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1.
Struct Chem ; : 1-9, 2022 Jul 26.
Article in English | MEDLINE | ID: covidwho-2014353

ABSTRACT

The novel coronavirus that has affected the whole world is declared a pandemic by the World Health Organization. Since the emergence of this virus, researchers worldwide have searched for potential antivirals against it. Being an RNA virus, it shows a high rate of mutability and variability in its genome. In the present study, all the reported SARS-CoV-2 genomes isolated from diverse regions of the world available in the GISAID database have been considered for phylogenetic analysis. The strain identified at the root is subjected to phylogenetic analysis with genomes of other known human viruses obtained from NCBI for identifying the nearest viral neighbor. Furthermore, the phylogenetic relationship between various human viruses was used to repurpose the known antiviral drugs towards coronavirus using in silico docking approach. The phylogeny reveals the link of the COVID virus with adenovirus. The known drugs against adenovirus are considered in the present study for drug repurposing through molecular docking analysis. The reference inhibitors of the respective targets were also considered in the docking study. The protein targets, namely protease, endoribonuclease, methyltransferase, phosphatase, and spike protein, are considered for screening with the known drug of adenovirus. Ribavirin, known to treat adenoviral infection, shows the best docking score, suggesting its use as a repurposed drug to treat SARS-CoV-2. Furthermore, the potency of the ribavirin drug is analyzed using molecular dynamics studies. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-022-02019-6.

2.
Struct Chem ; : 1-21, 2022 Jul 04.
Article in English | MEDLINE | ID: covidwho-2014346

ABSTRACT

COVID-19 disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV2) has resulted in tremendous loss of lives across the world and is continuing to do so. Extensive work is under progress to develop inhibitors which can prevent the disease by arresting the virus in its life cycle. One such way is by targeting the main protease of the virus which is crucial for the cleavage and conversion of polyproteins into functional units of polypeptides. In this endeavor, our effort was to identify hit molecule inhibitors for SARS-CoV2 main protease using fragment-based drug discovery (FBDD), based on the available crystal structure of chromene-based inhibitor (PDB_ID: 6M2N). The designed molecules were validated by molecular docking and molecular dynamics simulations. The stability of the complexes was further assessed by calculating their binding free energies, normal mode analysis, mechanical stiffness, and principal component analysis. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-022-01995-z.

3.
Struct Chem ; : 1-9, 2022 Jun 21.
Article in English | MEDLINE | ID: covidwho-2014345

ABSTRACT

Scientific insights gained from the severe acute respiratory syndrome (SARS) and middle east respiratory syndrome (MERS) outbreaks have been assisting scientists and researchers in the quest of antiviral drug discovery process against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Coronaviruses and influenza viruses both rely on the host type 2 transmembrane serine protease, TMPRSS2, for entry and propagation. Recent studies report SARS-CoV-2 also uses TMPRSS2 to enter cells. In the current study, we employed structure-based virtual screening of 1,82,651 natural compounds downloaded from the zin database against the homology model of the TMPRSS2 protein, followed by a molecular dynamics-based simulation to identify potential TMPRSS2 hits. The virtual screening yielded 110 hits with docking scores ranging from -8.654 to -6.775 and glide energies ranging from -55.714 to -29.065 kcal/mol. The binding mode analysis revealed that the hit molecules made H-bond, Pi-Pi stacking and salt bridge contacts with the TMPRSS2 active site residues. MD simulations of the top two hits (ZINC000095912839 and ZINC000085597504) revealed to form a stable complex with TMPRSS2, with a minimal RMSD and RMSF fluctuation. Both the hit structures interacted strongly with the Asp180, Gln183, Gly184, Ser186, Gly207 and Gly209, as predicted by Glide XP docking, and formed a significant H-bond interaction with Ser181 in MD simulation. Among these two, ZINC000095912839 was having the most stable binding interaction with TMPRSS2 of the two molecules. The present study successfully identified TMPRSS2 ligands from a database of zinc natural molecules as potential leads for novel SARs-CoV-2 treatment. Supplementary Inform: The online version contains supplementary material available at 10.1007/s11224-022-01991-3.

