Identification of phytochemical compounds of Fagopyrum dibotrys and their targets by metabolomics, network pharmacology and molecular docking studies.

Acute lung injury (ALI) is a clinically severe lung illness with high incidence rate and mortality. Especially, coronavirus disease 2019 (COVID-19) poses a serious threat to world wide governmental fitness. It has distributed to almost from corner to corner of the universe, and the situation in the prevention and control of COVID-19 remains grave. Traditional Chinese medicine plays a vital role in the precaution and therapy of sicknesses. At present, there is a lack of drugs for treating these diseases, so it is necessary to develop drugs for treating COVID-19 related ALI. Fagopyrum dibotrys (D. Don) Hara is an annual plant of the Polygonaceae family and one of the long-history used traditional medicine in China. In recent years, its rhizomes (medicinal parts) have attracted the attention of scholars at home and abroad due to their significant anti-inflammatory, antibacterial and anticancer activities. It can work on SARS-COV-2 with numerous components, targets, and pathways, and has a certain effect on coronavirus disease 2019 (COVID-19) related acute lung injury (ALI). However, there are few systematic studies on its aerial parts (including stems and leaves) and its potential therapeutic mechanism has not been studied. The phytochemical constituents of rhizome of F. dibotrys were collected using TCMSP database. And metabolites of F. dibotrys' s aerial parts were detected by metabonomics. The phytochemical targets of F. dibotrys were predicted by the PharmMapper website tool. COVID-19 and ALI-related genes were retrieved from GeneCards. Cross targets and active phytochemicals of COVID-19 and ALI related genes in F. dibotrys were enriched by gene ontology (GO) and KEGG by metscape bioinformatics tools. The interplay network entre active phytochemicals and anti COVID-19 and ALI targets was established and broke down using Cytoscape software. Discovery Studio (version 2019) was used to perform molecular docking of crux active plant chemicals with anti COVID-19 and ALI targets. We identified 1136 chemicals from the aerial parts of F. dibotrys, among which 47 were active flavonoids and phenolic chemicals. A total of 61 chemicals were searched from the rhizome of F. dibotrys, and 15 of them were active chemicals. So there are 6 commonly key active chemicals at the aerial parts and the rhizome of F. dibotrys, 89 these phytochemicals's potential targets, and 211 COVID-19 and ALI related genes. GO enrichment bespoken that F. dibotrys might be involved in influencing gene targets contained numerous biological processes, for instance, negative regulation of megakaryocyte differentiation, regulation of DNA metabolic process, which could be put down to its anti COVID-19 associated ALI effects. KEGG pathway indicated that viral carcinogenesis, spliceosome, salmonella infection, coronavirus disease - COVID-19, legionellosis and human immunodeficiency virus 1 infection pathway are the primary pathways obsessed in the anti COVID-19 associated ALI effects of F. dibotrys. Molecular docking confirmed that the 6 critical active phytochemicals of F. dibotrys, such as luteolin, (+) -epicatechin, quercetin, isorhamnetin, (+) -catechin, and (-) -catechin gallate, can combine with kernel therapeutic targets NEDD8, SRPK1, DCUN1D1, and PARP1. In vitro activity experiments showed that the total antioxidant capacity of the aerial parts and rhizomes of F. dibotrys increased with the increase of concentration in a certain range. In addition, as a whole, the antioxidant capacity of the aerial part of F. dibotrys was stronger than that of the rhizome. Our research afford cues for farther exploration of the anti COVID-19 associated ALI chemical compositions and mechanisms of F. dibotrys and afford scientific foundation for progressing modern anti COVID-19 associated ALI drugs based on phytochemicals in F. dibotrys. We also fully developed the medicinal value of F. dibotrys' s aerial parts, which can effectively avoid the waste of resources. Meanwhile, our work provides a new strategy for integrating metabonomics, network pharmacology, and molecular docking techniques which was an efficient way for recognizing effective constituents and mechanisms valid to the pharmacologic actions of traditional Chinese medicine.

D155Y substitution of SARS-CoV-2 ORF3a weakens binding with Caveolin-1.

