unnecessary. Thus, we desire to collect the right amount of data that is 1) feasibly attainable, 2) topologically sufficient, and 3) mathematically synthesizable so that we can mitigate inherent calculation errors and have higher confidence in our conclusions. The significance of above questions can be illustrated by a motivating example shown in Figure 1 and Table 1, which considers two different perturbation graph designs for 20 ligands with the same number of FEP perturbation pairs, 19, and the same reference, Ligand 1. These two designs reached different conclusions in rank ordering ligand potencies due to errors inherent in the FEP derived estimates. Based on design A, ligands 5, 7, 14, 15 would be selected as the best four (20%) picks since those d^G estimates are the most favorable. Design B would yield ligands 5, 12, 18, 19 as best for the same reason. Without knowing the true value, dGTrue of the other 19 ligands, we lack a prospective metric to assess which design could be more precise even though, retrospectively, we know that both designs had reasonably good agreement with the true values, as measured through correlation and error metrics. However, the top picks from neither design were consistent with the true top four ligands, which are ligands 7, 10, 12, 18. Yet, if all of the 20C2 =190 pairs could have been calculated as listed in the last column of Table 1, the best four ligands would have been correctly identified. Additionally, the other metrics included in Table 1 were significantly improved. However, as mentioned above, calculating all possible pairs, or even a significant fraction of all possible pairs, is unlikely in practice, especially when number of molecules are large. Given this restriction, is it possible to objectively determine whether design A or B will give more precise predictions? In this report, we investigated the performance of the calculated ddGFEP values compared to the pairwise differences in least squares derived d^G estimates both analytically and through simulations. Based on our findings, we recommend applying weighted least squares to transforming ddGFEP values into d^G estimates. Second, we investigated the factors that contribute to the precision of the d^G estimates, such as the total number of computed pairs, the selection of computed pairs, and the uncertainty in the computed ddGFEP values. The mean squared error, denoted MSE and Spearman's rank correlation, are used as performance metrics. To illustrate, we demonstrated how the structural similarity can be included in design and its potential impact on prediction precision. As in the majority of reported FEP studies on binding affinity prediction, the ddGFEP pairs were selected based on chemical structure similarity. Pairs with small chemical differences are assumed to be more likely to have smaller errors in ddGFEP calculation. Together using the constructed mathematic system and literature examples, we demonstrate that some of pair-selection schemes (designs) are better than the others. To minimize the prediction uncertainty, it is recommended to wisely select design optimality criterion to suit practical applications accordingly.

Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.0c00449 ◽

2020 ◽

Vol 60 (11) ◽

pp. 5563-5579 ◽

Cited By ~ 2

Author(s):

Francesca Deflorian ◽

Laura Perez-Benito ◽

Eelke B Lenselink ◽

Miles Congreve ◽

Herman W. T. van Vlijmen ◽

...

Keyword(s):

Free Energy ◽

Ligand Binding ◽

Binding Affinity ◽

Accurate Prediction ◽

Free Energy Perturbation ◽

Energy Perturbation