Potential phytochemical inhibitors of SARS-CoV-2 helicase Nsp13: a molecular docking and dynamic simulation study

From Flora to Pharmaceuticals: 100 new additions to angiosperms of Gafargaon subdistrict in Bangladesh and unraveling antidiabetic drug candidates targeting DPP4 through in silico approach

Addition to the angiosperm flora provides essential insights into the biodiversity of a region, contributing to ecological understanding and conservation planning. Gafargaon subdistrict under Mymensingh district in Bangladesh represents a diverse population of angiosperms with a multifaceted ecosystem that demands re-evaluation of the existing angiosperm diversity of Gafargaon to update the status of angiosperm taxa and facilitate their conservation efforts. With this endeavor, a total of 100 angiosperm taxa belonging to 90 genera and 46 families were uncovered as additional occurrence in Gafargaon. The species in the area showcased a variety of life forms, including 63 herbs, 14 shrubs, 14 trees, and 9 climbers. Among the recorded taxa, Chamaecostus cuspidatus (Nees & Mart.) C.D. Specht & D.W. Stev. was selected for antidiabetic drug design endeavor based on citation frequency and ethnomedicinal evidence. A total of 41 phytochemicals of C. cuspidatus were screened virtually, targeting the Dipeptidyl peptidase 4 protein through structure-based drug design approach, which unveiled two lead compounds, such as Tigogenin (-9.0 kcal/mol) and Diosgenin (-8.5 kcal/mol). The lead candidates demonstrated favorable pharmacokinetic and pharmacodynamic properties with no major side effects. Molecular dynamics simulation revealed notable stability and structural compactness of the lead compounds. Principal component analysis and Gibbs free energy landscape further supported the results of molecular dynamics simulation. Molecular mechanics-based MM/GBSA approach unraveled higher free binding energies of Diosgenin (-47.36 kcal/mol) and Tigogenin (-46.70 kcal/mol) over Alogliptin (-46.32 kcal/mol). The outcome of the present investigation would enrich angiosperm flora of Gafargaon and shed light on the role of C. cuspidatus to develop novel antidiabetic therapeutics to combat diabetes.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

In silico identification of potential inhibitors of vital monkeypox virus proteins from FDA approved drugs

The World Health Organization (WHO) recently declared the monkeypox outbreak 'A public health emergency of international concern'. The monkeypox virus belongs to the same Orthopoxvirus genus as smallpox. Although smallpox drugs are recommended for use against monkeypox, monkeypox-specific drugs are not yet available. Drug repurposing is a viable and efficient approach in the face of such an outbreak. Therefore, we present a computational drug repurposing study to identify the existing approved drugs which can be potential inhibitors of vital monkeypox virus proteins, thymidylate kinase and D9 decapping enzyme. The target protein structures of the monkeypox virus were modelled using the corresponding protein structures in the vaccinia virus. We identified four potential inhibitors namely, Tipranavir, Cefiderocol, Doxorubicin, and Dolutegravir as candidates for repurposing against monkeypox virus from a library of US FDA approved antiviral and antibiotic drugs using molecular docking and molecular dynamics simulations. The main goal of this in silico study is to identify potential inhibitors against monkeypox virus proteins that can be further experimentally validated for the discovery of novel therapeutic agents against monkeypox disease.

Computational prediction of phytochemical inhibitors against the cap-binding domain of Rift Valley fever virus

Rift Valley fever is a zoonotic disease that can spread through livestock and mosquitoes, and its symptoms include retinitis, photophobia, hemorrhagic fever and neurological effects. The World Health Organization has identified Rift Valley fever as one of the viral infections that has potential to cause a future epidemic. Hence, efforts are urgently needed toward development of therapeutics and vaccine against this infectious disease. Notably, the causative virus namely, the Rift Valley fever virus (RVFV), utilizes the cap-snatching mechanism for viral transcription, rendering its cap-binding domain (CBD) as an effective antiviral target. To date, there are no published studies towards identification of potential small molecule inhibitors for the CBD of RVFV. Here, we employ a virtual screening workflow comprising of molecular docking and molecular dynamics (MD) simulation, to identify 5 potential phytochemical inhibitors of the CBD of RVFV. These 5 phytochemical inhibitors can be sourced from Indian medicinal plants, Ferula assa-foetida, Glycyrrhiza glabra and Leucas cephalotes, used in traditional medicine. In sum, the 5 phytochemical inhibitors of the CBD of RVFV identified by this purely computational study are promising drug lead molecules which can be considered for detailed experimental validation against RVFV infection.