The clinical manifestation of the recent pandemic COVID-19, caused by the novel SARS-CoV-2 virus, varies from mild to severe respiratory illness. Although environmental, demographic and co-morbidity factors have an impact on the severity of the disease, contribution of the mutations in each of the viral genes towards the degree of severity needs a deeper understanding for designing a better therapeutic approach against COVID-19. Open Reading Frame-3a (ORF3a) protein has been found to be mutated at several positions. In this work, we have studied the effect of one of the most frequently occurring mutants, D155Y of ORF3a protein, found in Indian COVID-19 patients. Using computational simulations we demonstrated that the substitution at 155th changed the amino acids involved in salt bridge formation, hydrogen-bond occupancy, interactome clusters, and the stability of the protein compared with the other substitutions found in Indian patients. Protein-protein docking using HADDOCK analysis revealed that substitution D155Y weakened the binding affinity of ORF3a with caveolin-1 compared with the other substitutions, suggesting its importance in the overall stability of ORF3a-caveolin-1 complex, which may modulate the virulence property of SARS-CoV-2.

Screening and identification of potential MERS-CoV papain-like protease (PLpro) inhibitors; Steady-state kinetic and Molecular dynamic studies.

MERS-CoV belongs to the coronavirus group. Recent years have seen a rash of coronavirus epidemics. In June 2012, MERS-CoV was discovered in the Kingdom of Saudi Arabia, with 2,591 MERSA cases confirmed by lab tests by the end of August 2022 and 894 deaths at a case-fatality ratio (CFR) of 34.5% documented worldwide. Saudi Arabia reported the majority of these cases, with 2,184 cases and 813 deaths (CFR: 37.2%), necessitating a thorough understanding of the molecular machinery of MERS-CoV. To develop antiviral medicines, illustrative investigation of the protein in coronavirus subunits are required to increase our understanding of the subject. In this study, recombinant expression and purification of MERS-CoV (PLpro), a primary goal for the development of 22 new inhibitors, were completed using a high throughput screening methodology that employed fragment-based libraries in conjunction with structure-based virtual screening. Compounds 2, 7, and 20, showed significant biological activity. Moreover, a docking analysis revealed that the three compounds had favorable binding mood and binding free energy. Molecular dynamic simulation demonstrated the stability of compound 2 (2-((Benzimidazol-2-yl) thio)-1-arylethan-1-ones) the strongest inhibitory activity against the PLpro enzyme. In addition, disubstitutions at the meta and para locations are the only substitutions that may boost the inhibitory action against PLpro. Compound 2 was chosen as a MERS-CoV PLpro inhibitor after passing absorption, distribution, metabolism, and excretion studies; however, further investigations are required.

ARTIFICIAL INTELLIGENCE (AI) MODEL: ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) FOR DIAGNOSIS OF COVID-19 INFLUENZA

The COVID-19 influenza became a curse on the world. It has been around for two years, so no one needs to make a big introduction of it. It has became a significant challenge around the world. Owing to this, we made dynamic networks using an amalgamating of fuzzy logic and neural networks for the prediction of sufferers of COVID-19. These hybrid networks serve for the assessment of the COVID-19 victims and usefully serve for the assessment of the medical resources needed for future victims. This manuscript proposed Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction model for COVID-19 prediction in Andhra Pradesh, India. We gathered data on positive COVID-19 sufferers in Andhra Pradesh for this purpose. The data can be separated into three categories: training set, testing set and checking set. We have utilized Root Mean Square Deviation (RMSD) for prediction precision. If the prediction model has a lower RMSD value, it is regarded as the best forecast. In this study, we concluded that the 3 Triangular MFns for each input were excellent with the extreme precision for all of the districts based on our expertise. In the end, we deployed seven SANFIS replicas in Andhra Pradesh, but we discovered that SANFIS6 and SANFIS7 provided excellent COVID-19 prediction results. These findings will assist the government, healthcare agencies, and medical organizations in planning for future COVID-19 victims' medical requirements. These sorts of Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction models based on Artificial Intelligence (AI) will be beneficial in overcoming the COVID-19. © 2022 Slovak Academy of Sciences. All rights reserved.

