Structure-based virtual screening and molecular dynamics simulation of SARS-CoV-2 Guanine-N7 methyltransferase (nsp14) for identifying antiviral inhibitors against COVID-19

In silico evaluation of S-adenosyl-L-homocysteine analogs as inhibitors of nsp14-viral cap N7 methyltranferase and PLpro of SARS-CoV-2: synthesis, molecular docking, physicochemical data, ADMET and molecular dynamics simulations studies

A series of S-adenosyl-L-homosysteine (SAH) analogs, with modification in the base and sugar moiety, have been designed, synthesized and screened as nsp14 and PLpro inhibitors of severe acute respiratory syndrome corona virus (SARS-CoV-2). The outcomes of ADMET (Adsorption, Distribution, Metabolism, Excretion, and Toxicity) studies demonstrated that the physicochemical properties of all analogs were permissible for development of these SAH analogs as antiviral agents. All molecules were screened against different SARS-CoV-2 targets using molecular docking. The docking results revealed that the SAH analogs interacted well in the active site of nsp14 protein having H-bond interactions with the amino acid residues Arg289, Val290, Asn388, Arg400, Phe401 and π-alkyl interactions with Arg289, Val290 and Phe426 of Nsp14-MTase site. These analogs also formed stable H-bonds with Leu163, Asp165, Arg167, Ser246, Gln270, Tyr274 and Asp303 residues of PLpro proteins and found to be quite stable complexes therefore behaved as probable nsp14 and PLpro inhibitors. Interestingly, analog 3 showed significant in silico activity against the nsp14 N7 methyltransferase of SARS-CoV-2. The molecular dynamics (MD) and post-MD results of analog 3 unambiguously established the higher stability of the nsp14 (N7 MTase):3 complex and also indicated its behavior as probable nsp14 inhibitor like the reference sinefungin. The docking and MD simulations studies also suggested that sinefungin did act as SARS-CoV-2 PLpro inhibitor as well. This study's findings not only underscore the efficacy of the designed SAH analogs as potent inhibitors against crucial SARS-CoV-2 proteins but also pinpoint analog 3 as a particularly promising candidate. All the study provides valuable insights, paving the way for potential advancements in antiviral drug development against SARS-CoV-2.

Investigation of bioremediation for glyphosate and its metabolite in soil using arbuscular mycorrhizal GmHsp60 protein: a molecular docking and molecular dynamics simulations approach

The widespread use of glyphosate and the high dependence of the agricultural industry on this herbicide cause environmental pollution and pose a threat to living organisms. One of the appropriate solutions in sustainable agriculture to deal with pollution caused by glyphosate and its metabolites is creating a symbiotic relationship between plants and mycorrhizal fungi. Glomalin-related soil protein is a key protein for the bioremediation of glyphosate and its metabolite aminomethyl phosphonic acid in soil. This study uses homology modeling, molecular docking, and molecular dynamic simulation approaches to investigate the binding mechanism of glomalin-related soil protein from arbuscular mycorrhiza (GmHsp60) with glyphosate and its metabolite and the role of soil protein in the removal and sequestering of common agricultural soil pollutants. GmHsp60 protein structure was predicted by homology modeling, and the quality of the generated model was assessed. Then, the interaction between glyphosate and aminomethyl phosphonic acid and the modeled GmHsp60 protein was explored by molecular docking. Based on docking results, GmHsp60 has an efficient role in the bioremediation of glyphosate and aminomethyl phosphonic acid (-6.03 and -5.34 kcal/mol). Glyphosate forms three hydrogen bonds with Lys258, Gly262, and Glu58 of GmHsp60, and aminomethyl phosphonic acid forms three hydrogen bonds with Lys258, Gly261, and Gly262 of GmHsp60. In addition, the glyphosate's and its metabolite's stability was confirmed by molecular docking simulations and binding free energy calculations using MM/PBSA analysis. This study provides a molecular-level understanding of GmHsp60 expression and function for glyphosate bioremediation.

Identification of adenosine analogues as nsp14 N7‑methyltransferase inhibitors for treating coronaviruses infection

Coronaviruses are RNA viruses that have coevolved with humans and animals over time, exhibiting high mutation rates and mortality rates upon epidemic outbreaks. The nonstructural protein (nsp14) is crucial for various coronaviruses processes, including genome replication, protein translation, virus particle assembly, and evasion of host immunity via RNA methylation modification. In this study, a series of adenosine analogs were designed, synthesized, and evaluated for their inhibitory activities. Among them, MTI013 exhibited the strongest nsp14 MTase inhibition and antiviral activity, with an IC50 of 10.33 μM in HCoV-229E-infected Huh7 cells, along with low cytotoxicity. When combined with the RdRp inhibitor ATV014, MTI013 showed a synergistic antiviral effect, indicating its potential both as a standalone therapy and in combination treatments. Furthermore, MTI013 displayed high selectivity against the SARS-CoV-2 nsp10-nsp16 complex and five human methyltransferases. These results offer valuable structural insights for future exploration of nsp14 as a drug target for SARS-CoV-2 and other coronaviruses.

Toxicological study on ibuprofen and selenium in freshwater mussel Lamellidens marginalis and exploring the microbial cytochrome through modelling and quantum mechanics approaches for its toxicity degradation in contaminated environment

Toxicological stress in aquatic organisms is caused by the discharge of hundreds of toxic pollutants and contaminants among which the current study concentrates on the toxic effect of non-steroidal anti-inflammatory drug ibuprofen (IBF) and the trace element selenium (Se). In this study, IBF and Se toxicity on freshwater mussel Lamellidens marginalis was studied for 14 days, and in silico predictions for their degradation were made using Molecular modelling and Quantum Mechanical approaches. The degrading propensity of cytochrome c oxidase proteins from Trametes verticillatus and Thauera selenatis (Turkey tail fungi and Gram-negative bacteria) is examined into atom level. The results of molecular modelling study indicate that ionic interactions occur in the T. selenatis-HEME bound complex by Se interacting directly with HEME, and in the T. versicolor-HEME bound complex by IBF bound to a nearby region of HEME. Experimental and theoretical findings suggest that, the toxicological effects of Se and IBF pollution can be reduced by bioremediation with special emphasis on T. versicolor, and T. selenatis, which can effectively interact with Se and IBF present in the environment and degrade them. Besides, this is the first time in freshwater mussel L. marginalis that ibuprofen and selenium toxicity have been studied utilizing both experimental and computational methodologies for their bioremediation study.

In silico and in vitro analyses of a novel FoxO1 agonist reducing Aβ levels via downregulation of BACE1

AIMS

FoxO1 is an important target in the treatment of Alzheimer's disease (AD). However, FoxO1-specific agonists and their effects on AD have not yet been reported. This study aimed to identify small molecules that upregulate the activity of FoxO1 to attenuate the symptoms of AD.

METHODS

FoxO1 agonists were identified by in silico screening and molecular dynamics simulation. Western blotting and reverse transcription-quantitative polymerase chain reaction assays were used to assess protein and gene expression levels of P21, BIM, and PPARγ downstream of FoxO1 in SH-SY5Y cells, respectively. Western blotting and enzyme-linked immunoassays were performed to explore the effect of FoxO1 agonists on APP metabolism.

RESULTS

N-(3-methylisothiazol-5-yl)-2-(2-oxobenzo[d]oxazol-3(2H)-yl) acetamide (compound D) had the highest affinity for FoxO1. Compound D activated FoxO1 and regulated the expression of its downstream target genes, P21, BIM, and PPARγ. In SH-SY5Y cells treated with compound D, BACE1 expression levels were downregulated, and the levels of Aβ1-40 and Aβ1-42 were also reduced.