IMPPAT 2.0: An Enhanced and Expanded Phytochemical Atlas of Indian Medicinal Plants

Compilation, curation, digitization, and exploration of the phytochemical space of Indian medicinal plants can expedite ongoing efforts toward natural product and traditional knowledge based drug discovery. To this end, we present IMPPAT 2.0, an enhanced and expanded database compiling manually curated information on 4010 Indian medicinal plants, 17,967 phytochemicals, and 1095 therapeutic uses. Notably, IMPPAT 2.0 compiles associations at the level of plant parts and provides a FAIR-compliant nonredundant in silico stereo-aware library of 17,967 phytochemicals from Indian medicinal plants. The phytochemical library has been annotated with several useful properties to enable easier exploration of the chemical space. We have also filtered a subset of 1335 drug-like phytochemicals of which majority have no similarity to existing approved drugs. Using cheminformatics, we have characterized the molecular complexity and molecular scaffold based structural diversity of the phytochemical space of Indian medicinal plants and performed a comparative analysis with other chemical libraries. Altogether, IMPPAT 2.0 is a manually curated extensive phytochemical atlas of Indian medicinal plants that is accessible at https://cb.imsc.res.in/imppat/.

Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding

The search for an effective drug is still urgent for COVID-19 as no drug with proven clinical efficacy is available. Finding the new purpose of an approved or investigational drug, known as drug repurposing, has become increasingly popular in recent years. We propose here a new drug repurposing approach for COVID-19, based on knowledge graph (KG) embeddings. Our approach learns "ensemble embeddings" of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. Ensemble KG-embeddings are subsequently used in a deep neural network trained for discovering potential drugs for COVID-19. Compared to related works, we retrieve more in-trial drugs among our top-ranked predictions, thus giving greater confidence in our prediction for out-of-trial drugs. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. We show that Fosinopril is a potential ligand for the SARS-CoV-2 nsp13 target. We also provide explanations of our predictions thanks to rules extracted from the KG and instanciated by KG-derived explanatory paths. Molecular evaluation and explanatory paths bring reliability to our results and constitute new complementary and reusable methods for assessing KG-based drug repurposing.

Therapeutic potential of compounds targeting SARS-CoV-2 helicase

The economical and societal impact of COVID-19 has made the development of vaccines and drugs to combat SARS-CoV-2 infection a priority. While the SARS-CoV-2 spike protein has been widely explored as a drug target, the SARS-CoV-2 helicase (nsp13) does not have any approved medication. The helicase shares 99.8% similarity with its SARS-CoV-1 homolog and was shown to be essential for viral replication. This review summarizes and builds on existing research on inhibitors of SARS-CoV-1 and SARS-CoV-2 helicases. Our analysis on the toxicity and specificity of these compounds, set the road going forward for the repurposing of existing drugs and the development of new SARS-CoV-2 helicase inhibitors.

Exploring the Targets of Novel Corona Virus and Docking-based Screening of Potential Natural Inhibitors to Combat COVID-19

There is a need to explore natural compounds against COVID-19 due to their multitargeted actions against various targets of nCoV. They act on multiple sites rather than single targets against several diseases. Thus, there is a possibility that natural resources can be repurposed to combat COVID-19. However, the biochemical mechanisms of these inhibitors were not known. To reveal the mode of anti-nCoV action, structure-based docking plays a major role. The present study is an attempt to explore various potential targets of SARS-CoV-2 and the structure-based screening of various potential natural inhibitors to combat the novel coronavirus.

Computational exploration of the dual role of the phytochemical fortunellin: Antiviral activities against SARS-CoV-2 and immunomodulatory abilities against the host