Artificial Intelligence (Ai) Model: Adaptive Neuro-Fuzzy Inference System (Anfis) for Diagnosis of Covid-19 Influenza

The COVID-19 influenza became a curse on the world. It has been around for two years, so no one needs to make a big introduction of it. It has became a significant challenge around the world. Owing to this, we made dy-namic networks using an amalgamating of fuzzy logic and neural networks for the prediction of sufferers of COVID-19. These hybrid networks serve for the assess-ment of the COVID-19 victims and usefully serve for the assessment of the medical resources needed for future victims. This manuscript proposed Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction model for COVID-19 predic-tion in Andhra Pradesh, India. We gathered data on positive COVID-19 sufferers in Andhra Pradesh for this purpose. The data can be separated into three categories: training set, testing set and checking set. We have utilized Root Mean Square Devi-ation (RMSD) for prediction precision. If the prediction model has a lower RMSD value, it is regarded as the best forecast. In this study, we concluded that the 3 Tri-angular MFns for each input were excellent with the extreme precision for all of the districts based on our expertise. In the end, we deployed seven SANFIS replicas in Andhra Pradesh, but we discovered that SANFIS6 and SANFIS7 provided excellent COVID-19 prediction results. These findings will assist the government, healthcare agencies, and medical organizations in planning for future COVID-19 victims' med-ical requirements. These sorts of Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction models based on Artificial Intelligence (AI) will be beneficial in overcoming the COVID-19.

Glycyrrhizin as a promising kryptonite against SARS-CoV-2: Clinical, experimental, and theoretical evidences.

COVID-19 is the most devastating disease in recent times affecting most people globally. The higher rate of transmissibility and mutations of SARS-CoV-2 along with the lack of potential therapeutics has made it a global crisis. Potential molecules from natural sources could be a fruitful remedy to combat COVID-19. This systematic review highlights the detailed therapeutic implication of naturally occurring glycyrrhizin and its related derivatives against COVID-19. Glycyrrhizin has already been established for blocking different biomolecular targets related to the SARS-CoV-2 replication cycle. In this article, several experimental and theoretical evidences of glycyrrhizin and related derivatives have been discussed in detail to evaluate their potential as a promising therapeutic strategy against COVID-19. Moreover, the implication of glycyrrhizin in traditional Chinese medicines for alleviating the symptoms of COVID-19 has been reviewed. The potential role of glycyrrhizin and related compounds in affecting various stages of the SARS-CoV-2 life cycle has also been discussed in detail. Derivatization of glycyrrhizin for designing potential lead compounds along with combination therapy with other anti-SARS-CoV-2 agents followed by extensive evaluation may assist in the formulation of novel anti-coronaviral therapy for better treatment to combat COVID-19.

Identification of alkaloids from Justicia adhatoda as potent SARS CoV-2 main protease inhibitors: An in silico perspective.

The COVID-19 pandemic, caused by SARS CoV-2, is responsible for millions of death worldwide. No approved/proper therapeutics is currently available which can effectively combat this outbreak. Several attempts have been undertaken in the search of effective drugs to control the spread of SARS CoV-2 infection. The main protease (Mpro), key component for the cleavage of the viral polyprotein, is considered to be one of the important drug targets for treating COVID-19. Various phytochemicals, including polyphenols and alkaloids, have been proposed as potent inhibitors of Mpro. The alkaloids from leaf extracts of Justicia adhatoda have also been reported to possess anti-viral activity. But whether these alkaloids exhibit any inhibitory effect on SARS CoV-2 Mpro is far from clear. To explore this in detail, we have adopted computational approaches. Justicia adhatoda alkaloids possessing proper drug-likeness properties and two anti-HIV drugs (lopinavir and darunavir; having binding affinity -7.3 to -7.4 kcal/mol) were docked against SARS CoV-2 Mpro to study their binding properties. Only one alkaloid (anisotine) had interaction with both the catalytic residues (His41 and Cys145) of Mpro and exhibited good binding affinity (-7.9 kcal/mol). Molecular dynamic simulations (100 ns) revealed that Mpro-anisotine complex is more stable, conformationally less fluctuated; slightly less compact and marginally expanded than Mpro-darunavir/lopinavir complex. Even the number of intermolecular H-bonds and MM-GBSA analysis suggested that anisotine is a more potent Mpro inhibitor than the two previously recommended antiviral drugs (lopinavir and darunavir) and may evolve as a promising anti-COVID-19 drug if proven in animal experiments and on patients.