4.
International Journal of High Performance Computing Applications ; 2022.
Article in English | Web of Science | ID: covidwho-2005565

ABSTRACT

As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 M- pro and TMPRSS2, we performed FEP-ABFE-based virtual screening for similar to 12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards M- pro , and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, similar to 500 TB of data generated in this work will also accelerate the further development of FEP-related methods.

5.
Molecules ; 27(15)2022 Jul 23.
Article in English | MEDLINE | ID: covidwho-1994110

ABSTRACT

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20-25% inhibition of RIPK1's kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.


Subject(s)
Necroptosis , Computer Simulation , Ligands , Molecular Docking Simulation
6.
Comb Chem High Throughput Screen ; 2022 Aug 16.
Article in English | MEDLINE | ID: covidwho-1993659

ABSTRACT

BACKGROUND: The SARS-CoV-2 coronavirus (COVID-19) has raised innumerable global concerns, and few effective treatment strategy has yet been permitted by the FDA to lighten the disease burden. SARS-CoV-2 3C-like proteinase (3CLP) is a crucial protease and plays a key role in the viral life cycle, as it controls replication, and thus, it is viewed as a target for drug design. METHOD: In this study, we performed structure-based virtual screening of FDA drugs approved during the period 2015-2019 (total 220 drugs) for interaction with the active site of 3CLP (PDB ID 6LU7) using AutoDock 4.2. We report the top ten drugs that outperform the reported drugs against 3CLP (Elbasvir and Nelfinavir), particularly Cefiderocol having the highest affinity among the compounds tested, with a binding energy of -9.97 kcal/mol. H-bond (LYS102:HZ2-ligand:O49), hydrophobic (ligand-VAL104), and electrostatic (LYS102:NZ-ligand:O50) interactions were observed in cefiderocol-3CLP complex. The docked complex was subjected to a 50 ns molecular dynamics study to check its stability, and stable RMSD and RMSF graphs were observed. RESULT: Accordingly, we suggest cefiderocol might be effective against SARS-CoV-2 and urge that experimental validation to be performed to determine the antiviral efficacy of cefiderocol against SARS-CoV-2. DISCUSSION: Along with these, cefiderocol is effective for the treatment of respiratory tract pathogens and wide range of gram-negative bacteria for whom there are limited therapeutic alternatives. CONCLUSION: The aim of this article was to explore the FDA approved drugs as repurposing study against 3CLP for COVID-19 management.

7.
Mol Divers ; 2022 Aug 18.
Article in English | MEDLINE | ID: covidwho-1990719

ABSTRACT

To fight against the devastating coronavirus disease 2019 (COVID-19), identifying robust anti-SARS-CoV-2 therapeutics from all possible directions is necessary. To contribute to this effort, we selected a human metabolites database containing waters and lipid-soluble metabolites to screen against the 3-chymotrypsin-like proteases (3CLpro) protein of SARS-CoV-2. The top 8 hits from virtual screening displayed a docking score varying between ~ - 11 and ~ - 14 kcal/mol. Molecular dynamics simulations complement the virtual screening study in conjunction with the molecular mechanics generalized Born surface area (MM/GBSA) scheme. Our analyses revealed that (HMDB0132640) has the best glide docking score, - 14.06 kcal/mol, and MM-GBSA binding free energy, - 18.08 kcal/mol. The other three lead molecules are also selected along with the top molecule through a critical inspection of their pharmacokinetic properties. HMDB0132640 displayed a better binding affinity than the other three compounds (HMDB0127868, HMDB0134119, and HMDB0125821) due to increased favorable contributions from the intermolecular electrostatic and van der Waals interactions. Further, we have investigated the ligand-induced structural dynamics of the main protease. Overall, we have identified new compounds that can serve as potential leads for developing novel antiviral drugs against SARS-CoV-2 and elucidated molecular mechanisms of their binding to the main protease. Identification of probable hits from human metabolites against SARS-CoV-2 using integrated computational approaches-Missed against MS.