CONCLUSIONS

We present a novel small-molecule FoxO1 agonist with good anti-AD effects. This study highlights a promising strategy for new drug discovery for AD.

A universal fluorescence polarization high throughput screening assay to target the SAM-binding sites of SARS-CoV-2 and other viral methyltransferases

SARS-CoV-2 has caused a global pandemic with significant humanity and economic loss since 2020. Currently, only limited options are available to treat SARS-CoV-2 infections for vulnerable populations. In this study, we report a universal fluorescence polarization (FP)-based high throughput screening (HTS) assay for SAM-dependent viral methyltransferases (MTases), using a fluorescent SAM-analogue, FL-NAH. We performed the assay against a reference MTase, NSP14, an essential enzyme for SARS-CoV-2 to methylate the N7 position of viral 5'-RNA guanine cap. The assay is universal and suitable for any SAM-dependent viral MTases such as the SARS-CoV-2 NSP16/NSP10 MTase complex and the NS5 MTase of Zika virus (ZIKV). Pilot screening demonstrated that the HTS assay was very robust and identified two candidate inhibitors, NSC 111552 and 288387. The two compounds inhibited the FL-NAH binding to the NSP14 MTase with low micromolar IC50. We used three functional MTase assays to unambiguously verified the inhibitory potency of these molecules for the NSP14 N7-MTase function. Binding studies indicated that these molecules are bound directly to the NSP14 MTase with similar low micromolar affinity. Moreover, we further demonstrated that these molecules significantly inhibited the SARS-CoV-2 replication in cell-based assays at concentrations not causing cytotoxicity. Furthermore, NSC111552 significantly synergized with known SARS-CoV-2 drugs including nirmatrelvir and remdesivir. Finally, docking suggested that these molecules bind specifically to the SAM-binding site on the NSP14 MTase. Overall, these molecules represent novel and promising candidates to further develop broad-spectrum inhibitors for the management of viral infections.

What do we know about the function of SARS-CoV-2 proteins?

The COVID-19 pandemic has highlighted the importance in the understanding of the biology of SARS-CoV-2. After more than two years since the first report of COVID-19, it remains crucial to continue studying how SARS-CoV-2 proteins interact with the host metabolism to cause COVID-19. In this review, we summarize the findings regarding the functions of the 16 non-structural, 6 accessory and 4 structural SARS-CoV-2 proteins. We place less emphasis on the spike protein, which has been the subject of several recent reviews. Furthermore, comprehensive reviews about COVID-19 therapeutic have been also published. Therefore, we do not delve into details on these topics; instead we direct the readers to those other reviews. To avoid confusions with what we know about proteins from other coronaviruses, we exclusively report findings that have been experimentally confirmed in SARS-CoV-2. We have identified host mechanisms that appear to be the primary targets of SARS-CoV-2 proteins, including gene expression and immune response pathways such as ribosome translation, JAK/STAT, RIG-1/MDA5 and NF-kβ pathways. Additionally, we emphasize the multiple functions exhibited by SARS-CoV-2 proteins, along with the limited information available for some of these proteins. Our aim with this review is to assist researchers and contribute to the ongoing comprehension of SARS-CoV-2's pathogenesis.

Identifying inhibitors of NSP16-NSP10 of SARS-CoV-2 from large databases

The COVID-19 pandemic, which has already claimed millions of lives, continues to pose a serious threat to human health, requiring the development of new effective drugs. Non-structural proteins of SARS-CoV-2 play an important role in viral replication and infection. Among them, NSP16 (non-structured protein 16) and its cofactor NSP10 (non-structured protein 10) perform C2'-O methylation at the 5' end of the viral RNA, which promotes efficient virus replication. Therefore, the NSP16-NSP10 complex becomes an attractive target for drug development. Using a multi-step virtual screening protocol which includes Lipinski's rule, docking, steered molecular dynamics and umbrella sampling, we searched for potential inhibitors from the PubChem and anti-HIV databases. It has been shown that CID 135566620 compound from PubChem is the best candidate with an inhibition constant in the sub-μM range. The Van der Waals interaction was found to be more important than the electrostatic interaction in the binding affinity of this compound to NSP16-NSP10. Further in vitro and in vivo studies are needed to test the activity of the identified compound against COVID-19.Communicated by Ramaswamy H. Sarma.

Structural and non-structural proteins in SARS-CoV-2: potential aspects to COVID-19 treatment or prevention of progression of related diseases

Coronavirus disease 2019 (COVID-19) is caused by a new member of the Coronaviridae family known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There are structural and non-structural proteins (NSPs) in the genome of this virus. S, M, H, and E proteins are structural proteins, and NSPs include accessory and replicase proteins. The structural and NSP components of SARS-CoV-2 play an important role in its infectivity, and some of them may be important in the pathogenesis of chronic diseases, including cancer, coagulation disorders, neurodegenerative disorders, and cardiovascular diseases. The SARS-CoV-2 proteins interact with targets such as angiotensin-converting enzyme 2 (ACE2) receptor. In addition, SARS-CoV-2 can stimulate pathological intracellular signaling pathways by triggering transcription factor hypoxia-inducible factor-1 (HIF-1), neuropilin-1 (NRP-1), CD147, and Eph receptors, which play important roles in the progression of neurodegenerative diseases like Alzheimer's disease, epilepsy, and multiple sclerosis, and multiple cancers such as glioblastoma, lung malignancies, and leukemias. Several compounds such as polyphenols, doxazosin, baricitinib, and ruxolitinib could inhibit these interactions. It has been demonstrated that the SARS-CoV-2 spike protein has a stronger affinity for human ACE2 than the spike protein of SARS-CoV, leading the current study to hypothesize that the newly produced variant Omicron receptor-binding domain (RBD) binds to human ACE2 more strongly than the primary strain. SARS and Middle East respiratory syndrome (MERS) viruses against structural and NSPs have become resistant to previous vaccines. Therefore, the review of recent studies and the performance of current vaccines and their effects on COVID-19 and related diseases has become a vital need to deal with the current conditions. This review examines the potential role of these SARS-CoV-2 proteins in the initiation of chronic diseases, and it is anticipated that these proteins could serve as components of an effective vaccine or treatment for COVID-19 and related diseases. Video Abstract.

Interactome-Based Machine Learning Predicts Potential Therapeutics for COVID-19

COVID-19, the disease caused by SARS-CoV-2, has been disrupting our lives for more than two years now. SARS-CoV-2 interacts with human proteins to pave its way into the human body, thereby wreaking havoc. Moreover, the mutating variants of the virus that take place in the SARS-CoV-2 genome are also a cause of concern among the masses. Thus, it is very important to understand human-spike protein-protein interactions (PPIs) in order to predict new PPIs and consequently propose drugs for the human proteins in order to fight the virus and its different mutated variants, with the mutations occurring in the spike protein. This fact motivated us to develop a complete pipeline where PPIs and drug-protein interactions can be predicted for human-SARS-CoV-2 interactions. In this regard, initially interacting data sets are collected from the literature, and noninteracting data sets are subsequently created for human-SARS-CoV-2 by considering only spike glycoprotein. On the other hand, for drug-protein interactions both interacting and noninteracting data sets are considered from DrugBank and ChEMBL databases. Thereafter, a model based on a sequence-based feature is used to code the protein sequences of human and spike proteins using the well-known Moran autocorrelation technique, while the drugs are coded using another well-known technique, viz., PaDEL descriptors, to predict new human-spike PPIs and eventually new drug-protein interactions for the top 20 predicted human proteins interacting with the original spike protein and its different mutated variants like Alpha, Beta, Delta, Gamma, and Omicron. Such predictions are carried out by random forest as it is found to perform better than other predictors, providing an accuracy of 90.53% for human-spike PPI and 96.15% for drug-protein interactions. Finally, 40 unique drugs like eicosapentaenoic acid, doxercalciferol, ciclesonide, dexamethasone, methylprednisolone, etc. are identified that target 32 human proteins like ACACA, DST, DYNC1H1, etc.

