Upgrading nirmatrelvir to inhibit SARS-CoV-2 Mpro via DeepFrag and free energy calculations.

The first oral drug for the treatment of COVID-19, Paxlovid, has been authorized; however, nirmatrelvir, a major component of the drug, is reported to be associated with some side effects. Moreover, the appearance of many novel variants raises concerns about drug resistance, and designing new potent inhibitors to prevent viral replication is thus urgent. In this context, using a hybrid approach combining machine learning (ML) and free energy simulations, 6 compounds obtained by modifying nirmatrelvir were proposed to bind strongly to SARS-CoV-2 Mpro. The structural modification of nirmatrelvir significantly enhances the electrostatic interaction free energy between the protein and ligand and slightly decreases the vdW term. However, the vdW term is the most important factor in controlling the ligand-binding affinity. In addition, the modified nirmatrelvir might be less toxic to the human body than the original inhibitor.

Correction: Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations.

[This corrects the article DOI: 10.1039/D0RA06212J.].

Correction: Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches.

Correction for 'Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches' by Son Tung Ngo et al., Phys. Chem. Chem. Phys., 2022, https://doi.org/10.1039/d2cp04476e.

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches.

Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro. In particular, molecular docking and molecular dynamics (MD) simulations are usually combined to predict the binding affinity of thousands of compounds. Quantitative structure-activity relationship (QSAR) is the least computationally demanding and therefore can be used for large chemical collections of ligands. However, its accuracy may not be high. Moreover, the quantum mechanics/molecular mechanics (QM/MM) method is most commonly used for covalently binding inhibitors, which also play an important role in inhibiting the activity of SARS-CoV-2. Furthermore, machine learning (ML) models can significantly increase the searching space of ligands with high accuracy for binding affinity prediction. Physical insights into the binding process can then be confirmed via physics-based calculations. Integration of ML models into computational chemistry provides many more benefits and can lead to new therapies sooner.

Assessing potential inhibitors of SARS-CoV-2 main protease from available drugs using free energy perturbation simulations† † Electronic supplementary information (ESI) available. See DOI: 10.1039/d0ra07352k

The main protease (Mpro) of the novel coronavirus SARS-CoV-2, which has caused 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. First, we validated 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 predict the binding affinity of SARS-CoV-2 Mpro + ligand complexes. 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 bind strongly to 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 complexes of 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. 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.

Binding of inhibitors to the monomeric and dimeric SARS-CoV-2 Mpro† † Electronic supplementary information (ESI) available: The binding pose of ligands to SARS-CoV-2 Mpro, pulling forces and works over 8 independent trajectories of SMD simulations, interaction diagram between inhibitors and SARS-CoV-2 Mpro, the binding pose of other inhibitors to SARS-CoV-2 Mpro, the pulling force and pulling work of other inhibitors to SARS-CoV-2 Mpro. See DOI: 10.1039/d0ra09858b

SARS-CoV-2 rapidly infects millions of people worldwide since December 2019. There is still no effective treatment for the virus, resulting in the death of more than one million patients. Inhibiting the activity of SARS-CoV-2 main protease (Mpro), 3C-like protease (3CLP), is able to block the viral replication and proliferation. In this context, our study has revealed that in silico screening for inhibitors of SARS-CoV-2 Mpro can be reliably done using the monomeric structure of the Mpro instead of the dimeric one. Docking and fast pulling of ligand (FPL) simulations for both monomeric and dimeric forms correlate well with the corresponding experimental binding affinity data of 24 compounds. The obtained results were also confirmed via binding pose and noncovalent contact analyses. Our study results show that it is possible to speed up computer-aided drug design for SARS-CoV-2 Mpro by focusing on the monomeric form instead of the larger dimeric one. Binding of inhibitors to the monomeric SARS-CoV-2 Mpro is similar to the dimeric one.