8.
22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2022 ; : 595-604, 2022.
Article in English | Scopus | ID: covidwho-1992573

ABSTRACT

We describe the design, implementation and performance of the RADICAL-Pilot task overlay (RAPTOR). RAPTOR enables the execution of heterogeneous tasks-i.e., functions and executables with arbitrary duration-on HPC platforms, pro-viding high throughput and high resource utilization. RAPTOR supports the high throughput virtual screening requirements of DOE's National Virtual Biotechnology Laboratory effort to find therapeutic solutions for COVID-19. RAPTOR has been used on 8300 compute nodes to sustain 144M/hour docking hits, and to screen 1011 ligands. To the best of our knowledge, both the throughput rate and aggregated number of executed tasks are a factor of two greater than previously reported in literature. RAPTOR represents important progress towards improvement of computational drug discovery, in terms of size of libraries screened, and for the possibility of generating training data fast enough to serve the last generation of docking surrogate models. © 2022 IEEE.

9.
Front Microbiol ; 13: 912145, 2022.
Article in English | MEDLINE | ID: covidwho-1987525

ABSTRACT

In order to screen the disease-related compounds of a traditional Chinese medicine prescription in network pharmacology research accurately, a new virtual screening method based on flexible neural tree (FNT) model, hybrid evolutionary method and negative sample selection algorithm is proposed. A novel hybrid evolutionary algorithm based on the Grammar-guided genetic programming and salp swarm algorithm is proposed to infer the optimal FNT. According to hypertension, diabetes, and Corona Virus Disease 2019, disease-related compounds are collected from the up-to-date literatures. The unrelated compounds are chosen by negative sample selection algorithm. ECFP6, MACCS, Macrocycle, and RDKit are utilized to numerically characterize the chemical structure of each compound collected, respectively. The experiment results show that our proposed method performs better than classical classifiers [Support Vector Machine (SVM), random forest (RF), AdaBoost, decision tree (DT), Gradient Boosting Decision Tree (GBDT), KNN, logic regression (LR), and Naive Bayes (NB)], up-to-date classifier (gcForest), and deep learning method (forgeNet) in terms of AUC, ROC, TPR, FPR, Precision, Specificity, and F1. MACCS method is suitable for the maximum number of classifiers. All methods perform poorly with ECFP6 molecular descriptor.

10.
Biochemical and Biophysical Research Communications ; 2022.
Article in English | ScienceDirect | ID: covidwho-1982610

ABSTRACT

New variations of SARS-CoV-2 continue to emerge in the global pandemic, which may be resistant to at least some vaccines in COVID-19, indicating that drug and vaccine development must be continuously strengthened. NSP10 plays an essential role in SARS-CoV-2 viral life cycle. It stimulates the enzymatic activities of NSP14-ExoN and NSP16–O-MTase by the formation of NSP10/NSP14 and NSP10/NSP16 complexes. Inhibiting NSP10 can block the binding of NSP10 to NSP14 and NSP16. This study has identified potential natural NSP10 inhibitors from ZINC database. The protein druggable pocket was identified for screening candidates. Molecular docking of the selected compounds was performed and MM-GBSA binding energy was calculated. After ADMET assessment, 4 hits were obtained for favorable druggability. The analysis of site interactions suggested that the hits all had excellent binding. Molecular dynamics studies revealed that selected natural compounds stably bind to NSP10. These compounds were identified as potential leads against NSP10 for the development of strategies to combat SARS-CoV-2 replication and could serve as the basis for further studies.