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections generate approximately one million virions per day, and the majority of available antivirals are ineffective against it due to the virus's inherent genetic mutability. This necessitates the investigation of concurrent inhibition of multiple SARS-CoV-2 targets. We show that fortunellin (acacetin 7-O-neohesperidoside), a phytochemical, is a promising candidate for preventing and treating coronavirus disease (COVID-19) by targeting multiple key viral target proteins. Fortunellin supports protective immunity while inhibiting pro-inflammatory cytokines and apoptosis pathways and protecting against tissue damage. Fortunellin is a phytochemical found in Gojihwadi kwath, an Indian traditional Ayurvedic formulation with an antiviral activity that is effective in COVID-19 patients. The mechanistic action of its antiviral activity, however, is unknown. The current study comprehensively evaluates the potential therapeutic mechanisms of fortunellin in preventing and treating COVID-19. We have used molecular docking, molecular dynamics simulations, free-energy calculations, host target mining of fortunellin, gene ontology enrichment, pathway analyses, and protein-protein interaction analysis. We discovered that fortunellin reliably binds to key targets that are necessary for viral replication, growth, invasion, and infectivity including Nucleocapsid (N-CTD) (-54.62 kcal/mol), Replicase-monomer at NSP-8 binding site (-34.48 kcal/mol), Replicase-dimer interface (-31.29 kcal/mol), Helicase (-30.02 kcal/mol), Papain-like-protease (-28.12 kcal/mol), 2'-O-methyltransferase (-23.17 kcal/mol), Main-protease (-21.63 kcal/mol), Replicase-monomer at dimer interface (-22.04 kcal/mol), RNA-dependent-RNA-polymerase (-19.98 kcal/mol), Nucleocapsid-NTD (-16.92 kcal/mol), and Endoribonuclease (-16.81 kcal/mol). Furthermore, we identify and evaluate the potential human targets of fortunellin and its effect on the SARS-CoV-2 infected tissues, including normal-human-bronchial-epithelium (NHBE) and lung cells and organoids such as pancreatic, colon, liver, and cornea using a network pharmacology approach. Thus, our findings indicate that fortunellin has a dual role; multi-target antiviral activities against SARS-CoV-2 and immunomodulatory capabilities against the host.

Molecular dynamics studies reveal structural and functional features of the SARS-CoV-2 spike protein

The SARS-CoV-2 virus is responsible for the COVID-19 pandemic the world experience since 2019. The protein responsible for the first steps of cell invasion, the spike protein, has probably received the most attention in light of its central role during infection. Computational approaches are among the tools employed by the scientific community in the enormous effort to study this new affliction. One of these methods, namely molecular dynamics (MD), has been used to characterize the function of the spike protein at the atomic level and unveil its structural features from a dynamic perspective. In this review, we focus on these main findings, including spike protein flexibility, rare S protein conformational changes, cryptic epitopes, the role of glycans, drug repurposing, and the effect of spike protein variants.

Z-Guggulsterone Is a Potential Lead Molecule of Dawa-ul-Kurkum against Hepatocellular Carcinoma

An ancient saffron-based polyherbal formulation, Dawa-ul-Kurkum (DuK), has been used to treat liver ailments and other diseases and was recently evaluated for its anticancer potential against hepatocellular carcinoma (HCC) by our research team. To gain further insight into the lead molecule of DuK, we selected ten active constituents belonging to its seven herbal constituents (crocin, crocetin, safranal, jatamansone, isovaleric acid, cinnamaldehyde, coumaric acid, citral, guggulsterone and dehydrocostus lactone). We docked them with 32 prominent proteins that play important roles in the development, progression and suppression of HCC and those involved in endoplasmic reticulum (ER) stress to identify the binding interactions between them. Three reference drugs for HCC (sorafenib, regorafenib, and nivolumab) were also examined for comparison. The in silico studies revealed that, out of the ten compounds, three of them—viz., Z-guggulsterone, dehydrocostus lactone and crocin—showed good binding efficiency with the HCC and ER stress proteins. Comparison of binding affinity with standard drugs was followed by preliminary in vitro screening of these selected compounds in human liver cancer cell lines. The results provided the basis for selecting Z-guggulsterone as the best-acting phytoconstituent amongst the 10 studied. Further validation of the binding efficiency of Z-guggulsterone was undertaking using molecular dynamics (MD) simulation studies. The effects of Z-guggulsterone on clone formation and cell cycle progression were also assessed. The anti-oxidant potential of Z-guggulsterone was analyzed through DPPH and FRAP assays. qRTPCR was utilized to check the results at the in vitro level. These results indicate that Z-guggulsterone should be considered as the main constituent of DuK instead of the crocin in saffron, as previously hypothesized.

1,2,3,4,6-Pentagalloyl glucose of Pistacia lentiscus can inhibit the replication and transcription processes and viral pathogenesis of SARS-COV-2