Repurposing of phyto-ligand molecules from the honey bee products for Alzheimer's disease as novel inhibitors of BACE-1: small molecule bioinformatics strategies as amyloid-based therapy

Alzheimer's disease (AD) is one of the neurodegenerative diseases, manifesting dementia, spatial disorientation, language, cognitive, and functional impairment, mainly affects the elderly population with a growing concern about the financial burden on society. Repurposing can improve the traditional progress of drug design applications and could speed up the identification of innovative remedies for AD. The pursuit of potent anti-BACE-1 drugs for AD treatment has become a pot boiler topic in the recent past and to instigate the design of novel improved inhibitors from the bee products. Drug-likeness characteristics (ADMET: absorption, distribution, metabolism, excretion, and toxicity), docking (AutoDock Vina), simulation (GROMACS), and free energy interaction (MM-PBSA, molecular mechanics Poisson-Boltzmann surface area) analyses were performed to identify the lead candidates from the bee products (500 bioactives from the honey, royal jelly, propolis, bee bread, bee wax, and bee venom) for Alzheimer's disease as novel inhibitors of BACE-1 (beta-site amyloid precursor protein cleaving enzyme (1) receptor using appropriate bioinformatics tools. Forty-four bioactive lead compounds were screened from the bee products through high throughput virtual screening on the basis of their pharmacokinetic and pharmacodynamics characteristics, showing favorable intestinal and oral absorption, bioavailability, blood brain barrier penetration, less skin permeability, and no inhibition of cytochrome P450 inhibitors. The docking score of the forty-four ligand molecules was found to be between -4 and -10.3 kcal/mol, respectively, exhibiting strong binding affinity to BACE1 receptor. The highest binding affinity was observed in the rutin (-10.3 kcal/mol), 3,4-dicaffeoylquinic acid (-9.5 kcal/mol), nemorosone (-9.5 kcal/mol), and luteolin (-8.9 kcal/mol). Furthermore, these compounds demonstrated high total binding energy -73.20 to -105.85 kJ/mol), and low root mean square deviation (0.194-0.202 nm), root mean square fluctuation (0.0985-0.1136 nm), radius of gyration (2.12 nm), number of H-bonds (0.778-5.436), and eigenvector values (2.39-3.54 nm²) in the molecular dynamic simulation, signifying restricted motion of Cα atoms, proper folding and flexibility, and highly stable with compact of the BACE1 receptor with the ligands. Docking and simulation studies concluded that rutin, 3,4-dicaffeoylquinic acid, nemorosone, and luteolin are plausibly used as novel inhibitors of BACE1 to combat AD, but further in-depth experimental investigations are warranted to prove these in silico findings.

A combination of metformin and epigallocatechin gallate potentiates glioma chemotherapy in vivo

Glioma is the most devastating high-grade tumor of the central nervous system, with dismal prognosis. Existing treatment modality does not provide substantial benefit to patients and demands novel strategies. One of the first-line treatments for glioma, temozolomide, provides marginal benefit to glioma patients. Repurposing of existing non-cancer drugs to treat oncology patients is gaining momentum in recent years. In this study, we investigated the therapeutic benefits of combining three repurposed drugs, namely, metformin (anti-diabetic) and epigallocatechin gallate (green tea-derived antioxidant) together with temozolomide in a glioma-induced xenograft rat model. Our triple-drug combination therapy significantly inhibited tumor growth in vivo and increased the survival rate (50%) of rats when compared with individual or dual treatments. Molecular and cellular analyses revealed that our triple-drug cocktail treatment inhibited glioma tumor growth in rat model through ROS-mediated inactivation of PI3K/AKT/mTOR pathway, arrest of the cell cycle at G1 phase and induction of molecular mechanisms of caspases-dependent apoptosis.In addition, the docking analysis and quantum mechanics studies performed here hypothesize that the effect of triple-drug combination could have been attributed by their difference in molecular interactions, that maybe due to varying electrostatic potential. Thus, repurposing metformin and epigallocatechin gallate and concurrent administration with temozolomide would serve as a prospective therapy in glioma patients.

Multi-target molecular dynamic simulations reveal glutathione-S-transferase as the most favorable drug target of knipholone in Plasmodium falciparum

Knipholone is an antiplasmodial phytocompound obtained from the roots of Kniphofia foliosa. Despite several available studies, the molecular drug targets of knipholone in P. falciparum remained unknown. Nowadays, in silico techniques are widely used to study the molecular interactions between compounds and proteins as they provide results quickly with high precision and accuracy. In this study, we aim to identify the potential molecular drug targets of knipholone in P. falciparum. We selected 10 proteins of P. falciparum with unique metabolic functions and we found that knipholone showed better binding affinity than the native ligands of 6 proteins. Out of the 6 proteins, knipholone showed better enzyme inhibitory potential than the native ligands of 4 proteins. We carried out a 100 ns MD simulations for knipholone and the native ligands of four proteins and this was followed by binding free energy calculations. In each step, the performance of knipholone was compared to the native ligands of the proteins. Knipholone outperformed the native ligand of Glutathione-S-Transferase (1OKT) at crucial computational studies as evidence from the lower protein-ligand root mean square deviation value, protein root mean square fluctuation value, and protein-ligand binding free energies. The ligand properties of knipholone provide additional evidence for its stability and it maintains adequate protein-ligand contacts during the entire simulation. The density functional theory study also supported the stability of knipholone at the active binding site of 1OKT. From the studied proteins, we conclude that Glutathione-S-Transferase is the most favorable drug target for knipholone in P. falciparum.Communicated by Ramaswamy H. Sarma.

Examples of Structural Motifs in Viral Genomes and Approaches for RNA Structure Characterization

The relationship between conserved structural motifs and their biological function in the virus replication cycle is the interest of many researchers around the world. RNA structure is closely related to RNA function. Therefore, technological progress in high-throughput approaches for RNA structure analysis and the development of new ones are very important. In this mini review, we discuss a few perspectives on the structural elements of viral genomes and some methods used for RNA structure prediction and characterization. Based on the recent literature, we describe several examples of studies concerning the viral genomes, especially severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza A virus (IAV). Herein, we emphasize that a better understanding of viral genome architecture allows for the discovery of the structure-function relationship, and as a result, the discovery of new potential antiviral therapeutics.