Benchmarked molecular docking integrated molecular dynamics stability analysis for prediction of SARS-CoV-2 papain-like protease inhibition by olive secoiridoids.

Objectives: We performed a virtual screening of olive secoiridoids of the OliveNetTM library to predict SARS-CoV-2 PLpro inhibition. Benchmarked molecular docking protocol that evaluated the performance of two docking programs was applied to execute virtual screening. Molecular dynamics stability analysis of the top-ranked olive secoiridoid docked to PLpro was also carried out. Methods: Benchmarking virtual screening used two freely available docking programs, AutoDock Vina 1.1.2. and AutoDock 4.2.1. for molecular docking of olive secoiridoids to a single PLpro structure. Screening also included benchmark structures of known active and decoy molecules from the DEKOIS 2.0 library. Based on the predicted binding energies, the docking programs ranked the screened molecules. We applied the usual performance evaluation metrices to evaluate the docking programs using the predicted ranks. Molecular dynamics of the top-ranked olive secoiridoid bound to PLpro and computation of MM-GBSA energy using three iterations during the last 50 ps of the analysis of the dynamics in Desmond supported the stability prediction. Results and discussions: Predictiveness curves suggested that AutoDock Vina has a better predictive ability than AutoDock, although there was a moderate correlation between the active molecules rankings (Kendall's correlation of rank (τ) = 0.581). Interestingly, two same molecules, Demethyloleuropein aglycone, and Oleuroside enriched the top 1 % ranked olive secoiridoids predicted by both programs. Demethyloleuropein aglycone bound to PLpro obtained by docking in AutoDock Vina when analyzed for stability by molecular dynamics simulation for 50 ns displayed an RMSD, RMSF<2 Å, and MM-GBSA energy of -94.54 ± 6.05 kcal/mol indicating good stability. Molecular dynamics also revealed the interactions of Demethyloleuropein aglycone with binding sites 2 and 3 of PLpro, suggesting a potent inhibition. In addition, for 98 % of the simulation time, two phenolic hydroxy groups of Demethyloleuropein aglycone maintained two hydrogen bonds with Asp302 of PLpro, specifying the significance of the groups in receptor binding. Conclusion: AutoDock Vina retrieved the active molecules accurately and predicted Demethyloleuropein aglycone as the best inhibitor of PLpro. The Arabian diet consisting of olive products rich in secoiridoids benefits from the PLpro inhibition property and reduces the risk of viral infection.

Rational design of an anti-cancer peptide inhibiting CD147 / Cyp A interaction.

The CD147 / Cyp A interaction is a critical pathway in cancer types and an essential factor in entering the COVID-19 virus into the host cell. Melittin acts as an inhibitory peptide in cancer types by blocking the CD147/ Cyp A interaction. The clinical application of Melittin is limited due to weak penetration into cancer cells. TAT is an arginine-rich peptide with high penetration ability into cells widely used in drug delivery systems. This study aimed to design a hybrid peptide derived from Melittin and TAT to inhibit CD147 /Cyp A interaction. An amino acid region with high anti-cancer activity in Melittin was selected based on the physicochemical properties. Based on the results, a truncated Melittin peptide with 15 amino acids by the GGGS linker was fused to a TAT peptide (nine amino acids) to increase the penetration rate into the cell. A new hybrid peptide analog(TM) was selected by replacing the glycine with serine based on random point mutation. Docking results indicated that the TM peptide acts as an inhibitory peptide with high binding energy when interacting with CD147 and the CypA proteins. RMSD and RMSF results confirmed the high stability of the TM peptide in interaction with CD147. Also, the coarse-grained simulation showed the penetration potential of TM peptide into the DOPS-DOPC model membrane. Our findings indicated that the designed multifunctional peptide could be an attractive therapeutic candidate to halter tumor types and COVID-19 infection.

Elucidating the enhanced binding affinity of a double mutant SP-D with trimannose on the influenza A virus using molecular dynamics.

