Computational Estimation of Potential Inhibitors from the Known Drugs against the Main Protease of SARS-CoV-2

Tam, Nguyen Minh; Quân, Phạm Minh; Ha, Nguyen Xuan; Nam, Pham Cam; Phung, Huong Thi Thu

doi:10.26434/chemrxiv.14102675

“…21 Numerous investigations were thus performed to characterize a potential inhibitor for SARS-CoV-2 Mpro. [22][23][24][25][26][27][28][29][30][31][32][33][34] Several compounds were suggested to be able to inhibit the biological activity of Mpro. In this context, a compound, named PF-07321332, emerges as one of the most potent candidates for an oral antiviral therapeutic factor.…”

Section: Introductionmentioning

confidence: 99%

Insights into the Binding and Covalent Inhibition Mechanism of PF-07321332 to SARS-CoV-2 Mpro

Ngo

¹

,

Nguyen

²

,

Tùng

³

et al. 2021

Preprint

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been causing the COVID-19 pandemic resulting in several million death were reported. Numerous investigations have been carried out to discover a compound that can inhibit the biological activity of SARS-CoV-2 main protease, which is an enzyme related to the viral replication. Among these, PF-07321332 is currently under clinical trial for COVID-19 therapy. Therefore, in this work, atomistic and electronic simulations were performed to unravel the binding and covalent inhibition mechanism of the compound to Mpro. Initially, 5 µs of steered-molecular dynamics simulations were carried out to evaluate the ligand-binding process to SARS-CoV-2 Mpro. Successfully generated bound state between two molecules showed the important role of the PF-07321332 pyrrolidinyl group and the residues Glu166 and Gln189 in the ligand-binding process. Moreover, from the MD-refined structure, quantum mechanics/molecular mechanics (QM/MM) calculations were carried out to unravel the reaction mechanism for the formation of thioimidate product from SARS-CoV-2 Mpro and PF07321332 inhibitor. We found that the catalytic triad Cys145–His41–Asp187 of SARS-CoV-2 Mpro plays important role in the activation of PF-07321332 covalent inhibitor, which renders the deprotonation of Cys145 and, thus, facilitates further reaction. Our results are definitely beneficial for better understanding on the inhibition mechanism and designing new effective inhibitors for SARS-CoV-2 Mpro.

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“…21 Numerous investigations were thus performed to characterize a potential inhibitor for SARS-CoV-2 Mpro. [22][23][24][25][26][27][28][29][30][31][32][33][34] Several compounds were suggested to be able to inhibit the biological activity of Mpro. In this context, a compound, named PF-07321332, emerges as one of the most potent candidates for an oral antiviral therapeutic factor.…”

Section: Introductionmentioning

confidence: 99%

Insights into the Binding and Covalent Inhibition Mechanism of PF-07321332 to SARS-CoV-2 Mpro

Ngo

¹

,

Nguyen

²

,

Tùng

³

et al. 2021

Preprint

View full text Add to dashboard Cite

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been causing the COVID-19 pandemic resulting in several million death were reported. Numerous investigations have been carried out to discover a compound that can inhibit the biological activity of SARS-CoV-2 main protease, which is an enzyme related to the viral replication. Among these, PF-07321332 is currently under clinical trial for COVID-19 therapy. Therefore, in this work, atomistic and electronic simulations were performed to unravel the binding and covalent inhibition mechanism of the compound to Mpro. Initially, 5 µs of steered-molecular dynamics simulations were carried out to evaluate the ligand-binding process to SARS-CoV-2 Mpro. Successfully generated bound state between two molecules showed the important role of the PF-07321332 pyrrolidinyl group and the residues Glu166 and Gln189 in the ligand-binding process. Moreover, from the MD-refined structure, quantum mechanics/molecular mechanics (QM/MM) calculations were carried out to unravel the reaction mechanism for the formation of thioimidate product from SARS-CoV-2 Mpro and PF07321332 inhibitor. We found that the catalytic triad Cys145–His41–Asp187 of SARS-CoV-2 Mpro plays important role in the activation of PF-07321332 covalent inhibitor, which renders the deprotonation of Cys145 and, thus, facilitates further reaction. Our results are definitely beneficial for better understanding on the inhibition mechanism and designing new effective inhibitors for SARS-CoV-2 Mpro.

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“…39,40 However, before MD simulations were performed, because docking poses are required to use as MD initial conformations, molecular docking approaches are initially performed to preliminary estimate the ligand-binding pose and affinity. 41,42 Moreover, molecular docking approaches have also been used for screening a large database of compounds, 43,44 which can comprise several millions of compounds such as ZINC, 45 ChEMBL, 46 PubChem, 47 etc. Therefore, although their accuracy is not very high, molecular docking approaches play an important role in CADD.…”

mentioning

confidence: 99%

Improving the Accuracy of AutoDock Vina by Changing the Empirical Parameters

Pham

¹

,

Nguyen

²

,

Tâm

³

et al. 2021

Preprint

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According to the previous benchmark, Autodock Vina (Vina) achieved a very high successful-docking rate, p ̂, but give a rather a low correlation coefficient, R, for binding affinity with respect to experiment. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is a main objective of docking simulations. The accuracy of Vina likely depends on the empirical parameters, which include the Gaussian steric interaction, repulsion, hydrophobic, hydrogen bond, and rotation metrics. In this context, we evaluated the dependence of Vina accuracy upon empirical parameters. Although changing of six parameters alters the obtained R values, the gauss2 and rotation terms form more effects. The p ̂ terms are sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Therefore, three sets of empirical parameters were proposed as well as more to modestly focus on R (set1), modestly focus on p ̂ (set3), and keep a tradeoff between R and p ̂. The testing study over 800 complexes indicated that the Vina with proposed sets of parameters can provide more accurate results since forming the larger R value (R_set1=0.556±0.025) compared with the original one (R_Default=0.493±0.028) and Vina version 1.2 (R_(Vina 1.2)=0.503±0.029). Besides, the testing study over 48 biological targets indicated that the modified Vina can be applied more widely compared with the default package. These newly proposed parameters achieved a higher correlation coefficient and reasonable correlation coefficients (R>0.500) for at least 32 targets, whereas the default parameters provided by the original Vina gave only 31 targets with at least 0.500 correlation. In addition, validation calculations for 1315 complexes obtained from the version 2019 of PDBbind refined structures suggested that set1 of parameters are more appropriate than the other parameters (R_set1=0.621±0.016) compared with the default package (R_Default=0.552±0.018) and Vina version 1.2 (R_(Vina 1.2)=0.549±0.017). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes probably enhance the ranking of ligand-binding affinity using Autodock Vina.

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Computational Estimation of Potential Inhibitors from the Known Drugs against the Main Protease of SARS-CoV-2

Cited by 3 publications

References 52 publications

Insights into the Binding and Covalent Inhibition Mechanism of PF-07321332 to SARS-CoV-2 Mpro

Insights into the Binding and Covalent Inhibition Mechanism of PF-07321332 to SARS-CoV-2 Mpro

Insights into the Binding and Covalent Inhibition Mechanism of PF-07321332 to SARS-CoV-2 Mpro

Improving the Accuracy of AutoDock Vina by Changing the Empirical Parameters

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