3CLpro Research Articles

Structure and inhibition of SARS-CoV-1 and SARS-CoV-2 main proteases by oral antiviral compound AG7404

Severe acute respiratory syndrome coronaviruses 1 and 2 (SARS-CoV-1 and SARS-CoV-2) pose a threat to global public health. The 3C-like main protease (Mpro), which presents structural similarity with the active site domain of enterovirus 3C protease, is one of the best-characterized drug targets of these viruses. Here we studied the antiviral activity of the orally bioavailable enterovirus protease inhibitor AG7404 against SARS-CoV-1 and SARS-CoV-2 from a structural, biochemical, and cellular perspective, comparing it with the related molecule rupintrivir (AG7800). Crystallographic structures of AG7404 in complex with SARS-CoV-1 Mpro and SARS-CoV-2 Mpro and of rupintrivir in complex with SARS-CoV-2 Mpro were solved, revealing that all protein residues interacting with the inhibitors are conserved between the two proteins. A detailed analysis of protein-inhibitor interactions indicates that AG7404 has a better fit to the active site of the target protease than rupintrivir. This observation was further confirmed by biochemical FRET assays showing IC50 values of 47 μM and 101 μM for AG7404 and rupintrivir, respectively, in the case of SARS-CoV-2 Mpro. Equivalent IC50 values for SARS-CoV-1 also revealed greater inhibitory capacity of AG7404, with a value of 29 μM vs. 66 μM for rupintrivir. Finally, the antiviral activity of the two inhibitors against SARS-CoV-2 was confirmed in a human cell culture model of SARS-CoV-2 infection, although rupintrivir showed a higher potency and selectivity index in this assay.

Potent and biostable inhibitors of the main protease of SARS-CoV-2

Potent and biostable inhibitors of the main protease (Mpro) of SARS-CoV-2 were designed and synthesized based on an active hit compound 5h (2). Our strategy was based not only on the introduction of fluorine atoms into the inhibitor molecule for an increase of binding affinity for the pocket of Mpro and cell membrane permeability but also on the replacement of the digestible amide bond by a surrogate structure to increase the biostability of the compounds. Compound 3 is highly potent and blocks SARS-CoV-2 infection invitro without a viral breakthrough. The derivatives, which contain a thioamide surrogate in the P2-P1 amide bond of these compounds (2 and 3), showed remarkably preferable pharmacokinetics in mice compared with the corresponding parent compounds. These data show that compounds 3 and its biostable derivative 4 are potential drugs for treating COVID-19 and that replacement of the digestible amide bond by its thioamide surrogate structure is an effective method.

Predicting drug-target binding affinity through molecule representation block based on multi-head attention and skip connection.

Exiting computational models for drug-target binding affinity prediction have much room for improvement in prediction accuracy, robustness and generalization ability. Most deep learning models lack interpretability analysis and few studies provide application examples. Based on these observations, we presented a novel model named Molecule Representation Block-based Drug-Target binding Affinity prediction (MRBDTA). MRBDTA is composed of embedding and positional encoding, molecule representation block and interaction learning module. The advantages of MRBDTA are reflected in three aspects: (i) developing Trans block to extract molecule features through improving the encoder of transformer, (ii) introducing skip connection at encoder level in Trans block and (iii) enhancing the ability to capture interaction sites between proteins and drugs. The test results on two benchmark datasets manifest that MRBDTA achieves the best performance compared with 11 state-of-the-art models. Besides, through replacing Trans block with single Trans encoder and removing skip connection in Trans block, we verified that Trans block and skip connection could effectively improve the prediction accuracy and reliability of MRBDTA. Then, relying on multi-head attention mechanism, we performed interpretability analysis to illustrate that MRBDTA can correctly capture part of interaction sites between proteins and drugs. In case studies, we firstly employed MRBDTA to predict binding affinities between Food and Drug Administration-approved drugs and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication-related proteins. Secondly, we compared true binding affinities between 3C-like proteinase and 185 drugs with those predicted by MRBDTA. The final results of case studies reveal reliable performance of MRBDTA in drug design for SARS-CoV-2.