11.
Microb Pathog ; 170: 105701, 2022 Aug 10.
Article in English | MEDLINE | ID: covidwho-1977657

ABSTRACT

Neuropilin-1 (NRP1) is a widely expressed cell surface receptor protein characterized by its pleiotropic function. Recent reports highlighted NRP1 as an additional entry point of the SARS-CoV-2 virus, enhancing viral infectivity by interacting with the S-protein of SARS-CoV-2. The ubiquitous distribution and mechanism of action of NRP1 enable the SARS-CoV-2 virus to attack multiple organs in the body simultaneously. Therefore, blocking NRP1 is a potential therapeutic approach against SARS-CoV-2 infection. The current study screened the South African natural compounds database (SANCDB) for molecules that can disrupt the SARS-CoV-2 S protein-NRP1 interaction as a potential antiviral target for SARS-CoV-2 cellular entry. Following excessive screening and validation analysis 3-O-Methylquercetin and Esculetin were identified as potential compounds to disrupt the S-protein-NRP1 interaction. Furthermore, to understand the conformational stability and dynamic features between NRP1 interaction with the selected natural products, we performed 200 ns molecular dynamics (MD) simulations. In addition, molecular mechanics-generalized Born surface area (MM/GBSA) was utilized to calculate the free binding energies of the natural products interacting with NRP1. 3-O-methylquercetin showed an inhibitory effect with binding energies ΔG of -25.52 ±â€¯0.04 kcal/mol to NRP1, indicating the possible disruption of the NRP1-S-protein interaction. Our analysis demonstrated that 3-O-methylquercetin presents a potential antiviral compound against SARS-CoV-2 infectivity. These results set the path for future functional in-vitro and in-vivo studies in SARS-CoV-2 research.

12.
Appl Biochem Biotechnol ; 2022 Aug 03.
Article in English | MEDLINE | ID: covidwho-1971833

ABSTRACT

In the year 2019-2020, the whole world witnessed the spread of a disease called COVID-19 caused by SARS-CoV-2. A number of effective drugs and vaccine has been formulated to combat this outbreak. For the development of anti-COVID-19 drugs, the main protease (Mpro) is considered a key target as it has rare mutations and plays a crucial role in the replication of the SARS CoV-2. In this study, a library of selected lichen compounds was prepared and used for virtual screening against SARS-CoV-2 Mpro using molecular docking, and several hits as potential inhibitors were identified. Remdesivir was used as a standard inhibitor of Mpro for its comparison with the identified hits. Twenty-six compounds were identified as potential hits against Mpro, and these were subjected to in silico ADMET property prediction, and the compounds having favorable properties were selected for further analysis. After manual inspection of their interaction with the binding pocket of Mpro and binding affinity score, four compounds, namely, variolaric acid, cryptostictinolide, gyrophoric acid, and usnic acid, were selected for molecular dynamics study to evaluate the stability of complex. The molecular dynamics results indicated that except cryptostictinolide, all the three compounds made a stable complex with Mpro throughout a 100-ns simulation time period. Among all, usnic acid seems to be more stable and effective against SARS-CoV-2 Mpro. In summary, our findings suggest that usnic acid, variolaric acid, and gyrophoric acid have potential to inhibit SARS-Cov-2 Mpro and act as a lead compounds for the development of antiviral drug candidates against SARS-CoV-2.

13.
Chem Biol Drug Des ; 2022 Jul 06.
Article in English | MEDLINE | ID: covidwho-1971106

ABSTRACT

The Papain-Like proteases (PLpro) of SARS-CoV-2 play a crucial role in viral replication and the formation of nonstructural proteins. To find available inhibitors, the 3D structure of PLpro of SARS2 was obtained by homologous modelling, and we used this structure as a target to search for inhibitors through molecular docking and MM/GBSA binding free energy rescoring. A novel hydrogen bonding penalty was applied to the screening process, which meanwhile took desolvation into account. Finally, 61 compounds were acquired and 4 of them with IC50 at micromolar level tested in vitro enzyme activity assay, which includes clinical drugs tegaserod. Considering the importance of crystal water molecules, the 4 compounds were re-docked and considered bound waters in the active site as a part of PLpro. The binding modes of these 4 compounds were further explored with metadynamics simulations. The hits will provide a starting point for future key interactions identified and lead optimization targetting PLpro.

