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

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.

scholarly journals Identify potent SARS-CoV-2 main protease inhibitors via accelerated free energy perturbation-based virtual screening of existing drugs

2020 ◽  
Vol 117 (44) ◽  
pp. 27381-27387 ◽  
Author(s):  
Zhe Li ◽  
Xin Li ◽  
Yi-You Huang ◽  
Yaoxing Wu ◽  
Runduo Liu ◽  
...  
Keyword(s):  
Free Energy ◽  
Virtual Screening ◽  
Binding Free Energy ◽  
Drug Repurposing ◽  
Free Energy Perturbation ◽  
Therapeutic Effects ◽  
Screening Approach ◽  
Main Protease ◽  
Energy Perturbation ◽  
Virtual Screening Approach

The 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 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 restraint energy distribution (RED) function, making the practical FEP-ABFE−based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (inhibitory constant Ki= 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki= 0.36 µM) and chloroquine (Ki= 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro. We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.

scholarly journals Identify potent SARS-CoV-2 main protease inhibitors via accelerated free energy perturbation-based virtual screening of existing drugs

2020 ◽  
Author(s):  
Zhe Li ◽  
Xin Li ◽  
Yi-You Huang ◽  
Yaoxing Wu ◽  
Runduo Liu ◽  
...  
Keyword(s):  
Free Energy ◽  
Virtual Screening ◽  
Binding Free Energy ◽  
Drug Repurposing ◽  
Free Energy Perturbation ◽  
Hit Rate ◽  
Screening Approach ◽  
Main Protease ◽  
Energy Perturbation ◽  
Virtual Screening Approach

AbstractCoronavirus 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 μM) which has showed promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki=0.36 μM) and chloroquine (Ki=0.56 μM) 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 μM. 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.

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

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> <p>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.</p> </div> <br>

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

RSC Advances ◽  
2020 ◽  
Vol 10 (66) ◽  
pp. 40284-40290
Author(s):  
Son Tung Ngo ◽  
Hung Minh Nguyen ◽  
Le Thi Thuy Huong ◽  
Pham Minh Quan ◽  
Vi Khanh Truong ◽  
...  
Keyword(s):  
Free Energy ◽  
Binding Affinity ◽  
Free Energy Perturbation ◽  
The Novel ◽  
Main Protease ◽  
Energy Perturbation ◽  
Novel Coronavirus ◽  
Potential Inhibitors

Free Energy Pertubation (FEP) can be used to accurately predict the binding affinity of a ligand to the main protease (Mpro) of the novel coronavirus SARS-CoV-2.

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

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> <p>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.</p> </div> <br>

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

2021 ◽  
Vol 7 (3) ◽  
pp. 467-475 ◽  
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

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

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.<br><br>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:<br><br>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?<br>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?<br><br>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.<br><br>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?<br><br>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.<br><br>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<br>practical applications accordingly.<br>

Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation

2020 ◽  
Vol 60 (11) ◽  
pp. 5563-5579 ◽  
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

Identification of potential inhibitors against pathogenic missense mutations of PMM2 using a structure-based virtual screening approach

2020 ◽  
Vol 39 (1) ◽  
pp. 171-187 ◽  
Author(s):  
D. Thirumal Kumar ◽  
Nikita Jain ◽  
S. Udhaya Kumar ◽  
C. George Priya Doss ◽  
Hatem Zayed
Keyword(s):  
Virtual Screening ◽  
Missense Mutations ◽  
Screening Approach ◽  
Potential Inhibitors ◽  
Virtual Screening Approach
Sign in / Sign up

Export Citation Format

Share Document