Hybrid Approach to Identifying Druglikeness Leading Compounds against COVID-19 3CL Protease.

SARS-CoV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdowns, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure COVID-19. In the first step, a total of 133 drug-likeness bioactive molecules are retrieved from the ChEMBL database against SARS coronavirus 3CL Protease. Based on the standard IC50, the dataset is divided into three classes: active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR)-based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting-, XGBoost-, Support Vector-, Decision Tree-, and Random Forest-based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with the ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134, and 426898. These molecules are highly suitable drug candidates for SARS-CoV-2 3CL Protease. In the next step, the efficacy of the bioactive molecules is computed in terms of binding affinity using molecular docking, and then six bioactive molecules are shortlisted, with the ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-CoV-2. It is anticipated that the pharmacologist and/or drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-CoV-2. They can adopt these promising compounds for their downstream drug development stages.

Clinical course of patients with severe SARS-CoV-2 infection co-treatment with Jin Si Herbal Tea in Eastern Taiwan: A retrospective cohort study

IntroductionCoronavirus disease-2019 (COVID-19) has affected more than 608 million people and has killed 6.5 million people in the world. A few studies showed traditional Chinese medicine can be beneficial for COVID-19 treatment. An herbal preparation Jin Si Herbal Tea (JS) was formulated with herbal extracts known for their potential to decrease spike protein and ACE2 interaction, 3CL, and TRPMSS2 protease activity, and thus aimed to evaluate the clinical course of JS co-treatment along with the usual treatment schedule given for severe COVID-19 patients. MethodsThis retrospective cohort study included patients with severe COVID-19 admitted to Hualien Tzu Chi Hospital between June and July 2021. All the patients were co-treated with JS and the primary outcome was death. The secondary outcomes included laboratory exam, Ct value, clinical course, and hospital stays. There were 10 patients recruited in this study and divided into < 70 years and ≧ 70 years groups (n = 5 in each group). ResultsOlder patients (≧70 years) had a higher Charlson Comorbidity Index, VACO index, and lower hemoglobin levels than < 70 years patients. The trend of lymphocyte count, LDH, D-dimer, and Ct value of non-survivors was not consistent with previous studies. The death rate was 20% and the recovery rate to mild illness in 14 days was 40%. ConclusionIn conclusion, this is the first clinical study of JS co-treatment in severe COVID-19 patients. JS co-treatment might reduce death rate and recovery time. Further large-scale clinical trials would be expected.

Effect of GS-441524 in combination with the 3C-like protease inhibitor GC376 on the treatment of naturally transmitted feline infectious peritonitis.

The main objectives of this study were to investigate the efficacy of the nucleotide analog GS-441524 in combination with the 3C-like protease inhibitor GC376 for the treatment of naturally aquired feline infectious peritonitis (FIP) and to test whether their combination shortens the dosing period and improves the cure rate. In total, 46 FIP-affected cats were enrolled in this experiment, including 36 with wet FIP (29 with abdominal effusion, six with thoracic effusion, and one with thoracic+abdominal effusion), and 10 with dry FIP. The cats were aged from 3 to 96 months. Thoracic+abdominal effusion, lymph-node puncture fluid and perirenal puncture fluid was collected from the affected cats for qPCR testing, and all 46 cats were positive for feline coronavirus (FCoV). The cats divided into different dose groups, all treated for 4 weeks: group 1 (GS-441524, 5 mg/kg.sc.q.24 h; GC376, 20 mg/kg.sc.q.12 h), group 2 (GS-441524, 2.5 mg/kg.sc.q.24 h; GC376, 20 mg/kg.sc.q.12 h), group 3 (GS-441524, 2.5 mg/kg.sc.q.24 h; GC376, 10 mg/kg.sc.q.12 h), and group 4 (GS-441524, 5 mg/kg.sc.q.24 h; GC376, 10 mg/kg.sc.q.12 h). After the 4-week combination treatment, 45 of the 46 (97.8%) cats survived, and 43 of those became clinically normal. Two cats required longer (7 to 12 weeks) treatment to achieve full recovery. As of writing (10 months after completion of the trial), all 45 cats were alive and no relapse was observed. GS-441524 combined with GC376 can be safely and effectively used to treat FIP and reduces the treatment period to 4 weeks, with an excellent cure rate.