14.
J Biomol Struct Dyn ; : 1-12, 2022 Jul 31.
Article in English | MEDLINE | ID: covidwho-1967740

ABSTRACT

This study proposes a novel model for integration of SARS-CoV-2 into host cell via endocytosis as a possible alternative to the prevailing direct fusion model. It is known that the SARS-CoV-2 spike protein undergoes proteolytic cleavage at S1-S2 cleavage site and the cleaved S2 domain is primed by the activated serine protease domain (SPD) of humanTMPRSS2 to become S2'. The activated SPD of TMPRSS2 is formed after it is cleaved by autocatalysis from the membrane bound non-catalytic ectodomain (hNECD) comprising of LDLRA CLASS-I repeat and a SRCR domain. It is known that the SRCR domains as well as LDLRA repeat harboring proteins mediate endocytosis of viruses and certain ligands. Based on this, we put forward a hypothesis that the exposed hNECD binds to the S2' as both are at an interaction proximity soon after S2 is processed by the SPD and this interaction may lead to the endocytosis of virus. Based on this hypothesis we have modelled the hNECD structure, followed by docking studies with the known 3D structure of S2'. The interaction interface of hNECD with S2' was further used for virtual screening of FDA-approved drug molecules and Indian medicinal plant-based compounds. We also mapped the known mutations of concern and mutations of interest on interaction interface of S2' and found that none of the known mutations map onto the interaction interface. This indicates that targeting the interaction between the hNECD of TMPRSS2 and S2' may serve as an attractive therapeutic target.Communicated by Ramaswamy H. Sarma.

15.
Mar Drugs ; 20(6)2022 Jun 16.
Article in English | MEDLINE | ID: covidwho-1964023

ABSTRACT

Coronavirus disease 2019, caused by the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global pandemic that poses an unprecedented threat to the global economy and human health. Several potent inhibitors targeting SARS-CoV-2 have been published; however, most of them have failed in clinical trials. This study aimed to assess the therapeutic compounds among aldehyde derivatives from seaweeds as potential SARS-CoV-2 inhibitors using a computer simulation protocol. The absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties of the compounds were analyzed using a machine learning algorithm, and the docking simulation of these compounds to the 3C-like protease (Protein Data Bank (PDB) ID: 6LU7) was analyzed using a molecular docking protocol based on the CHARMm algorithm. These compounds exhibited good drug-like properties following the Lipinski and Veber rules. Among the marine aldehyde derivatives, 4-hydroxybenzaldehyde, 3-hydroxybenzaldehyde, 3,4-dihydroxybenzaldehyde, and 5-bromoprotocatechualdehyde were predicted to have good absorption and solubility levels and non-hepatotoxicity in the ADME/Tox prediction. 3-hydroxybenzaldehyde and 3,4-dihydroxybenzaldehyde were predicted to be non-toxic in TOPKAT prediction. In addition, 3,4-dihydroxybenzaldehyde was predicted to exhibit interactions with the 3C-like protease, with binding energies of -71.9725 kcal/mol. The computational analyses indicated that 3,4-dihydroxybenzaldehyde could be regarded as potential a SARS-CoV-2 inhibitor.


Subject(s)
COVID-19 , Seaweed , Aldehydes/pharmacology , Antiviral Agents/chemistry , Antiviral Agents/pharmacology , COVID-19/drug therapy , Computer Simulation , Coronavirus 3C Proteases , Humans , Molecular Docking Simulation , Molecular Dynamics Simulation , Protease Inhibitors/pharmacology , SARS-CoV-2 , Seaweed/metabolism , Viral Nonstructural Proteins/chemistry
16.
IEEE Transactions on Emerging Topics in Computing ; : 1-12, 2022.
Article in English | Scopus | ID: covidwho-1961439