High throughput bioluminescent assay to characterize and monitor the activity of SARS-CoV-2 methyltransferases

The fast rate of viral mutations of SARS CoV-2 result in decrease in the efficacy of the vaccines that have been developed before the emergence of these mutations. Thus, it is believed that using additional measures to combat the virus is not only advisable but also beneficial. Two antiviral drugs were authorized for emergency use by the FDA, namely Pfizer's two-drug regimen sold under the brand name Paxlovid, and Merck's drug Lagevrio. Pfizer's two-drug combination consists of nirmatrelvir, a protease inhibitor that blocks coronavirus ability to multiply and another antiviral, ritonavir, that lowers the rate of drug clearance to boost the longevity and activity of the protease inhibitor. Merck's drug Lagevrio (molnupiravir) is a nucleoside analogue with a mechanism of action that aims to introduce errors into the genetic code of the virus. We believe the armament against the virus can be augmented by the addition of another class of enzyme inhibitors that are required for viral survival and its ability to replicate. Enzymes like nsp14 and nsp10/16 methyltransferases (MTases) represent another class of drug targets since they are required for viral RNA translation and evading the host immune system. In this communication, we have successfully verified that the MTase-Glo, which is universal and homogeneous MTase assay can be used to screen for inhibitors of the two pivotal enzymes nsp14 and nsp16 of SARS CoV-2. Furthermore, we have carried out extensive studies on those enzymes using different RNA substrates and tested their activity using various inhibitors and verified the utility of this assay for use in drug screening programs. We anticipate our work will be pursued further to screen for large libraries to discover new and selective inhibitors for the viral enzymes particularly that these enzymes are structurally different from their mammalian counterparts.

Repurposing of the herbal formulations: molecular docking and molecular dynamics simulation studies to validate the efficacy of phytocompounds against SARS-CoV-2 proteins

Herbal formulations mentioned in traditional medicinal texts were investigated for in silico effect against SARS-COV-2 proteins involved in various functions of a virus such as attachment, entry, replication, transcription, etc. To repurpose and validate polyherbal formulations, molecular docking was performed to study the interactions of more than 150 compounds from various formulations against the SARS-CoV-2 proteins. Molecular dynamics (MD) simulation was performed to evaluate the interaction of top scored ligands with the various receptor proteins. The docking results showed that Liquiritic acid, Liquorice acid, Terchebulin, Glabrolide, Casuarinin, Corilagin, Chebulagic acid, Neochebulinic acid, Daturataturin A, and Taraxerol were effective against SARS-COV-2 proteins with higher binding affinities with different proteins. Results of MD simulations validated the stability of ligands from potent formulations with various receptors of SARS-CoV-2. Binding free energy analysis suggested the favourable interactions of phytocompounds with the recpetors. Besides, in silico comparison of the various formulations determined that Pathyadi kwath, Sanjeevani vati, Yashtimadhu, Tribhuvan Keeratiras, and Septillin were more effective than Samshamni vati, AYUSH-64, and Trikatu. Polyherbal formulations having anti-COVID-19 potential can be used for the treatment with adequate monitoring. New formulations may also be developed for systematic trials based on ranking from these studies.Communicated by Ramaswamy H. Sarma.

Multi-target mechanisms against coronaviruses of constituents from Chinese Dagang Tea revealed by experimental and docking studies

ETHNOPHARMACOLOGICAL RELEVANCE

The leaves of Eurya chinensis（Chinese Dagang Tea）have been consumed as herbal tea for centuries in Guangdong, China, and have also been used to prevent influenza and treat colds and fevers in traditional Chinese medicine. However, there are no reports on the chemical profile and efficacy of its leaves for the treatment of fever and viral infections.

MATERIALS AND METHODS

The chemical constituents of Eurya chinensis leaves were isolated and identified by phytochemical study and spectroscopic data, E. chinensis extracts and compounds were evaluated for their antiviral activities by cytopathic effect (CPE) reduction and antibody-based EC₅₀ assay. The antiviral effect of the main component was confirmed by immunofluorescence and transmission electron microscopy. Virtual screening and docking enzyme inhibition experiments were performed to analyze the anti-coronavirus mechanisms of the compounds from E. chinensis leaves.

RESULTS

In this study, we found for the first time that E. chinensis leaf extract exhibited inhibitory effects against coronaviruses HCoV-OC43 in vitro. Among 23 monomer compounds isolated from E. chinensis leaf extract, the triterpenoids (betulinic acid, α-amyrin) and the flavonoids (naringenin, eriodictyol and quercetin) showed marked antiviral activity. Microscopic optical analyses further demonstrated that betulinic acid can remove virus particles from HCoV-OC43 infected cells. Virtual screening and docking analysis towards the coronavirus in vogue revealed that betulinic acid was able to bind well to PLpro and Nsp14N7-MTase, and that the flavonoids prefer to bind with PLpro, Nsp3MES, NspP14N7-MTase, Nsp16GTA, and Nsp16SAM. The enzyme inhibition experiments demonstrated that betulinic acid (1) exhibited significant inhibition of PLpro and N7-MTase activity of SARS-CoV-2.

CONCLUSION

This study proposes E. chinensis and its triterpenoids and flavonoids as promising potential treatments for coronaviruses.

Novel Drug Design for Treatment of COVID-19: A Systematic Review of Preclinical Studies

Background

Since the beginning of the novel coronavirus (SARS-CoV-2) disease outbreak, there has been an increasing interest in discovering potential therapeutic agents for this disease. In this regard, we conducted a systematic review through an overview of drug development (in silico, in vitro, and in vivo) for treating COVID-19.

Methods

A systematic search was carried out in major databases including PubMed, Web of Science, Scopus, EMBASE, and Google Scholar from December 2019 to March 2021. A combination of the following terms was used: coronavirus, COVID-19, SARS-CoV-2, drug design, drug development, In silico, In vitro, and In vivo. A narrative synthesis was performed as a qualitative method for the data synthesis of each outcome measure.

Results

A total of 2168 articles were identified through searching databases. Finally, 315 studies (266 in silico, 34 in vitro, and 15 in vivo) were included. In studies with in silico approach, 98 article study repurposed drug and 91 studies evaluated herbal medicine on COVID-19. Among 260 drugs repurposed by the computational method, the best results were observed with saquinavir (n = 9), ritonavir (n = 8), and lopinavir (n = 6). Main protease (n = 154) following spike glycoprotein (n = 62) and other nonstructural protein of virus (n = 45) was among the most studied targets. Doxycycline, chlorpromazine, azithromycin, heparin, bepridil, and glycyrrhizic acid showed both in silico and in vitro inhibitory effects against SARS-CoV-2.

Conclusion

The preclinical studies of novel drug design for COVID-19 focused on main protease and spike glycoprotein as targets for antiviral development. From evaluated structures, saquinavir, ritonavir, eucalyptus, Tinospora cordifolia, aloe, green tea, curcumin, pyrazole, and triazole derivatives in in silico studies and doxycycline, chlorpromazine, and heparin from in vitro and human monoclonal antibodies from in vivo studies showed promised results regarding efficacy. It seems that due to the nature of COVID-19 disease, finding some drugs with multitarget antiviral actions and anti-inflammatory potential is valuable and some herbal medicines have this potential.

A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19

BACKGROUND

COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed.

PURPOSE

This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases.

METHODS

A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles.

RESULTS

In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks.

CONCLUSION

Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.

Intrinsically disordered proteins in viral pathogenesis and infections

Disordered proteins serve a crucial part in many biological processes that go beyond the capabilities of ordered proteins. A large number of virus-encoded proteins have extremely condensed proteomes and genomes, which results in highly disordered proteins. The presence of these IDPs allows them to rapidly adapt to changes in their biological environment and play a significant role in viral replication and down-regulation of host defense mechanisms. Since viruses undergo rapid evolution and have a high rate of mutation and accumulation in their proteome, IDPs' insights into viruses are critical for understanding how viruses hijack cells and cause disease. There are many conformational changes that IDPs can adopt in order to interact with different protein partners and thus stabilize the particular fold and withstand high mutation rates. This chapter explains the molecular mechanism behind viral IDPs, as well as the significance of recent research in the field of IDPs, with the goal of gaining a deeper comprehension of the essential roles and functions played by viral proteins.