Safety, Tolerability, and Pharmacokinetics of Intravenous Doses of PF-07304814, a Phosphate Prodrug Protease Inhibitor for the Treatment of SARS-CoV-2, in Healthy Adult Participants.

Studies on targeted antivirals for treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the ongoing pandemic, are limited. PF-07304814 (lufotrelvir) is the phosphate prodrug of PF-00835231, a protease inhibitor targeting the 3C-like protease of SARS-CoV-2. This phase 1 study evaluated the safety, tolerability, and pharmacokinetics (PK) of single ascending intravenous doses of lufotrelvir (continuous 24-hour infusion of 50, 150, 500, or 700 mg) versus placebo in healthy volunteers (2 interleaving cohorts: 1, n = 8; 2, n = 7). Each dosing period was separated by a washout interval (≥5 days). Treatment-emergent adverse events, PK, and biomarker concentrations were estimated from plasma/urine samples. Lufotrelvir was administered to 15 volunteers (mean [SD] age 39.7 [11.8] years). No serious adverse events, discontinuations, or deaths were reported. Mean maximum observed concentration of PF-00835231 (active moiety; 97.0 ng/mL to 1288 ng/mL) were observed between median time to maximum concentration of 14 to 16 hours after the start of the lufotrelvir infusion. Near-maximum plasma concentrations of PF-00835231 were observed ≈6 hours after infusion start and sustained until infusion end. PF-00835231 plasma concentrations declined rapidly after infusion end (mean terminal half-life: 500 mg, 2.0 hours; 700 mg, 1.7 hours). Approximately 9%-11% of the dose was recovered in urine as PF-00835231 across doses. A continuous, single-dose, 24-hour infusion of lufotrelvir (50-700 mg) was rapidly converted to PF-00835231 (active moiety), with dose-proportional PK exposures and no significant safety concerns. A daily, 24-hour continuous infusion of 270 to 350 mg is expected to maintain PF-00835231 concentration at steady state/above effective antiviral concentrations. Further studies exploring lufotrelvir efficacy in patients with coronavirus disease 2019 are ongoing.

Discovery of Chlorofluoroacetamide-Based Covalent Inhibitors for Severe Acute Respiratory Syndrome Coronavirus 2 3CL Protease.

The coronavirus disease 2019 (COVID-19) pandemic has necessitated the development of antiviral agents against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). 3C-like protease (3CLpro) is a promising target for COVID-19 treatment. Here, we report a new class of covalent inhibitors of 3CLpro that possess chlorofluoroacetamide (CFA) as a cysteine-reactive warhead. Based on an aza-peptide scaffold, we synthesized a series of CFA derivatives in enantiopure form and evaluated their biochemical efficiency. The data revealed that 8a (YH-6) with the R configuration at the CFA unit strongly blocks SARS-CoV-2 replication in infected cells, and its potency is comparable to that of nirmatrelvir. X-ray structural analysis showed that YH-6 formed a covalent bond with Cys145 at the catalytic center of 3CLpro. The strong antiviral activity and favorable pharmacokinetic properties of YH-6 suggest its potential as a lead compound for the treatment of COVID-19.

Prediction of drug-likeness using graph convolutional attention network.

The drug-likeness has been widely used as a criterion to distinguish drug-like molecules from non-drugs. Developing reliable computational methods to predict the drug-likeness of compounds is crucial to triage unpromising molecules and accelerate the drug discovery process. In this study, a deep learning method was developed to predict the drug-likeness based on the graph convolutional attention network (D-GCAN) directly from molecular structures. Results showed that the D-GCAN model outperformed other state-of-the-art models for drug-likeness prediction. The combination of graph convolution and attention mechanism made an important contribution to the performance of the model. Specifically, the application of the attention mechanism improved accuracy by 4.0%. The utilization of graph convolution improved the accuracy by 6.1%. Results on the dataset beyond Lipinski's rule of five space and the non-US dataset showed that the model had good versatility. Then, the billion-scale GDB-13 database was used as a case study to screen SARS-CoV-2 3C-like protease inhibitors. Sixty-five drug candidates were screened out, most substructures of which are similar to these of existing oral drugs. Candidates screened from S-GDB13 have higher similarity to existing drugs and better molecular docking performance than those from the rest of GDB-13. The screening speed on S-GDB13 is significantly faster than screening directly on GDB-13. In general, D-GCAN is a promising tool to predict the drug-likeness for selecting potential candidates and accelerating drug discovery by excluding unpromising candidates and avoiding unnecessary biological and clinical testing. The source code, model and tutorials are available at https://github.com/JinYSun/D-GCAN. The S-GDB13 database is available at https://doi.org/10.5281/zenodo.7054367. Supplementary data are available at Bioinformatics online.