ABSTRACT

The social and economic impact of the COVID-19 pandemic demands a reduction of the time required to find a therapeutic cure. In this paper, we describe the EXSCALATE molecular docking platform capable to scale on an entire modern supercomputer for supporting extreme-scale virtual screening campaigns. Such virtual experiments can provide in short time information on which molecules to consider in the next stages of the drug discovery pipeline, and it is a key asset in case of a pandemic. The EXSCALATE platform has been designed to benefit from heterogeneous computation nodes and to reduce scaling issues. In particular, we maximized the accelerators’usage, minimized the communications between nodes, and aggregated the I/O requests to serve them more efficiently. Moreover, we balanced the computation across the nodes by designing an ad-hoc workflow based on the execution time prediction of each molecule. We deployed the platform on two HPC supercomputers, with a combined computational power of 81 PFLOPS, to evaluate the interaction between 70 billion of small molecules and 15 binding-sites of 12 viral proteins of SARS-CoV-2. The experiment lasted 60 hours and it performed more than one trillion ligand-pocket evaluations, setting a new record on the virtual screening scale. IEEE

17.
Energy Nexus ; 6: 100080, 2022 Jun 16.
Article in English | MEDLINE | ID: covidwho-1946138

ABSTRACT

The novel coronavirus 2019 is spreading around the world and causing serious concern. However, there is limited information about novel coronavirus that hinders the design of effective drug. Bioactive compounds are rich source of chemo preventive ingredients. In our present research focuses on identifying and recognizing bioactive chemicals in Lantana camara, by evaluating their potential toward new coronaviruses and confirming the findings using molecular docking, ADMET, network analysis and dynamics investigations.. The spike protein receptor binding domain were docked with 25 identified compounds and 2,4-Ditertbutyl-phenol (-6.3kcal/mol) shows highest docking score, its interactions enhances the increase in binding and helps to identify the biological activity. The ADME/toxicity result shows that all the tested compounds can serve as inhibitors of the enzymes CYP1A2 and CYP2D6. In addition, Molecular dynamics simulations studies with reference inhibitors were carried out to test the stability. This study identifies the possible active molecules against the receptor binding domain of spike protein, which can be further exploited for the treatment of novel coronavirus 2019. The results of the toxicity risk for phytocompounds and their active derivatives showed a moderate to good drug score.

18.
Comput Biol Med ; 147: 105709, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1944685

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the contagious coronavirus disease 2019 (COVID-19) which was first identified in Wuhan, China, in December 2019. Around the world, many researchers focused their research on identifying inhibitors against the druggable SARS-CoV-2 targets. The reported genomic mutations have a direct effect on the receptor-binding domain (RBD), which interacts with host angiotensin-converting enzyme 2 (ACE-2) for viral cell entry. These mutations, some of which are variants of concern (VOC), lead to increased morbidity and mortality rates. The newest variants including B.1.617.2 (Delta), AY.1 (Delta plus), and C.37 (Lambda) were considered in this study. Thus, an exhaustive structure-based virtual screening of a ligand library (in which FDA approved drugs are also present) using the drug-likeness screening, molecular docking, ADMET profiling was performed followed by molecular dynamics (MD) simulation, and Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) calculation to identify compounds or drugs can be repurposed for inhibiting the wild type, Delta, Delta plus and Lambda variants of RBD of the spike protein. Based on the virtual screening steps, two FDA approved drugs, Atovaquone (atv) and Praziquantel (prz), were selected and repurposed as the best candidates of SARS-CoV-2 RBD inhibitors. Molecular docking results display that both atv and prz contribute in different interaction with binding site residues (Gln493, Asn501 and Gly502 in the hydrogen bond formation, Phe490 and Tyr505 in the π- π stacking and Tyr449, Ser494, and Phe497 in the vdW interactions) in the wild type, Delta, Delta plus and Lambda variants of RBD of the spike protein. MD simulations revealed that among the eight studied complexes, the wild type-atv and Delta-prz complexes have the most structural stability over the simulation time. Furthermore, MM-PBSA calculation showed that in the atv containing complexes, highest binding affinity is related to the wild type-atv complex and in the prz containing complexes, it is related to the Delta-prz complex. The validation of docking results was done by comparing with experimental data (heparin in complex with wild type and Delta variants). Also, comparison of the obtained results with the result of simulation of the k22 with the studied proteins showed that atv and prz are suitable inhibitors for these proteins, especially wild type t and Delta variant, respectively. Thus, we found that atv and prz are the best candidate for inhibition of wild type and Delta variant of the spike protein. Also, atv can be an appropriate inhibitor for the Lambda variant. Obtained in silico results may help the development of new anti-COVID-19 drugs.