Surfactant protein D (SP-D) is an essential component of the human pulmonary surfactant system, which is crucial in the innate immune response against glycan-containing pathogens, including Influenza A viruses (IAV) and SARS-CoV-2. Previous studies have shown that wild-type (WT) SP-D can bind IAV but exhibits poor antiviral activities. However, a double mutant (DM) SP-D consisting of two point mutations (Asp325Ala and Arg343Val) inhibits IAV more potently. Presently, the structural mechanisms behind the point mutations' effects on SP-D's binding affinity with viral surface glycans are not fully understood. Here we use microsecond-scale, full-atomistic molecular dynamics (MD) simulations to understand the molecular mechanism of mutation-induced SP-D's higher antiviral activity. We find that the Asp325Ala mutation promotes a trimannose conformational change to a more stable state. Arg343Val increases the binding with trimannose by increasing the hydrogen bonding interaction with Glu333. Free energy perturbation (FEP) binding free energy calculations indicate that the Arg343Val mutation contributes more to the increase of SP-D's binding affinity with trimannose than Asp325Ala. This study provides a molecular-level exploration of how the two mutations increase SP-D binding affinity with trimannose, which is vital for further developing preventative strategies for related diseases.

Unsymmetrical aromatic disulfides as SARS-CoV-2 Mpro inhibitors: Molecular docking, molecular dynamics, and ADME scoring investigations.

COVID-19 pandemic caused by very severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) agent is an ongoing major global health concern. The disease has caused more than 452 million affected cases and more than 6 million death worldwide. Hence, there is an urgency to search for possible medications and drug treatments. There are no approved drugs available to treat COVID-19 yet, although several vaccine candidates are already available and some of them are listed for emergency use by the world health organization (WHO). Identifying a potential drug candidate may make a significant contribution to control the expansion of COVID-19. The in vitro biological activity of asymmetric disulfides against coronavirus through the inhibition of SARS-CoV-2 main protease (Mpro) protein was reported. Due to the lack of convincing evidence those asymmetric disulfides have favorable pharmacological properties for the clinical treatment of Coronavirus, in silico evaluation should be performed to assess the potential of these compounds to inhibit the SARS-CoV-2 Mpro. In this context, we report herein the molecular docking for a series of 40 unsymmetrical aromatic disulfides as SARS-CoV-2 Mpro inhibitor. The optimal binding features of disulfides within the binding pocket of SARS-CoV-2 endoribonuclease protein (Protein Data Bank [PDB]: 6LU7) was described. Studied compounds were ranked for potential effectiveness, and those have shown high molecular docking scores were proposed as novel drug candidates against SARS-CoV-2. Moreover, the outcomes of drug similarity and ADME (Absorption, Distribution, Metabolism, and Excretion) analyses have may have the effectiveness of acting as medicines, and would be of interest as promising starting point for designing compounds against SARS-CoV-2. Finally, the stability of these three compounds in the complex with Mpro was validated through molecular dynamics (MD) simulation, in which they displayed stable trajectory and molecular properties with a consistent interaction profile.

Characterization of mutations modulating enhanced transmissibility of SARS-CoV-2 B.1.617+ (Delta) variant using In Silico tools.

Since the beginning of the of SARS-CoV-2 (Covid-19) pandemic, variants of concern (VOC) have emerged taxing health systems worldwide. In October 2020, a new variant of SARS-CoV-2 (B.1.617+/Delta variant) emerged in India, triggering a deadly wave of Covid-19. Epidemiological data strongly suggests that B.1.617+ is more transmissible and previous reports have revealed that B.1.617+ has numerous mutations compared to wild type (WT), including several changes in the spike protein (SP). The main goal of this study was to use In Silico (computer simulation) techniques to examine mutations in the SP, specifically L452R and E484Q (part of the receptor binding domain (RBD) for human angiotensin-converting enzyme 2 (hACE2)) and P681R (upstream of the Furin cleavage motif), for effects in modulating the transmissibility of the B.1.617+ variant. Using computational models, the binding free energy (BFE) and H-bond lengths were calculated for SP-hACE2 and SP-Furin complexes. Comparison of the SP-hACE2 complex in the WT and B.1.617+ revealed both complexes have identical receptor-binding modes but the total BFE of B.1.617+ binding was more favorable for complex formation than WT, suggesting L452R and E484Q have a moderate impact on binding affinity. In contrast, the SP-Furin complex of B.1.617+ substantially lowered the BFE and revealed changes in molecular interactions compared to the WT complex, implying stronger complex formation between the variant and Furin. This study provides an insight into mutations that modulate transmissibility of the B.1.617+ variant, specifically the P681R mutation which appears to enhance transmissibility of the B.1.617+ variant by rendering it more receptive to Furin.