Structural similarities between SARS-CoV2 3CLpro and other viral proteases suggest potential lead molecules for developing broad spectrum antivirals.

Considering the significant impact of the recent COVID-19 outbreak, development of broad-spectrum antivirals is a high priority goal to prevent future global pandemics. Antiviral development processes generally emphasize targeting a specific protein from a particular virus. However, some antiviral agents developed for specific viral protein targets may exhibit broad spectrum antiviral activity, or at least provide useful lead molecules for broad spectrum drug development. There is significant potential for repurposing a wide range of existing viral protease inhibitors to inhibit the SARS-CoV2 3C-like protease (3CLpro). If effective even as relatively weak inhibitors of 3CLpro, these molecules can provide a diverse and novel set of scaffolds for new drug discovery campaigns. In this study, we compared the sequence- and structure-based similarity of SARS-CoV2 3CLpro with proteases from other viruses, and identified 22 proteases with similar active-site structures. This structural similarity, characterized by secondary-structure topology diagrams, is evolutionarily divergent within taxonomically related viruses, but appears to result from evolutionary convergence of protease enzymes between virus families. Inhibitors of these proteases that are structurally similar to the SARS-CoV2 3CLpro protease were identified and assessed as potential inhibitors of SARS-CoV2 3CLpro protease by virtual docking. Several of these molecules have docking scores that are significantly better than known SARS-CoV2 3CLpro inhibitors, suggesting that these molecules are also potential inhibitors of the SARS-CoV2 3CLpro protease. Some have been previously reported to inhibit SARS-CoV2 3CLpro. The results also suggest that established inhibitors of SARS-CoV2 3CLpro may be considered as potential inhibitors of other viral 3C-like proteases.

Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen

SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly focused on non-covalent inhibitor discovery. Here, we developed a deep learning-based stepwise strategy for selective covalent inhibitor screen. We used a deep learning framework that integrated a directed message passing neural network with a feed-forward neural network to construct two different classifiers for either covalent or non-covalent inhibition activity prediction. These two classifiers were trained on the covalent and non-covalent 3CL protease inhibitors dataset, respectively, which achieved high prediction accuracy. We then successively applied the covalent inhibitor model and the non-covalent inhibitor model to screen a chemical library containing compounds with covalent warheads of cysteine. We experimentally tested the inhibition activity of 32 top-ranking compounds and 12 of them were active, among which 6 showed IC50 values less than 12 μM and the strongest one inhibited SARS-CoV-2 3CL protease with an IC50 of 1.4 μM. Further investigation demonstrated that 5 of the 6 active compounds showed typical covalent inhibition behavior with time-dependent activity. These new covalent inhibitors provide novel scaffolds for developing highly active SARS-CoV-2 3CL covalent inhibitors.

Compounds Isolated from Lawsonia inermis L. Collected in Vietnam and Evaluation of Their Potential Activity Against the Main Protease of SARS-CoV-2 Using In silico Molecular Docking and Molecular Dynamic Simulation