Insights into the binding and covalent inhibition mechanism of PF-07321332 to SARS-CoV-2 Mpro† † Electronic supplementary information (ESI) available: Includes a movie describing a representative binding process, the minimum distance between the non-hydrogen atoms of PF-07321332 and the non-hydrogen atoms of the catalytic dyad, the minimum distance dSγ–CN between the nitrile group of PF-07321332 and the sulfur atom of the Cys145 residue, the binding pathway of PF-07321332 to SARS-CoV-2 Mpro over the trajectories 0 and 20, the optimization starting from the ion pair Cys145−–His41H+, the optimized transition state for the hydrolysis of Int-3, and energy and Cartesian coordinates. See DOI: 10.1039/d1ra08752e

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been causing the COVID-19 pandemic, resulting in several million deaths being reported. Numerous investigations have been carried out to discover a compound that can inhibit the biological activity of the SARS-CoV-2 main protease, which is an enzyme related to the viral replication. Among these, PF-07321332 (Nirmatrelvir) is currently under clinical trials 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. The successfully generated bound state between the 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 the thioimidate product from SARS-CoV-2 Mpro and the PF-07321332 inhibitor. We found that the catalytic triad Cys145–His41–Asp187 of SARS-CoV-2 Mpro plays an important role in the activation of the PF-07321332 covalent inhibitor, which renders the deprotonation of Cys145 and, thus, facilitates further reaction. Our results are definitely beneficial for a better understanding of the inhibition mechanism and designing new effective inhibitors for SARS-CoV-2 Mpro. The catalytic triad Cys145–His41–Asp187 of SARS-CoV-2 Mpro plays an important role in the activation of the PF-07321332 covalent inhibitor.

Insights into the binding and covalent inhibition mechanism of PF-07321332 to SARS-CoV-2 Mpro.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been causing the COVID-19 pandemic, resulting in several million deaths being reported. Numerous investigations have been carried out to discover a compound that can inhibit the biological activity of the SARS-CoV-2 main protease, which is an enzyme related to the viral replication. Among these, PF-07321332 (Nirmatrelvir) is currently under clinical trials 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. The successfully generated bound state between the 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 the thioimidate product from SARS-CoV-2 Mpro and the PF-07321332 inhibitor. We found that the catalytic triad Cys145-His41-Asp187 of SARS-CoV-2 Mpro plays an important role in the activation of the PF-07321332 covalent inhibitor, which renders the deprotonation of Cys145 and, thus, facilitates further reaction. Our results are definitely beneficial for a better understanding of the inhibition mechanism and designing new effective inhibitors for SARS-CoV-2 Mpro.

Unbinding ligands from SARS-CoV-2 Mpro via umbrella sampling simulations.

The umbrella sampling (US) simulation is demonstrated to be an efficient approach for determining the unbinding pathway and binding affinity to the SARS-CoV-2 Mpro of small molecule inhibitors. The accuracy of US is in the same range as the linear interaction energy (LIE) and fast pulling of ligand (FPL) methods. In detail, the correlation coefficient between US and experiments does not differ from FPL and is slightly smaller than LIE. The root mean square error of US simulations is smaller than that of LIE. Moreover, US is better than FPL and poorer than LIE in classifying SARS-CoV-2 Mpro inhibitors owing to the reciever operating characteristic-area under the curve analysis. Furthermore, the US simulations also provide detailed insights on unbinding pathways of ligands from the binding cleft of SARS-CoV-2 Mpro. The residues Cys44, Thr45, Ser46, Leu141, Asn142, Gly143, Glu166, Leu167, Pro168, Ala191, Gln192 and Ala193 probably play an important role in the ligand dissociation. Therefore, substitutions at these points may change the mechanism of binding of inhibitors to SARS-CoV-2 Mpro.

Thermodynamics and kinetics in antibody resistance of the 501Y.V2 SARS-CoV-2 variant.