SARS-COV-2 stands as the source of the most catastrophic pandemic of this century, known as COVID-19. In this regard, we explored the effects of five Pistacia sp. active ingredients on the most crucial targets of SARS-COV-2, including 3CLpro, PLpro, RdRp, helicase, NSP15, and E protein. The results of molecular docking determined 1,2,3,4,6-pentagalloyl glucose (PG) as the most effective compound of Pistacia sp, which also confirmed its excellent binding affinities and stable interactions with helicase (−10.76 kcal/mol), RdRp (−10.19 kcal/mol), E protein (−9.51 kcal/mol), and 3CLpro (−9.47 kcal/mol). Furthermore, MD simulation was conducted to investigate the stability of all complexes throughout a 100 ns. In contrast to PLpro and NSP15, the analyses of Lennard-Jones potential, RMSDas, PCA, and SASA verified the ability of PG in forming stable and adequate interactions with RdRp, helicase, 3CLpro, and E protein due to standing as an effective inhibitor among the six targets, these data proposed the capability of PG, the most important compound of Pistacia sp., in inducing antiviral, anti-inflammatory, and antioxidant impacts on RdRp, helicase, 3CLpro, and E protein. Therefore, the possibility of inhibiting the replication and transcription processes and viral pathogenesis of SARS-COV-2 may be facilitated through the application of PG.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Potential COVID-19 Therapies from Computational Repurposing of Drugs and Natural Products against the SARS-CoV-2 Helicase

Repurposing of existing drugs is a rapid way to find potential new treatments for SARS-CoV-2. Here, we applied a virtual screening approach using Autodock Vina and molecular dynamic simulation in tandem to screen and calculate binding energies of repurposed drugs against the SARS-CoV-2 helicase protein (non-structural protein nsp13). Amongst the top hits from our study were antivirals, antihistamines, and antipsychotics, plus a range of other drugs. Approximately 30% of our top 87 hits had published evidence indicating in vivo or in vitro SARS-CoV-2 activity. Top hits not previously reported to have SARS-CoV-2 activity included the antiviral agents, cabotegravir and RSV-604; the NK1 antagonist, aprepitant; the trypanocidal drug, aminoquinuride; the analgesic, antrafenine; the anticancer intercalator, epirubicin; the antihistamine, fexofenadine; and the anticoagulant, dicoumarol. These hits from our in silico SARS-CoV-2 helicase screen warrant further testing as potential COVID-19 treatments.

Isolation and Characterization of Werneria Chromene and Dihydroxyacidissimol from Burkillanthus malaccensis (Ridl.) Swingle

The secondary metabolites of endemic plants from the Rutaceae family, such as Burkillanthusmalaccensis (Ridl.) Swingle from the rainforest of Malaysia, has not been studied. Burkillanthusmalaccensis (Ridl.) Swingle may produce antibacterial and antibiotic-potentiating secondary metabolites. Hexane, chloroform, and methanol extracts of leaves, bark, wood, pericarps, and endocarps were tested against bacteria by broth microdilution assay and their antibiotic-potentiating activities. Chromatographic separations of hexane extracts of seeds were conducted to investigate effective phytochemicals and their antibacterial activities. Molecular docking studies of werneria chromene and dihydroxyacidissiminol against SARS-CoV-2 virus infection were conducted using AutoDock Vina. The methanol extract of bark inhibited the growth of Staphylococcusaureus, Escherichiacoli, and Pseudomonasaeruginosa with the minimum inhibitory concentration of 250, 500, and 250 µg/mL, respectively. The chloroform extract of endocarps potentiated the activity of imipenem against imipenem-resistant Acinetobacterbaumannii. The hexane extract of seeds increased the sensitivity of P. aeruginosa against ciprofloxacin and levofloxacin. The hexane extract of seeds and chloroform extract of endocarps were chromatographed, yielding werneria chromene and dihydroxyacidissiminol. Werneria chromene was bacteriostatic for P.aeruginosa and P.putida, with MIC/MBC values of 1000 > 1000 µg/mL. Dihydroxyacidissiminol showed the predicted binding energies of −8.1, −7.6, −7.0, and −7.5 kcal/mol with cathepsin L, nsp13 helicase, SARS-CoV-2 main protease, and SARS-CoV-2 spike protein receptor-binding domain S-RBD. Burkillanthusmalaccensis (Ridl.) Swingle can be a potential source of natural products with antibiotic-potentiating activity and that are anti-SARS-CoV-2.

Virtual screening of phytochemicals from Indian medicinal plants against the endonuclease domain of SFTS virus L polymerase