Since late 2019 to early 2020, an outbreak caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has become a worldwide health emergency due to its rapid infection and mortality of millions of people around the world. As the main protease Mpro or 3CLpro produced by the virus plays an important role in coronavirus survival and proliferation, it becomes an excellent drug target to identify COVID-19 inhibitors. Lawsonia inermis L. (henna) is a medicinal plant that has been used for a long time for the treatment of many fungal and bacterial infections. In the search for new anti-COVID agents from medicinal plants, we report the results of our study into the potential inhibition of Mpro by the compounds isolated from the extracts of L. inermis roots and leaves using molecular docking and molecular dynamics simulation. The molecular modeling results showed that all isolated compounds bonded spontaneously into the catalytic pockets of Mpro with binding energies &lt;0. The docking and calculated pharmacokinetic results of the compounds (1-3, 6-8) were similar to and even better than those of the commercial COVID-19 inhibitor remdesivir. In particular, the triterpenoid glycoside suavissimoside R1 (8) showed the best binding to SARS-CoV Mpro, with the lowest binding energy ΔG and IC50,calc. values of −8.19 kcal/mol and 0.98 μM, respectively. Furthermore, the calculations of ADMET (absorption, distribution, metabolism, excretion, and toxicity) showed that it had the lowest toxicity, with a predicted LD50 value of 3320 mg/kg. These triterpenoids are worthy of further study to evaluate their actual bioactivity against SARS-CoV-2 in vitro and in vivo in the hope of contributing valuable scientific data for natural resources for the development novel drug formulations for either the prevention or treatment of COVID-19.

Repurposing Ayush-64 for COVID-19: A Computational Study Based on Network Pharmacology and Molecular Docking.

As COVID-19 pandemic continues to affect people's lives, the government of India gave emergency use approval to the ayurvedic antimalarial drug Ayush-64 in April 2021 to treat asymptomatic COVID-19 positive and mild COVID-19 positive patients. This study aims to explore the therapeutic potential of Ayush-64 to treat COVID-19 and provide a new approach for repurposing Ayurvedic drugs. The bioactives present in Ayush-64 were found along with their targets, and a plantbioactive- target network was created. A protein-protein interaction network of the common targets of Ayush-64 and COVID-19 was constructed and analyzed to find the key targets of Ayush-64 associated with the disease. Gene ontology and pathway enrichment analysis were performed to find COVID-19 related biological processes and pathways involved by the key targets. The key bioactives were docked with SARS-CoV-2 main protease 3CL, native Human Angiotensin-converting Enzyme ACE2, Spike protein S1, and RNA-dependent RNA polymerase RdRp. From the 336 targets for Ayush-64, we found 38 key targets. Functional enrichment analysis of the key targets resulted in 121 gene ontology terms and 38 pathways. When molecular docking was performed with four receptors, thirteen bioactives showed good binding affinity comparable to that of the eight drugs presently used to treat COVID-19. Network pharmacological analysis and molecular docking study of Ayush-64 revealed that it can be recommended to treat COVID-19. Further in vitro and in vivo studies are needed to confirm the results. The study demonstrated a new approach for repurposing Ayurvedic drugs.

Development of a deep learning-based quantitative structure-activity relationship model to identify potential inhibitors against the 3C-like protease of SARS-CoV-2.

<b>Background:</b> In the recent COVID-19 pandemic, SARS-CoV-2 infection spread worldwide. The3C-like protease (3CLpro) is a promising drug target for SARS-CoV-2. <b>Results:</b> We constructed a deep learning-based convolutional neural network-quantitative structure-activity relationship (CNN-QSAR) model and deployed it on various databases to predict the biological activity of 3CLpro inhibitors. Subsequently, molecular docking analysis, molecular dynamics simulations and binding free energy calculations were performed to validate the predicted inhibitory activity against 3CLpro of SARS-CoV-2. The model showed mean squared error=0.114, mean absolute error=0.24 and predicted R²=0.84 for the test dataset. Diosmin showed good binding affinity and stability over the course of the simulations. <b>Conclusion:</b> The results suggest that the proposed CNN-QSAR model can be an efficient method for hit prediction and a new way to identify hit compounds against 3CLpro of SARS-CoV-2.

Identification of compelling inhibitors of human norovirus 3CL protease to combat gastroenteritis: A structure-based virtual screening and molecular dynamics study.