Developing New Treatments for COVID-19 through Dual-Action Antiviral/Anti-Inflammatory Small Molecules and Physiologically Based Pharmacokinetic Modeling

Broad-spectrum antiviral agents that are effective against many viruses are difficult to develop, as the key molecules, as well as the biochemical pathways by which they cause infection, differ largely from one virus to another. This was more strongly highlighted by the COVID-19 pandemic, which found health systems all over the world largely unprepared and proved that the existing armamentarium of antiviral agents is not sufficient to address viral threats with pandemic potential. The clinical protocols for the treatment of COVID-19 are currently based on the use of inhibitors of the inflammatory cascade (dexamethasone, baricitinib), or inhibitors of the cytopathic effect of the virus (monoclonal antibodies, molnupiravir or nirmatrelvir/ritonavir), using different agents. There is a critical need for an expanded armamentarium of orally bioavailable small-molecular medicinal agents, including those that possess dual antiviral and anti-inflammatory (AAI) activity that would be readily available for the early treatment of mild to moderate COVID-19 in high-risk patients. A multidisciplinary approach that involves the use of in silico screening tools to identify potential drug targets of an emerging pathogen, as well as in vitro and in vivo models for the determination of a candidate drug’s efficacy and safety, are necessary for the rapid and successful development of antiviral agents with potentially dual AAI activity. Characterization of candidate AAI molecules with physiologically based pharmacokinetics (PBPK) modeling would provide critical data for the accurate dosing of new therapeutic agents against COVID-19. This review analyzes the dual mechanisms of AAI agents with potential anti-SARS-CoV-2 activity and discusses the principles of PBPK modeling as a conceptual guide to develop new pharmacological modalities for the treatment of COVID-19.

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.

The Role of Antibodies in the Treatment of SARS-CoV-2 Virus Infection, and Evaluating Their Contribution to Antibody-Dependent Enhancement of Infection

Antibodies play a crucial role in the immune response, in fighting off pathogens as well as helping create strong immunological memory. Antibody-dependent enhancement (ADE) occurs when non-neutralising antibodies recognise and bind to a pathogen, but are unable to prevent infection, and is widely known and is reported as occurring in infection caused by several viruses. This narrative review explores the ADE phenomenon, its occurrence in viral infections and evaluates its role in infection by SARS-CoV-2 virus, which causes coronavirus disease 2019 (COVID-19). As of yet, there is no clear evidence of ADE in SARS-CoV-2, though this area is still subject to further study.

Opportunities and Challenges in Targeting the Proofreading Activity of SARS-CoV-2 Polymerase Complex

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the COVID-19 pandemic. While the development of vaccines and the emergence of antiviral therapeutics is promising, alternative strategies to combat COVID-19 (and potential future pandemics) remain an unmet need. Coronaviruses feature a unique mechanism that may present opportunities for therapeutic intervention: the RNA polymerase complex of coronaviruses is distinct in its ability to proofread and remove mismatched nucleotides during genome replication and transcription. The proofreading activity has been linked to the exonuclease (ExoN) activity of non-structural protein 14 (NSP14). Here, we review the role of NSP14, and other NSPs, in SARS-CoV-2 replication and describe the assays that have been developed to assess the ExoN function. We also review the nucleoside analogs and non-nucleoside inhibitors known to interfere with the proofreading activity of NSP14. Although not yet validated, the potential use of non-nucleoside proofreading inhibitors in combination with chain-terminating nucleosides may be a promising avenue for the development of anti-CoV agents.

Chemical biology and medicinal chemistry of RNA methyltransferases

RNA methyltransferases (MTases) are ubiquitous enzymes whose hitherto low profile in medicinal chemistry, contrasts with the surging interest in RNA methylation, the arguably most important aspect of the new field of epitranscriptomics. As MTases become validated as drug targets in all major fields of biomedicine, the development of small molecule compounds as tools and inhibitors is picking up considerable momentum, in academia as well as in biotech. Here we discuss the development of small molecules for two related aspects of chemical biology. Firstly, derivates of the ubiquitous cofactor S-adenosyl-l-methionine (SAM) are being developed as bioconjugation tools for targeted transfer of functional groups and labels to increasingly visible targets. Secondly, SAM-derived compounds are being investigated for their ability to act as inhibitors of RNA MTases. Drug development is moving from derivatives of cosubstrates towards higher generation compounds that may address allosteric sites in addition to the catalytic centre. Progress in assay development and screening techniques from medicinal chemistry have led to recent breakthroughs, e.g. in addressing human enzymes targeted for their role in cancer. Spurred by the current pandemic, new inhibitors against coronaviral MTases have emerged at a spectacular rate, including a repurposed drug which is now in clinical trial.

Molecular dynamics simulation and docking analysis of NF-κB protein binding with sulindac acid

It is of interest to document the Molecular Dynamics Simulation and docking analysis of NF-κB target with sulindac sodium in combating COVID-19 for further consideration. Sulindac is a nonsteroidal anti-inflammatory drug (NSAID) of the arylalkanoic acid class that is marketed by Merck under the brand name Clinoril. We show the binding features of sulindac sodium with NF-κB that can be useful in drug repurposing in COVID-19 therapy.

Molecular docking and dynamic simulations study for repurposing of multitarget coumarins against SARS-CoV-2 main protease, papain-like protease and RNA-dependent RNA polymerase

Proteases and RNA-Dependent RNA polymerase, major enzymes which are essential targets involved in the life and replication of SARS-CoV-2. This study aims at in silico examination of the potential ability of coumarins and their derivatives to inhibit the replication of SARS-Cov-2 through multiple targets, including the main protease, papain-like protease and RNA-Dependent RNA polymerase. Several coumarins as biologically active compounds were studied, including coumarin antibiotics and some naturally reported antiviral coumarins. Aminocoumarin antibiotics, especially coumermycin, showed a high potential to bind to the enzymes’ active site, causing possible inhibition and termination of viral life. They demonstrate the ability to bind to residues essential for triggering the crucial cascades within the viral cell. Molecular dynamics simulations for 50 ns supported these data pointing out the formation of rigid, stable Coumermycin/enzyme complexes. These findings strongly suggest the possible use of Coumermycin, Clorobiocin or Novobiocin in the fight against COVID-19, but biological evidence is still required to support such suggestions.

Neem and Turmeric in the management of Covid Associated Mucormycosis (CAM) derived through network pharmacology

Mucormycosis or 'Black Fungus' has been known to target immunocompromised individuals even before the emergence of COVID-19. Nevertheless, the present circumstances provide the best opening for Covid Associated Mucormycosis (CAM), as the global pandemic is engulfing a large part of human population making them immunocompromised. This drastic increase in Mucormycosis infections has to be addressed as early as possible. There is a growing tendency of relying upon herbal drugs that have minimal side effects and does not compromise our immune system. Recently, the concept of network pharmacology has grabbed the attention of modern science, especially advanced medical sciences. This is a new discipline that can use computational power to systematically catalogue the molecular interactions between botanical formulations and the human body. In this study, Neem and Turmeric was considered as the target plants and an attempt was made to reveal various aspects through which phytocompounds derived from them may effectively manage CAM menace. We have taken a step-by-step approach for identifying the target proteins and ligands associated with Mucormycosis treatment. Functional network analysis and Molecular docking approaches were applied to validate our findings. Quercetin derived from both Neem and Turmeric was found to be one of the main phytocompounds working against Mucormycosis. Along with that, Caffeic acid, Curcumin, Kaempferol, Tetrahydrocurcumin and Myricetin also play a pivotal role in fighting against Black-Fungus. A thorough analysis of our result suggested a triple-front attack on the fungal pathogens and the approaches are necrosis inhibition, iron chelation and immuno-boosting.Communicated by Ramaswamy H. Sarma.