Nature's therapy for COVID-19: Targeting the vital non-structural proteins (NSP) from SARS-CoV-2 with phytochemicals from Indian medicinal plants.

Background: Containing COVID-19 is still a global challenge. It has affected the "normal" world by targeting its economy and health sector. The effect is shifting of focus of research from life threatening diseases like cancer. Thus, we need to develop a medical solution at the earliest. The purpose of this present work was to understand the efficacy of 22 rationally screened phytochemicals from Indian medicinal plants obtained from our previous work, following drug-likeness properties, against 6 non-structural-proteins (NSP) from SARS-CoV-2. Methods: 100 ns molecular dynamics simulations were performed, and relative binding free energies were computed by MM/PBSA. Further, principal component analysis, dynamic cross correlation and hydrogen bond occupancy were analyzed to characterize protein-ligand interactions. Biological pathway enrichment analysis was also carried out to elucidate the therapeutic targets of the phytochemicals in comparison to SARS-CoV-2. Results: The potential binding modes and favourable molecular interaction profile of 9 phytochemicals, majorly from Withania somnifera with lowest free binding energies, against the SARS-CoV-2 NSP targets were identified. It was understood that phytochemicals and 2 repurposed drugs with steroidal moieties in their chemical structures formed stable interactions with the NSPs. Additionally, human target pathway analysis for SARS-CoV-2 and phytochemicals showed that cytokine mediated pathway and phosphorylation pathways were with the most significant p-value. Conclusions: To summarize this work, we suggest a global approach of targeting multiple proteins of SARS-CoV-2 with phytochemicals as a natural alternative therapy for COVID-19. We also suggest that these phytochemicals need to be tested experimentally to confirm their efficacy.

Montelukast is a dual-purpose inhibitor of SARS-CoV-2 infection and virus-induced IL-6 expression identified by structure-based drug repurposing.

Drug-repurposing has been instrumental to identify drugs preventing SARS-CoV-2 replication or attenuating the disease course of COVID-19. Here, we identify through structure-based drug-repurposing a dual-purpose inhibitor of SARS-CoV-2 infection and of IL-6 production by immune cells. We created a computational structure model of the receptor binding domain (RBD) of the SARS-CoV-2 spike 1 protein, and used this model for insilico screening against a library of 6171 molecularly defined binding-sites from drug molecules. Molecular dynamics simulation of candidate molecules with high RBD binding-scores in docking analysis predicted montelukast, an antagonist of the cysteinyl-leukotriene-receptor, to disturb the RBD structure, and infection experiments demonstrated inhibition of SARS-CoV-2 infection, although montelukast binding was outside the ACE2-binding site. Molecular dynamics simulation of SARS-CoV-2 variant RBDs correctly predicted interference of montelukast with infection by the beta but not the more infectious alpha variant. With distinct binding sites for RBD and the leukotriene receptor, montelukast also prevented SARS-CoV-2-induced IL-6 release from immune cells. The inhibition of SARS-CoV-2 infection through a molecule binding distal to the ACE-binding site of the RBD points towards an allosteric mechanism that is not conserved in the more infectious alpha and delta SARS-CoV-2 variants.

Identification of Papain-Like Protease inhibitors of SARS CoV-2 through HTVS, Molecular docking, MMGBSA and Molecular dynamics approach.