Human noroviruses (NV) are the most prevalent cause of sporadic and pandemic acute gastroenteritis. NV infections cause substantial morbidity and death globally, especially amongst the aged, immunocompromised individuals, and children. There are presently no authorized NV vaccines, small-molecule therapies, or prophylactics for humans. NV 3 C L protease (3CLP) has been identified as a promising therapeutic target for anti-NV drug development. Herein, we employed a structure-based virtual screening method to screen a library of 700 antiviral compounds against the active site residues of 3CLP. We report three compounds, Sorafenib, YM201636, and LDC4297, that were revealed to have a higher binding energy (BE) value with 3CLP than the control (Dipeptidyl inhibitor 7) following a sequential screening, in-depth molecular docking and visualization, physicochemical and pharmacological property analysis, and molecular dynamics (MD) study. Sorafenib, YM201636, and LDC4297 had BEs of -11.67, -10.34, and -9.78 kcal/mol with 3CLP, respectively, while control had a BE of -6.38 kcal/mol. Furthermore, MD simulations of the two best compounds and control were used to further optimize the interactions, and a 100 ns MD simulation revealed that they form stable complexes with 3CLP. The estimated physicochemical, drug-like, and ADMET properties of these hits suggest that they might be employed as 3CLP inhibitors in the management of gastroenteritis. However, wet lab tests are a prerequisite to optimize them as NV 3CLP inhibitors.

Individual and Synergistic Potential of Bioactive Compounds from Chrysopogon Zizanioides Against Main-Protease of SARSCov-2 using Computational Approach

This study presents the anti-COVID potential of bioactive compounds from Chrysopogon zizanioides thorough in-silico molecular docking approach using AutoDock Vina software. As of our knowledge, the antiviral potential of all its bioactive compounds and their synergistic potentials against SARS-CoV-2 main-protease is not reported earlier. The results were promising with ß-Sitosterol (?G = -7.5 kcal/mol; Ki = 3.13 µM); Campesterol (?G = -7.4 kcal/mol; Ki = 3.71 µM); Stigmast-4-en-3-one (?G = -7.3 kcal/mol; Ki = 4.39 µM) forming noncovalent interactions with the amino acids in the active site of Mpro causing inhibition. The synergistic potential of compounds showed a significant sign of inhibition against Mpro with -7.9 kcal/mol with the sequential combination of ß-Sitosterol; Campesterol; Stigmast-4-en-3-one. The docking protocol validation was performed by re-docking and superimposing co-crystallized ligand, and interactions visualized using Discovery Studio 2020. Moreover, all the compounds satisfied Lipinski’s oral drug-likeliness properties to be used and oral drug. These bioactive compounds of Chrysopogon zizanioides showed low binding energies against SARS-CoV-2 Mpro which proved their anti-COVID potential. Thus, by incorporating Chrysopogon zizanioides for consumption in daily life, it is very likely that one can get rid of COVID-19.

Repurposing of HIV/HCV protease inhibitors against SARS-CoV-2 3CLpro

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen that caused the global COVID-19 outbreak. The 3C-like protease (3CLpro) of SARS-CoV-2 plays a key role in virus replication and has become an ideal target for antiviral drug design. In this work, we have employed bioluminescence resonance energy transfer (BRET) technology to establish a cell-based assay for screening inhibitors against SARS-CoV-2 3CLpro, and then applied the assay to screen a collection of known HIV/HCV protease inhibitors. Our results showed that the assay is capable of quantification of the cleavage efficiency of 3CLpro with good reproducibility (Z′ factor is 0.59). Using the assay, we found that 9 of 26 protease inhibitors effectively inhibited the activity of SARS-CoV-2 3CLpro in a dose-dependent manner. Among them, four compounds exhibited the ability to bind to 3CLproin vitro. HCV protease inhibitor simeprevir showed the most potency against 3CLpro with an EC50 vale of 2.6 μM, bound to the active site pocket of 3CLpro in a predicted model, and importantly, exhibited a similar activity against the protease containing the mutations P132H in Omicron variants. Taken together, this work demonstrates the feasibility of using the cell-based BRET assay for screening 3CLpro inhibitors and supports the potential of simeprevir for the development of 3CLpro inhibitors.

In silico Study Phytosterol Cymbopogon citratus and Curcuma longa as Inhibitor Agent 3C-Like Protease SARS-CoV-2.