Phytoestrogens as Potential Antiandrogenic Agents Against Prostate Cancer: An In Silico Analysis

Background:

Prostate cancer is the second most common cancer worldwide. The androgen deprivation therapy or castration leads to the recurrence of castration-resistant prostate cancer after some time. Androgen receptor is one of the most promising targets for the treatment of prostate cancer. The health benefits of phytoestrogens led us to explore them for their androgen receptor inhibition potential that may lead to inhibition of initiation and progression of prostate cancer.

Methods:

Protein-ligand interaction plays a central role in structure-based drug design, so we screened 23 phytoestrogens for their binding affinity to the androgen receptor using the molecular docking approach. These phytoestrogens were also tested for their ADME and toxicity profiles using the software.

Results:

Based on binding affinity, interacting amino acid residues, pharmacokinetics and toxicity profile, four phytoestrogens, namely naringenin, luteolin, hesperetin, and biochanin A were shortlisted as lead molecules.

Conclusion:

Therefore, our study has shown that these four phytoestrogens could be promising candidates for further evaluation for prostate cancer treatment or management.

Exploring the therapeutic nature of limonoids and triterpenoids against SARS-CoV-2 by targeting nsp13, nsp14, and nsp15 through molecular docking and dynamic simulations

Background and aim

The ongoing global pandemic due to SARS-CoV-2 caused a medical emergency. Since December 2019, the COVID-19 disease is spread across the globe through physical contact and respiratory droplets. Coronavirus caused a severe effect on the human immune system where some of the non-structural proteins (nsp) are involved in virus-mediated immune response and pathogenesis. To suppress the viral RNA replication mechanism and immune-mediated responses, we aimed to identify limonoids and triterpenoids as antagonists by targeting helicases (nsp13), exonuclease (nsp14), and endoribonuclease (nsp15) of SARS-CoV-2 as therapeutic proteins.

Experimental procedure

In silico molecular docking and drug-likeness of a library of 369 phytochemicals from limonoids and triterpenoids were performed to screen the potential hits that binds effectively at the active site of the proteins target. In addition, the molecular dynamics simulations of the proteins and their complexes with the potential hits were performed for 100 ns by using GROMACS.

Results and conclusion

The potential compounds 26-deoxyactein and 25-O-anhydrocimigenol 3-O-beta-d-xylopyranoside posing strong interactions with a minimum binding energy of -10.1 and -9.5 kcal/mol, respectively and sustained close contact with nsp13 for 100 ns. The nsp14 replication fork activity was hindered by the tomentosolic acid, timosaponin A-I, and shizukaol A with the binding affinity score of -9.2, -9.2, and -9.0 kcal/mol, respectively. The nsp15 endoribonuclease catalytic residues were inhibited potentially by limonin, 25-O-anhydrocimigenol 3-O-alpha-l-arabinopyranoside, and asperagenin posing strong binding affinity scores of -9.0, -8.8, and -8.7 kcal/mol, respectively. Computationally predicted potential phytochemicals for SARS-CoV-2 are known to possess various medicinal properties.

Discovery of SARS-CoV-2 Nsp14 and Nsp16 Methyltransferase Inhibitors by High-Throughput Virtual Screening

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uses mRNA capping to evade the human immune system. The cap formation is performed by the SARS-CoV-2 mRNA cap methyltransferases (MTases) nsp14 and nsp16, which are emerging targets for the development of broad-spectrum antiviral agents. Here, we report results from high-throughput virtual screening against these two enzymes. The docking of seven million commercially available drug-like compounds and S-adenosylmethionine (SAM) co-substrate analogues against both MTases resulted in 80 virtual screening hits (39 against nsp14 and 41 against nsp16), which were purchased and tested using an enzymatic homogeneous time-resolved fluorescent energy transfer (HTRF) assay. Nine compounds showed micromolar inhibition activity (IC₅₀ < 200 μM). The selectivity of the identified inhibitors was evaluated by cross-checking their activity against human glycine N-methyltransferase. The majority of the compounds showed poor selectivity for a specific MTase, no cytotoxic effects, and rather poor cell permeability. Nevertheless, the identified compounds represent good starting points that have the potential to be developed into efficient viral MTase inhibitors.

Discovery of a Potent Candidate for RET-Specific Non-Small-Cell Lung Cancer-A Combined In Silico and In Vitro Strategy

Rearranged during transfection (RET) is a tyrosine kinase oncogenic receptor, activated in several cancers including non-small-cell lung cancer (NSCLC). Multiple kinase inhibitors vandetanib and cabozantinib are commonly used in the treatment of RET-positive NSCLC. However, specificity, toxicity, and reduced efficacy limit the usage of multiple kinase inhibitors in targeting RET protein. Thus, in the present investigation, we aimed to figure out novel and potent candidates for the inhibition of RET protein using combined in silico and in vitro strategies. In the present study, screening of 11,808 compounds from the DrugBank repository was accomplished by different hypotheses such as pharmacophore, e-pharmacophore, and receptor cavity-based models in the initial stage. The results from the different hypotheses were then integrated to eliminate the false positive prediction. The inhibitory activities of the screened compounds were tested by the glide docking algorithm. Moreover, RF score, Tanimoto coefficient, prime-MM/GBSA, and density functional theory calculations were utilized to re-score the binding free energy of the docked complexes with high precision. This procedure resulted in three lead molecules, namely DB07194, DB03496, and DB11982, against the RET protein. The screened lead molecules together with reference compounds were then subjected to a long molecular dynamics simulation with a 200 ns time duration to validate the inhibitory activity. Further analysis of compounds using MM-PBSA and mutation studies resulted in the identification of potent compound DB07194. In essence, a cell viability assay with RET-specific lung cancer cell line LC-2/ad was also carried out to confirm the in vitro biological activity of the resultant compound, DB07194. Indeed, the results from our study conclude that DB07194 can be effectively translated for this new therapeutic purpose, in contrast to the properties for which it was originally designed and synthesized.

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2

The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1– 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against 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.

In Silico Evaluation of Iranian Medicinal Plant Phytoconstituents as Inhibitors against Main Protease and the Receptor-Binding Domain of SARS-CoV-2

The novel coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in Wuhan, China, in December 2019. Elderly individuals and those with comorbid conditions may be more vulnerable to this disease. Consequently, several research laboratories continue to focus on developing drugs to treat this infection because this disease has developed into a global pandemic with an extremely limited number of specific treatments available. Natural herbal remedies have long been used to treat illnesses in a variety of cultures. Modern medicine has achieved success due to the effectiveness of traditional medicines, which are derived from medicinal plants. The objective of this study was to determine whether components of natural origin from Iranian medicinal plants have an antiviral effect that can prevent humans from this coronavirus infection using the most reliable molecular docking method; in our case, we focused on the main protease (Mpro) and a receptor-binding domain (RBD). The results of molecular docking showed that among 169 molecules of natural origin from common Iranian medicinal plants, 20 molecules (chelidimerine, rutin, fumariline, catechin gallate, adlumidine, astragalin, somniferine, etc.) can be proposed as inhibitors against this coronavirus based on the binding free energy and type of interactions between these molecules and the studied proteins. Moreover, a molecular dynamics simulation study revealed that the chelidimerine-Mpro and somniferine-RBD complexes were stable for up to 50 ns below 0.5 nm. Our results provide valuable insights into this mechanism, which sheds light on future structure-based designs of high-potency inhibitors for SARS-CoV-2.

