Fast and accurate determination of the relative binding affinities of small compounds to HIV-1 protease using non-equilibrium work

Ngo, Son Tung; Hung, Huynh Minh; Nguyen, Minh Tho

doi:10.1002/jcc.24502

Cited by 76 publications

(126 citation statements)

References 87 publications

Supporting

Mentioning

118

Contrasting

Order By: Relevance

“…The time-dependent pulling forces of these 47 systems are provided in Figure S3 of the SI. The form of pulling force curves are in good agreement with the previous studies, 27 in which the pulling forces continuously increase to maximum values before rapidly dropping to zero after the nonbonded contacts between the ligand to the protein were terminated. Here, the pulling work was selected as a criterion to rank the ligand-affinity (Figure 5Figure 5).…”

Section: Refining Docking Results Using Fpl Simulationssupporting

confidence: 90%

“…The obtained docking results were refined using the FPL method. 27 The FPL scheme is a very efficient technique to rapidly explore the binding affinity of a ligand to a protein, when the protein binding cavity accessible to the exogenous ligand without sizable conformational change during the binding/unbinding process. The FPL approach requires a small amount of computing resource, but it could provide results with high accuracy and precision.…”

Section: Refining Docking Results Using Fpl Simulationsmentioning

confidence: 99%

“…27 Details of the computations were referred to the previous studies. 27 In particular, the (x, y, z) dimensions of the systems are (9.83, 5.92, 8.70) nm as shown in Figure 1Figure 1. The systems in FPL simulations consists of 1 SARS-CoV-2 Mpro, 1 ligand, water molecules, and Na + ions for a total of ca.…”

Section: Free Energy Calculationmentioning

confidence: 99%

“…Corresponding Author * Email: ngosontung@tdtu.edu.vn; duchung.pham@cchmc.org and vanvu@ntt.edu.vn 18,27 .0 1,5 .0 2,24 .0 8,17 . 0 9,14 .0 21,26 Figure S1.…”

Section: Author Informationmentioning

confidence: 99%

“…20 Typically, the ligand-binding affinity of several thousand ligands to a protein is frequently predicted via the molecular docking method. 21 ; shortlist of these compounds would be then refined via more computationally expensive binding free energy methods such as the molecular mechanism/Poisson-Boltzmann surface area (MM/PBSA), [22][23][24] linear interaction energy (LIE), 25,26 or fast pulling of ligand (FPL) 27 approaches. The top-lead potential inhibitors will be finally validated through an accurate binding free energy approach such as the free energy perturbation (FEP), 28,29 thermodynamic integration (TI), 30,31 and non-equilibrium molecular dynamics simulations (NEMD).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Ngo

Pham

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Refining Docking Results Using Fpl Simulationssupporting

confidence: 90%

Section: Refining Docking Results Using Fpl Simulationsmentioning

confidence: 99%

Section: Free Energy Calculationmentioning

confidence: 99%

“…Corresponding Author * Email: ngosontung@tdtu.edu.vn; duchung.pham@cchmc.org and vanvu@ntt.edu.vn 18,27 .0 1,5 .0 2,24 .0 8,17 . 0 9,14 .0 21,26 Figure S1.…”

Section: Author Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Ngo

Pham

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Oversampling Free Energy Perturbation Simulation in Determination of the Ligand‐Binding Free Energy

et al. 2019

View full text Add to dashboard Cite

Determination of the ligand‐binding affinity is an extremely interesting problem. Normally, the free energy perturbation (FEP) method provides an appropriate result. However, it is of great interest to improve the accuracy and precision of this method. In this context, temperature replica exchange molecular dynamics implementation of the FEP computational approach, which we call replica exchange free energy perturbation (REP) was proposed. In particular, during REP simulations, the system can easily escape from being trapped in local minima by exchanging configurations with high temperatures, resulting in significant improvement in the accuracy and precision of protein–ligand binding affinity calculations. The distribution of the decoupling free energy was enlarged, and its mean values were decreased. This results in changes in the magnitude of the calculated binding free energies as well as in alteration in the binding mechanism. Moreover, the REP correlation coefficient with respect to experiment ( RREP = 0.85 ± 0.15) is significantly boosted in comparison with the FEP one ( RFEP = 0.64 ± 0.30). Furthermore, the root‐mean‐square error (RMSE) of REP is also smaller than FEP, RMSEREP = 4.28 ± 0.69 versus RMSEFEP = 5.80 ± 1.11 kcal/mol, respectively. © 2019 Wiley Periodicals, Inc.

show abstract

Accurate prediction of relative binding affinities of a series of HIV‐1 protease inhibitors using semi‐empirical quantum mechanical charge

Cheng

Wang

et al. 2020

J Comput Chem

View full text Add to dashboard Cite

A major challenge in computer‐aided drug design is the accurate estimation of ligand binding affinity. Here, a new approach that combines the adaptive steered molecular dynamics (ASMD) and partial atomic charges calculated by semi‐empirical quantum mechanics (SQMPC), namely ASMD‐SQMPC, is suggested to predict the ligand binding affinities, with 24 HIV‐1 protease inhibitors as testing examples. In the ASMD‐SQMPC, the relative binding free energy (ΔG) is reflected by the average maximum potential of mean force (max) between bound and unbound states. The correlation coefficient (R2) between the max and experimentally determined ΔG is 0.86, showing a significant improvement compared with the conventional ASMD (R2 = 0.52). Therefore, this study provides an efficient approach to predict the relative ΔG and reveals the significance of precise partial atomic charges in the theoretical simulations.

show abstract

Fast and accurate determination of the relative binding affinities of small compounds to HIV-1 protease using non-equilibrium work

Cited by 76 publications

References 87 publications

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

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

Oversampling Free Energy Perturbation Simulation in Determination of the Ligand‐Binding Free Energy

Accurate prediction of relative binding affinities of a series of HIV‐1 protease inhibitors using semi‐empirical quantum mechanical charge

Contact Info

Product

Resources

About