Coronaviruses (CoVs) are a large group of enveloped positive sense single-stranded RNA viruses that can cause disease to humans. These are zoonotic having potential to cause large-scale outbreaks of infections widely causing morbidity and mortality. Papain-Like Proteases (PLpro) is a cysteine protease, essential for viral replication and proliferation, as a highly conserved enzyme it cleaves peptide linkage between Nsp1, Nsp2, Nsp3, and Nsp4. As a valid therapeutic targets it stops viral reproduction and boosts host immune response thereby halting further spread of infection. In the purpose of identifying inhibitors targeting Papain-Like Proteases (PLpro) we initiated a high throughput virtual screening (HTVS) protocol using a SuperNatural Database. The XP docking results revealed that two compounds SN00334175 and SN00162745 exhibited docking score of -10.58 kcal/mol and -9.93 kcal/mol respectively. The Further PRIME MMGB-SA studies revealed Van der Waal energy and hydrophobic energy terms as major contributors for total binding free energy. The 100 ns molecular dynamics simulation of SN00334175/7JN2 and SN00162745/7JN2 revealed that these complexes were stabilized with ligand binding forming interactions with Gly266, Asn267, Tyr268, Tyr273, Thr301 and Asp302, Lys157, Leu162, Asp164, Arg166, Glu167, Pro248, Tyr264.

Computational screening of phytochemicals from three medicinal plants as inhibitors of transmembrane protease serine 2 implicated in SARS-CoV-2 infection.

Background: SARS-CoV-2 infection or COVID-19 is a major global public health issue that requires urgent attention in terms of drug development. Transmembrane Protease Serine 2 (TMPRSS2) is a good drug target against SARS-CoV-2 because of the role it plays during the viral entry into the cell. Virtual screening of phytochemicals as potential inhibitors of TMPRSS2 can lead to the discovery of drug candidates for the treatment of COVID-19. Purpose: The study was designed to screen 132 phytochemicals from three medicinal plants traditionally used as antivirals; Zingiber officinalis Roscoe (Zingiberaceae), Artemisia annua L. (Asteraceae), and Moringa oleifera Lam. (Moringaceae), as potential inhibitors of TMPRSS2 for the purpose of finding therapeutic options to treat COVID-19. Methods: Homology model of TMPRSS2 was built using the ProMod3 3.1.1 program of the SWISS-MODEL. Binding affinities and interaction between compounds and TMPRSS2 model was examined using molecular docking and molecular dynamics simulation. The drug-likeness and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of potential inhibitors of TMPRSS2 were also assessed using admetSAR web tool. Results: Three compounds, namely, niazirin, quercetin, and moringyne from M. oleifera demonstrated better molecular interactions with binding affinities ranging from -7.1 to -8.0 kcal/mol compared to -7.0 kcal/mol obtained for camostat mesylate (a known TMPRSS2 inhibitor), which served as a control. All the three compounds exhibited good drug-like properties by not violating the Lipinski rule of 5. Niazirin and moringyne possessed good ADMET properties and were stable in their interactions with the TMPRSS2 based on the molecular dynamics simulation. However, the ADMET tool predicted the potential hepatotoxic and mutagenic effects of quercetin. Conclusion: This study demonstrated the potentials of niazirin, quercetin, and moringyne from M. oleifera, to inhibit the activities of human TMPRSS2, thus probably being good candidates for further development as new drugs for the treatment or management of COVID-19.

Identification of SARS-CoV-2 RNA-dependent RNA polymerase inhibitors from the major phytochemicals of Nigella sativa: An in silico approach.

The coronavirus disease 2019 (COVID-19), which emerged in December 2019, continues to be a serious health concern worldwide. There is an urgent need to develop effective drugs and vaccines to control the spread of this disease. In the current study, the main phytochemical compounds of Nigella sativa were screened for their binding affinity for the active site of the RNA-dependent RNA polymerase (RdRp) enzyme of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The binding affinity was investigated using molecular docking methods, and the interaction of phytochemicals with the RdRp active site was analyzed and visualized using suitable software. Out of the nine phytochemicals of N. sativa screened in this study, a significant docking score was observed for four compounds, namely α-hederin, dithymoquinone, nigellicine, and nigellidine. Based on the findings of our study, we report that α-hederin, which was found to possess the lowest binding energy (-8.6 kcal/mol) and hence the best binding affinity, is the best inhibitor of RdRp of SARS-CoV-2, among all the compounds screened here. Our results prove that the top four potential phytochemical molecules of N. sativa, especially α-hederin, could be considered for ongoing drug development strategies against SARS-CoV-2. However, further in vitro and in vivo testing are required to confirm the findings of this study.