Structural Understanding of SARS-CoV-2 Drug Targets, Active Site Contour Map Analysis and COVID-19 Therapeutics

The most iconic word of the year 2020 is 'COVID-19', the shortened name for coronavirus disease 2019. The pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is responsible for multiple worldwide lockdowns, an economic crisis, and a substantial increase in hospitalizations for viral pneumonia along with respiratory failure and multiorgan dysfunctions. Recently, the first few vaccines were approved by World Health Organization (WHO) and can eventually save millions of lives. Even though, few emergency use drugs like Remdesivir and several other repurposed drugs, still there is no approved drug for COVID-19. The coronaviral encoded proteins involved in host-cell entry, replication, and host-cell invading mechanism are potentially therapeutic targets. This perspective review provides the molecular overview of SARS-CoV-2 life cycle for summarizing potential drug targets, structural insights, active site contour map analyses of those selected SARS-CoV-2 protein targets for drug discovery, immunology, and pathogenesis.

Comprehensive analysis of SARS-COV-2 drug targets and pharmacological aspects in treating the COVID-19

Corona viruses are enveloped, single-stranded RNA (Ribonucleic acid) viruses and they cause pandemic diseases having a devastating effect on both human healthcare and the global economy. To date, six corona viruses have been identified as pathogenic organisms which are significantly responsible for the infection and also cause severe respiratory diseases. Among them, the novel SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) caused a major outbreak of corona virus diseases 2019 (COVID-19). Coronaviridae family members can affects both humans and animals. In human, corona viruses cause severe acute respiratory syndrome with mild to severe outcomes. Several structural and genomics have been investigated, and the genome encodes about 28 proteins most of them with unknown function though it shares remarkable sequence identity with other proteins. There is no potent and licensed vaccine against SARS-CoV-2 and several trials are underway to investigate the possible therapeutic agents against viral infection. However, some of the antiviral drugs that have been investigated against SARS-CoV-2 are under clinical trials. In the current review we comparatively emphasize the emergence and pathogenicity of the SARS-CoV-2 and their infection and discuss the various putative drug targets of both viral and host receptors for developing effective vaccines and therapeutic combinations to overcome the viral outbreak.

Science's Response to CoVID-19

CoVID-19 is a multi-symptomatic disease which has made a global impact due to its ability to spread rapidly, and its relatively high mortality rate. Beyond the heroic efforts to develop vaccines, which we do not discuss herein, the response of scientists and clinicians to this complex problem has reflected the need to detect CoVID-19 rapidly, to diagnose patients likely to show adverse symptoms, and to treat severe and critical CoVID-19. Here we aim to encapsulate these varied and sometimes conflicting approaches and the resulting data in terms of chemistry and biology. In the process we highlight emerging concepts, and potential future applications that may arise out of this immense effort.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Immune evasion of SARS-CoV-2 from interferon antiviral system

The on-going pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to unprecedented medical and socioeconomic crises. Although the viral pathogenesis remains elusive, deficiency of effective antiviral interferon (IFN) responses upon SARS-CoV-2 infection has been recognized as a hallmark of COVID-19 contributing to the disease pathology and progress. Recently, multiple proteins encoded by SARS-CoV-2 have been shown to act as potential IFN antagonists with diverse possible mechanisms. Here, we summarize and discuss the strategies of SARS-CoV-2 for evasion of innate immunity (particularly the antiviral IFN responses), understanding of which will facilitate not only the elucidation of SARS-CoV-2 infection and pathogenesis but also the development of antiviral intervention therapies.

Structure-Based Virtual Screening Reveals Ibrutinib and Zanubrutinib as Potential Repurposed Drugs against COVID-19

Coronavirus disease (COVID)-19 is the leading global health threat to date caused by a severe acute respiratory syndrome coronavirus (SARS-CoV-2). Recent clinical trials reported that the use of Bruton's tyrosine kinase (BTK) inhibitors to treat COVID-19 patients could reduce dyspnea and hypoxia, thromboinflammation, hypercoagulability and improve oxygenation. However, the mechanism of action remains unclear. Thus, this study employs structure-based virtual screening (SBVS) to repurpose BTK inhibitors acalabrutinib, dasatinib, evobrutinib, fostamatinib, ibrutinib, inositol 1,3,4,5-tetrakisphosphate, spebrutinib, XL418 and zanubrutinib against SARS-CoV-2. Molecular docking is conducted with BTK inhibitors against structural and nonstructural proteins of SARS-CoV-2 and host targets (ACE2, TMPRSS2 and BTK). Molecular mechanics-generalized Born surface area (MM/GBSA) calculations and molecular dynamics (MD) simulations are then carried out on the selected complexes with high binding energy. Ibrutinib and zanubrutinib are found to be the most potent of the drugs screened based on the results of computational studies. Results further show that ibrutinib and zanubrutinib could exploit different mechanisms at the viral entry and replication stage and could be repurposed as potential inhibitors of SARS-CoV-2 pathogenesis.

An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19

The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.

The Antiviral and Virucidal Activities of Voacangine and Structural Analogs Extracted from Tabernaemontana cymosa Depend on the Dengue Virus Strain

Currently, no specific licensed antiviral exists for treating the illness caused by dengue virus (DENV). Therefore, the search for compounds of natural origin with antiviral activity is an important area of research. In the present study, three compounds were isolated and identified from seeds of Tabernaemontana cymosa plants. The in vitro antiviral effect of those compounds and voacangine against different DENV strains was assessed using different experimental approaches: compounds added before the infection (Pre), at the same time with the virus (Trans), after the infection (Post) or compounds present in all moments of the experiment (Pre-Trans-Post, Combined treatment). In silico studies (docking and molecular dynamics) were also performed to explain the possible antiviral mechanisms. The identified compounds were three structural analogs of voacangine (voacangine-7-hydroxyindolenine, rupicoline and 3-oxo-voacangine). In the Pre-treatment, only voacangine-7-hydroxyindolenine and rupicoline inhibited the infection caused by the DENV-2/NG strain (16.4% and 29.6% infection, respectively). In the Trans-treatment approach, voacangine, voacangine-7-hydroxyindolenine and rupicoline inhibited the infection in both DENV-2/NG (11.2%, 80.4% and 75.7% infection, respectively) and DENV-2/16681 infection models (73.7%, 74.0% and 75.3% infection, respectively). The latter strain was also inhibited by 3-oxo-voacangine (82.8% infection). Moreover, voacangine (most effective virucidal agent) was also effective against one strain of DENV-1 (DENV-1/WestPac/74) and against the third strain of DENV-2 (DENV-2/S16803) (48.5% and 32.4% infection, respectively). Conversely, no inhibition was observed in the post-treatment approach. The last approach (combined) showed that voacangine, voacangine-7-hydroxyindolenine and rupicoline inhibited over 90% of infections (3.5%, 6.9% and 3.5% infection, respectively) of both strains (DENV-2/NG and DENV-2/16681). The free energy of binding obtained with an in silico approach was favorable for the E protein and compounds, which ranged between −5.1 and −6.3 kcal/mol. Finally, the complex formed between DENV-2 E protein and the best virucidal compound was stable for 50 ns. Our results show that the antiviral effect of indole alkaloids derived from T. cymose depends on the serotype and the virus strain.