&lt;b&gt;Background and Objective:&lt;/b&gt; Lemongrass (&lt;i&gt;Cymbopogon citratus&lt;/i&gt;) and turmeric (&lt;i&gt;Curcuma longa&lt;/i&gt;) are widely used by the community for traditional medicinal spices and cooking spices. In the era of the COVID-19 pandemic, people use lemongrass and turmeric to increase immunity and protect the body from infection with the SARS-CoV-2 virus. However, the antiviral mechanisms have not been studied much. This study aims to predict the bioactivity of the phytosterol compounds of lemongrass and turmeric for COVID-19 therapy through inhibition of 3C-like protease (3CLPro) &lt;i&gt;in silico&lt;/i&gt;. &lt;b&gt;Materials and Methods:&lt;/b&gt; The 3CLPro protein 3D structure was downloaded from the PDB database with the access code 2ZU2 and the phytosterol compounds of lemongrass and turmeric were taken from PubChem. A total of 59 total phytosterol compounds from turmeric and lemongrass were screened for their bioactivity as an antiviral by using online PASS. Compounds with a high activating potential (Pa) were interacted with 3CLPro protein with the PyRx program and analyzed by Discovery Studio version 19.0 and LigPlus. &lt;b&gt;Results:&lt;/b&gt; A total of 22 total phytosterol compounds were identified as potential antiviral agents. Based on the Pa value, 15 phytosterol compounds have the potential to act as inhibitor agents for 3CLPro SARS-CoV-2. The phytosterol compounds of lemongrass and turmeric bind to the 3CLPro protein in the N-finger domain region and the A and B domain inhibitors connect residues of the 3CLPro protein. The phytosterols of lemongrass and turmeric show a low binding affinity with 3CLPro SARS-CoV-2, indicating a strong interaction between ligand and protein. The inhibition of phytosterols against 3CLPro protein can be used as a basis for determining candidates for COVID-19 therapeutic agents. &lt;b&gt;Conclusion:&lt;/b&gt; The phytosterol compounds contained in lemongrass and turmeric have the potential to act as 3CLPro inhibitors. Further studies both &lt;i&gt;in vitro&lt;/i&gt; and &lt;i&gt;in vivo&lt;/i&gt; need to be done to prove the inhibitory potential of phytosterol compounds.

SARS-CoV-2 main protease (3CLpro) interaction with acyclovir antiviral drug/methyl-β-cyclodextrin complex: Physiochemical characterization and molecular docking

During the current outbreak of the novel coronavirus disease 2019 (COVID-19), researchers have examined several antiviral drugs with the potential to inhibit the proliferation of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The antiviral drug acyclovir (AVR), which is used to treat COVID-19, in complex with methyl-β-cyclodextrin (Mβ-CD) was examined in the solution and solid phases. UV–visible and fluorescence spectroscopic analyses confirmed that the guest (AVR) was included inside the host (Mβ-CD) cavity. A solid inclusion complex of AVR was prepared by co-precipitation, physical mixing, kneading, and bath sonication methods at a 1:1 ratio of Mβ-CD:AVR. The prepared Mβ-CD:AVR inclusion complex was characterized using Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and scanning electron microscopy (SEM) analysis. Phase solubility studies indicated the Mβ-CD:AVR inclusion complex exhibited a higher stability constant and linear enhancement in AVR solubility with increasing Mβ-CD concentrations. In silico analysis of the Mβ-CD/AVR inclusion complex confirmed that AVR drugs show potential as inhibitors of SARS-CoV-2 3C-like protease (3CLpro) receptors. Results obtained using the PatchDock and FireDock servers indicated that the most favorable docking ligand was Mβ-CD:AVR, which interacted with SARS-CoV-2 (3CLPro) protease inhibitors with high geometric shape complementarity scores (2522 and 5872) and atomic contact energy (-313.77 and −214.70 kcal mol−1). Our results suggest that the Mβ-CD/AVR inclusion complex inhibits the main protease of SARS-CoV-2, although further wet-lab experiments are needed to verify these findings.

The first Chinese oral anti-COVID-19 drug Azvudine launched

The first Chinese oral anti-COVID-19 drug Azvudine launched