Subject(s)
COVID-19 , SARS-CoV-2 , Adipates , COVID-19/drug therapy , COVID-19/genetics , Drug Repositioning/methods , Humans , Molecular Docking Simulation , Molecular Dynamics Simulation , Mutation/genetics , Peptidyl-Dipeptidase A/chemistry , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/chemistry , Succinates
19.
Antiviral Res ; 204: 105350, 2022 08.
Article in English | MEDLINE | ID: covidwho-1944193

ABSTRACT

Two years after its emergence, SARS-CoV-2 still represents a serious and global threat to human health. Antiviral drug development usually takes a long time and, to increase the chances of success, chemical variability of hit compounds represents a valuable source for the discovery of new antivirals. In this work, we applied a platform of variably oriented virtual screening campaigns to seek for novel chemical scaffolds for SARS-CoV-2 main protease (Mpro) inhibitors. The study on the resulting 30 best hits led to the identification of a series of structurally unrelated Mpro inhibitors. Some of them exhibited antiviral activity in the low micromolar range against SARS-CoV-2 and other human coronaviruses (HCoVs) in different cell lines. Time-of-addition experiments demonstrated an antiviral effect during the viral replication cycle at a time frame consistent with the inhibition of SARS-CoV-2 Mpro activity. As a proof-of-concept, to validate the pharmaceutical potential of the selected hits against SARS-CoV-2, we rationally optimized one of the hit compounds and obtained two potent SARS-CoV-2 inhibitors with increased activity against Mpro both in vitro and in a cellular context, as well as against SARS-CoV-2 replication in infected cells. This study significantly contributes to the expansion of the chemical variability of SARS-CoV-2 Mpro inhibitors and provides new scaffolds to be exploited for pan-coronavirus antiviral drug development.


Subject(s)
Antiviral Agents , Coronavirus 3C Proteases , Protease Inhibitors , SARS-CoV-2 , Antiviral Agents/chemistry , Antiviral Agents/pharmacology , Coronavirus 3C Proteases/antagonists & inhibitors , Molecular Docking Simulation , Protease Inhibitors/chemistry , SARS-CoV-2/drug effects , SARS-CoV-2/enzymology
20.
Struct Chem ; : 1-15, 2022 Jun 02.
Article in English | MEDLINE | ID: covidwho-1942564

ABSTRACT

Coronavirus disease 2019 (COVID-19) has become a major challenge affecting almost every corner of the world, with more than five million deaths worldwide. Despite several efforts, no drug or vaccine has shown the potential to check the ever-mutating SARS-COV-2. The emergence of novel variants is a major concern increasing the need for the discovery of novel therapeutics for the management of this pandemic. Out of several potential drug targets such as S protein, human ACE2, TMPRSS2 (transmembrane protease serine 2), 3CLpro, RdRp, and PLpro (papain-like protease), RNA-dependent RNA polymerase (RdRP) is a vital enzyme for viral RNA replication in the mammalian host cell and is one of the legitimate targets for the development of therapeutics against this disease. In this study, we have performed structure-based virtual screening to identify potential hit compounds against RdRp using molecular docking of a commercially available small molecule library of structurally diverse and drug-like molecules. Since non-optimal ADME properties create hurdles in the clinical development of drugs, we performed detailed in silico ADMET prediction to facilitate the selection of compounds for further studies. The results from the ADMET study indicated that most of the hit compounds had optimal properties. Moreover, to explore the conformational dynamics of protein-ligand interaction, we have performed an atomistic molecular dynamics simulation which indicated a stable interaction throughout the simulation period. We believe that the current findings may assist in the discovery of drug candidates against SARS-CoV-2.

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