Severe fever with thrombocytopenia syndrome virus (SFTSV) causes a highly infectious disease with reported mortality in the range 2.8% to 47%. The replication and transcription of the SFTSV genome is performed by L polymerase, which has both an RNA dependent RNA polymerase domain and an N-terminal endonuclease (endoN) domain. Due to its crucial role in the cap-snatching mechanism required for initiation of viral RNA transcription, the endoN domain is an ideal antiviral drug target. In this virtual screening study for the identification of potential inhibitors of the endoN domain of SFTSV L polymerase, we have used molecular docking and molecular dynamics (MD) simulation to explore the natural product space of 14 011 phytochemicals from Indian medicinal plants. After generating a heterogeneous ensemble of endoN domain structures reflecting conformational diversity of the corresponding active site using MD simulations, ensemble docking of the phytochemicals was performed against the endoN domain structures. Apart from the ligand binding energy from docking, our virtual screening workflow imposes additional filters such as drug-likeness, non-covalent interactions with key active site residues, toxicity and chemical similarity with other hits, to identify top 5 potential phytochemical inhibitors of endoN domain of SFTSV L polymerase. Further, the stability of the protein–ligand docked complexes for the top 5 potential inhibitors was analyzed using MD simulations. The potential phytochemical inhibitors, predicted in this study using contemporary computational methods, are expected to serve as lead molecules in future experimental studies towards development of antiviral drugs against SFTSV.

Virtual screening of a large phytochemical library from Indian medicinal plants to identify potential endonuclease inhibitors against emerging virus SFTSV.

Phospholipids dock SARS-CoV-2 spike protein via hydrophobic interactions: a minimal in-silico study of lecithin nasal spray therapy

Understanding the physical and chemical properties of viral infections at molecular scales is a major challenge for the scientific community more so with the outbreak of global pandemics. There is currently a lot of effort being placed in identifying molecules that could act as putative drugs or blockers of viral molecules. In this work, we computationally explore the importance in antiviral activity of a less studied class of molecules, namely surfactants. We employ all-atoms molecular dynamics simulations to study the interaction between the receptor-binding domain of the SARS-CoV-2 spike protein and the phospholipid lecithin (POPC), in water. Our microsecond simulations show a preferential binding of lecithin to the receptor-binding motif of SARS-CoV-2 with binding free energies significantly larger than [Formula: see text]. Furthermore, hydrophobic interactions involving lecithin non-polar tails dominate these binding events, which are also accompanied by dewetting of the receptor binding motif. Through an analysis of fluctuations in the radius of gyration of the receptor-binding domain, its contact maps with lecithin molecules, and distributions of water molecules near the binding region, we elucidate molecular interactions that may play an important role in interactions involving surfactant-type molecules and viruses. We discuss our minimal computational model in the context of lecithin-based liposomal nasal sprays as putative mitigating therapies for COVID-19.

Current status of structure-based drug repurposing against COVID-19 by targeting SARS-CoV-2 proteins

More than one and half years have passed, as of August 2021, since the COVID-19 caused by the novel coronavirus named SARS-CoV-2 emerged in 2019. While the recent success of vaccine developments likely reduces the severe cases, there is still a strong requirement of safety and effective therapeutic drugs for overcoming the unprecedented situation. Here we review the recent progress and the status of the drug discovery against COVID-19 with emphasizing a structure-based perspective. Structural data regarding the SARS-CoV-2 proteome has been rapidly accumulated in the Protein Data Bank, and up to 68% of the total amino acid residues encoded in the genome were covered by the structural data. Despite a global effort of in silico and in vitro screenings for drug repurposing, there is only a limited number of drugs had been successfully authorized by drug regulation organizations. Although many approved drugs and natural compounds, which exhibited antiviral activity in vitro, were considered potential drugs against COVID-19, a further multidisciplinary investigation is required for understanding the mechanisms underlying the antiviral effects of the drugs.

Effectiveness of Natural Antioxidants against SARS-CoV-2? Insights from the In-Silico World

The SARS CoV-2 pandemic has affected millions of people around the globe. Despite many efforts to find some effective medicines against SARS CoV-2, no established therapeutics are available yet. The use of phytochemicals as antiviral agents provides hope against the proliferation of SARS-CoV-2. Several natural compounds were analyzed by virtual screening against six SARS CoV-2 protein targets using molecular docking simulations in the present study. More than a hundred plant-derived secondary metabolites have been docked, including alkaloids, flavonoids, coumarins, and steroids. SARS CoV-2 protein targets include Main protease (MPro), Papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp), Spike glycoprotein (S), Helicase (Nsp13), and E-Channel protein. Phytochemicals were evaluated by molecular docking, and MD simulations were performed using the YASARA structure using a modified genetic algorithm and AMBER03 force field. Binding energies and dissociation constants allowed the identification of potentially active compounds. Ligand-protein interactions provide an insight into the mechanism and potential of identified compounds. Glycyrrhizin and its metabolite 18-β-glycyrrhetinic acid have shown a strong binding affinity for MPro, helicase, RdRp, spike, and E-channel proteins, while a flavonoid Baicalin also strongly binds against PLpro and RdRp. The use of identified phytochemicals may help to speed up the drug development and provide natural protection against SARS-CoV-2.