Phytoconstituents from ten natural herbs as potent inhibitors of main protease enzyme of SARS-COV-2: In silico study.

Background: Lack of treatment of novel Coronavirus disease led to the search of specific antivirals that are capable to inhibit the replication of the virus. The plant kingdom has demonstrated to be an important source of new molecules with antiviral potential. Purpose: The present study aims to utilize various computational tools to identify the most eligible drug candidate that have capabilities to halt the replication of SARS-COV-2 virus by inhibiting Main protease (Mpro) enzyme. Methods: We have selected plants whose extracts have inhibitory potential against previously discovered coronaviruses. Their phytoconstituents were surveyed and a library of 100 molecules was prepared. Then, computational tools such as molecular docking, ADMET and molecular dynamic simulations were utilized to screen the compounds and evaluate them against Mpro enzyme. Results: All the phytoconstituents showed good binding affinities towards Mpro enzyme. Among them laurolitsine possesses the highest binding affinity i.e. -294.1533 kcal/mol. On ADMET analysis of best three ligands were simulated for 1.2 ns, then the stable ligand among them was further simulated for 20 ns. Results revealed that no conformational changes were observed in the laurolitsine w.r.t. protein residues and low RMSD value suggested that the Laurolitsine-protein complex was stable for 20 ns. Conclusion: Laurolitsine, an active constituent of roots of Lindera aggregata, was found to be having good ADMET profile and have capabilities to halt the activity of the enzyme. Therefore, this makes laurolitsine a good drug candidate for the treatment of COVID-19.

In search of RdRp and Mpro inhibitors against SARS CoV-2: Molecular docking, molecular dynamic simulations and ADMET analysis.

Corona Virus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome coronavirus (SARS CoV-2) has been declared a worldwide pandemic by WHO recently. The complete understanding of the complex genomic structure of SARS CoV-2 has enabled the use of computational tools in search of SARS CoV-2 inhibitors against the multiple proteins responsible for its entry and multiplication in human cells. With this endeavor, 177 natural, anti-viral chemical entities and their derivatives, selected through the critical analysis of the literatures, were studied using pharmacophore screening followed by molecular docking against RNA dependent RNA polymerase and main protease. The identified hits have been subjected to molecular dynamic simulations to study the stability of ligand-protein complexes followed by ADMET analysis and Lipinski filters to confirm their drug likeliness. It has led to an important start point in the drug discovery and development of therapeutic agents against SARS CoV-2.

Computational insights of phytochemical-driven disruption of RNA-dependent RNA polymerase-mediated replication of coronavirus: a strategic treatment plan against coronavirus disease 2019.

The current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has raised global health concerns. RNA-dependent RNA polymerase (RdRp) is the prime component of viral replication/proliferation machinery and is considered to be a potential drug target against SARS-CoV-2. The present study investigated the anti-RdRp activity of phytochemicals against SARS-CoV-2 infection. Virtual ligand screening was carried out to determine the potent compounds against RdRp. Molecular docking and an MD Simulation study were employed to evaluate the spatial affinity of selected phytochemicals for the active sites of RdRp. Structural stability of target compounds was determined using root mean square deviation computational analysis and drug-like abilities were investigated using ADMET. Bond distances between ligand and receptor were marked to predict the strength of interaction. Aloe, azadirachtin, columbin, cirsilineol, nimbiol, nimbocinol and sage exhibited the highest binding affinities and interacted with active sites of RdRp, surpassing the ability of chloroquine, lamivudine, favipiravir and remdesivir to target the same. All the natural metabolites exhibited stable conformation during MD Simulation of 101 ns at 310 K. Kinetic, potential and electrostatic energy were observed to be least in the case of natural metabolites in comparison with synthetic analogues. Deviations and fluctuations were observed to be structurally least in target phytochemicals. Physiochemical and biological properties of these compounds further validated their drug-like properties. Non-bonded distance was found to be short enough to form hydrogen bonding or hydrophobic interactions, which revealed that these target compounds can strongly bind with RdRp. The study found potential phytochemicals to disrupt the replication domain of SARS-CoV-2 by hindering RdRp. We therefore anticipate that the current findings could be considered as valuable for the development of an efficient preventive/therapeutic expedient against COVID-19.