Understanding the thermodynamics and kinetics of the binding process of an antibody to the SARS-CoV-2 receptor-binding domain (RBD) of the spike protein is very important for the development of COVID-19 vaccines. In particular, it is essential to understand how the binding mechanism may change under the effects of RBD mutations. In this context, we have demonstrated that the South African variant (B1.351 or 501Y.V2) can resist the neutralizing antibody (NAb). Three substitutions in the RBD including K417N, E484K, and N501Y alter the free energy landscape, binding pose, binding free energy, binding kinetics, hydrogen bonding, nonbonded contacts, and unbinding pathway of RBD + NAb complexes. The low binding affinity of NAb to 501Y.V2 RBD confirms the antibody resistance of the South African variant. Moreover, the fragment of NAb + RBD can be used as an affordable model to investigate changes in the binding process between the mutated RBD and antibodies.

Study on Performance of Rooftop Solar Power Generation Combined with Battery Storage at Office Building in Northeast Region, Vietnam

At present, renewable energy sources are considered to ensure energy security and combat climate change. Vietnam has a high potential for solar power development, especially in the central region and the southern region. However, the northeast region has the lowest solar radiation value, so it can cause difficulty for rooftop solar power investment. In this paper, the study results analyze the financial efficiency of the grid-tied rooftop solar power system with battery storage and compared it to the grid-tied rooftop solar power system without battery storage. The experimental data of a grid-tied solar power system with battery storage at an office building in the northeast region of Vietnam is collected to evaluate the system’s operation performance in real conditions. The study results present that the financial efficiency of rooftop grid-tied power project with and without storage is viable since the benefit-cost ratio (B–C) is larger than one, and internal rate of return (IRR) and net present value (NPV) are positive. However, the grid-tied rooftop solar power system with storage is not quite feasible in case of changing the electricity selling price and investment cost even though the grid-tied solar power system using the storage device can operate more flexibly. The payback period of the grid-tied solar power system with storage is 6.2 years longer and the total profit is nearly 1.9 times lower than the solar power system without battery storage due to the difference in the price of the inverters and the battery. In contrast, the grid-tied solar power system without battery storage shows better financial efficiency but strongly depends on the operation of the utility grid.

Binding of inhibitors to the monomeric and dimeric SARS-CoV-2 Mpro.

SARS-CoV-2 rapidly infects millions of people worldwide since December 2019. There is still no effective treatment for the virus, resulting in the death of more than one million patients. Inhibiting the activity of SARS-CoV-2 main protease (Mpro), 3C-like protease (3CLP), is able to block the viral replication and proliferation. In this context, our study has revealed that in silico screening for inhibitors of SARS-CoV-2 Mpro can be reliably done using the monomeric structure of the Mpro instead of the dimeric one. Docking and fast pulling of ligand (FPL) simulations for both monomeric and dimeric forms correlate well with the corresponding experimental binding affinity data of 24 compounds. The obtained results were also confirmed via binding pose and noncovalent contact analyses. Our study results show that it is possible to speed up computer-aided drug design for SARS-CoV-2 Mpro by focusing on the monomeric form instead of the larger dimeric one.

Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations.

Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of R Dock = 0.72 ± 0.14 and R W = -0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are periandrin V, penimocycline, cis-p-Coumaroylcorosolic acid, glycyrrhizin, and uralsaponin B. The obtained results could probably lead to enhance the COVID-19 therapy.

Particulate matter and SARS-CoV-2: A possible model of COVID-19 transmission.

Coronavirus disease 2019 (COVID-19), an acute respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly developed into a pandemic throughout the world. This disease is a highly infectious novel coronavirus and can affect people of all ages. Previous reports observed that particulate matter (PM) provided a platform for intermixing with viruses (i.e., influenza). However, the role of PM in SARS-CoV-2 transmission remains unclear. In this paper, we propose that PM plays a direct role as a "carrier" of SARS-CoV-2. SARS-CoV-2 is reported to have a high affinity for the angiotensin-converting enzyme 2 (ACE2) receptor. Indirectly, exposure to PM increases ACE2 expression in the lungs which facilitates SARS-CoV-2 viral adhesion. Thus, the high risk of SARS-CoV-2 in heavily polluted regions can be explained by upregulation of ACE2 caused by PM. PM could be both a direct and indirect transmission model for SARS-CoV-2 infection.