Computational search for drug repurposing to identify potential inhibitors against SARS-COV-2 using Molecular Docking, QTAIM and IQA methods in viral Spike protein - Human ACE2 interface

With the advancement of the Covid-19 pandemic, this work aims to find molecules that can inhibit the attraction between the Spike proteins of the SARS-COV-2 virus and human ACE2. The results of molecular docking positioned four molecules at the interaction site Tyr-491(Spike)-Glu-37(ACE2) and one at the site Gly-488(Spike)-Lys-353(ACE2). The QTAIM and IQA data showed that the 1629 molecule had a significant inhibitory effect on the Gly488-Ly353 site, decreasing the Laplacian of the electronic density of the BCP O₄-N₁₀. The molecule 2542 showed an inhibitory effect in two regions of interaction of the Tyr491-Glu37 site, acting on the BCPs H₃₀-H₃₃ and O₈-H₃₁ while the ligand 2600, in conformation 26, presented a similar effect only on the BCP O₈-H₃₁ of that same interactive site. Thus, the data suggest laboratory tests of a combination of molecules that can act at two sites of interaction simultaneously, using the combination of 1629/2542 and 1629/2600 ligands.

Exploring safe and potent bioactives for the treatment of non-small cell lung cancer

Activating and suppressing mutations in the MAPK pathway receptors are the primary causes of NSCLC. Of note, MEK inhibition is considered a promising strategy because of the diverse structures and harmful effects of upstream receptors in MAPK pathway. Thus, we explore a total of 1574 plant-based bioactive compounds activity against MEK using an energy-based virtual screening strategy. Molecular docking, binding free energy, and drug-likeness analysis were performed through GLIDE, Prime MM-GBSA, and QikProp module, respectively. The findings indicate that 5-O-caffeoylshikimic acid has an increased binding affinity to MEK protein. Further, molecular dynamic simulations and MM-PBSA analysis were performed to explore the ligand activity in real-life situations. In essence, compounds inhibitory activity was validated across 77 lung cancer cell lines using multimodal attention-based neural network algorithm. Eventually, our analysis highlight that 5-O-caffeoylshikimic acid obtained from the bark of Rhizoma smilacis glabrae would be developed as a potential compound for treating NSCLC.

Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation

Background

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has ravaged lives across the globe since December 2019, and new cases are still on the rise. Peoples’ ongoing sufferings trigger scientists to develop safe and effective remedies to treat this deadly viral disease. While repurposing the existing FDA-approved drugs remains in the front line, exploring drug candidates from synthetic and natural compounds is also a viable alternative. This study employed a comprehensive computational approach to screen inhibitors for SARS-CoV-2 3CL-PRO (also known as the main protease), a prime molecular target to treat coronavirus diseases.

Methods

We performed 100 ns GROMACS molecular dynamics simulations of three high-resolution X-ray crystallographic structures of 3CL-PRO. We extracted frames at 10 ns intervals to mimic conformational diversities of the target protein in biological environments. We then used AutoDock Vina molecular docking to virtual screen the Sigma–Aldrich MyriaScreen Diversity Library II, a rich collection of 10,000 druglike small molecules with diverse chemotypes. Subsequently, we adopted in silico computation of physicochemical properties, pharmacokinetic parameters, and toxicity profiles. Finally, we analyzed hydrogen bonding and other protein-ligand interactions for the short-listed compounds.

Results

Over the 100 ns molecular dynamics simulations of 3CL-PRO’s crystal structures, 6LZE, 6M0K, and 6YB7, showed overall integrity with mean Cα root-mean-square deviation (RMSD) of 1.96 (±0.35) Å, 1.98 (±0.21) Å, and 1.94 (±0.25) Å, respectively. Average root-mean-square fluctuation (RMSF) values were 1.21 ± 0.79 (6LZE), 1.12 ± 0.72 (6M0K), and 1.11 ± 0.60 (6YB7). After two phases of AutoDock Vina virtual screening of the MyriaScreen Diversity Library II, we prepared a list of the top 20 ligands. We selected four promising leads considering predicted oral bioavailability, druglikeness, and toxicity profiles. These compounds also demonstrated favorable protein-ligand interactions. We then employed 50-ns molecular dynamics simulations for the four selected molecules and the reference ligand 11a in the crystallographic structure 6LZE. Analysis of RMSF, RMSD, and hydrogen bonding along the simulation trajectories indicated that S51765 would form a more stable protein-ligand complexe with 3CL-PRO compared to other molecules. Insights into short-range Coulombic and Lennard-Jones potentials also revealed favorable binding of S51765 with 3CL-PRO.

Conclusion

We identified a potential lead for antiviral drug discovery against the SARS-CoV-2 main protease. Our results will aid global efforts to find safe and effective remedies for COVID-19.

Recent efforts for drug identification from phytochemicals against SARS-CoV-2: Exploration of the chemical space to identify druggable leads

Nature, which remains a central drug discovery pool, is always looked upon to find a putative druggable lead. The natural products and phytochemical derived from plants are essential during a global health crisis. This class represents one of the most practical and promising approaches to decrease pandemic's intensity owing to their therapeutic potential. The present manuscript is therefore kept forth to give the researchers updated information on undergoing research in allied areas of natural product-based drug discovery, particularly for Covid-19 disease. The study briefly shreds evidence from in vitro and in silico researches done so far to find a lead molecule against Covid-19. Following this, we exhaustively explored the concept of chemical space and molecular similarity parameters for the drug discovery about the lead(s) generated from in silico-based studies. The comparison was drawn using FDA-approved anti-infective agents during 2015–2020 using key descriptors to evaluate druglike properties. The outcomes of results were further corroborated using Molecular Dynamics studies which suggested the outcomes in alignment with chemical space ranking. In a nutshell, current research work aims to provide a holistic strategic approach to drug design, keeping in view the identified phytochemicals against Covid-19.

Structure insights of SARS-CoV-2 open state envelope protein and inhibiting through active phytochemical of ayurvedic medicinal plants from Withania somnifera

Coronaviruses have been causing pandemic situations across the globe for the past two decades and the focus is on identifying suitable novel targets for antivirals and vaccine development. SARS-CoV-2 encodes a small hydrophobic envelope (E) protein that mediates envelope formation, budding, replication, and release of progeny viruses from the host. Through this study, the SARS-CoV-2 E protein is studied for its open and closed state and focused in identifying antiviral herbs used in traditional medicine practices for COVID-19 infections. In this study using computational tools, we docked the shortlisted phytochemicals with the envelope protein of the SARS-CoV-2 virus and the results hint that these compounds interact with the pore-lining residues. The molecular level understanding of the open state is considered and the active inhibitors from the phytochemicals of Ayurvedic medicinal plants from Withania somnifera. We have thus identified a potential phytochemical compound that directly binds with the pore region of the E protein and thereby blocks its channel activity. Blocking the ion channel activity of E protein is directly related to the inhibition of virus replication. The study shows encouraging results on the usage of these phytochemicals in the treatment/management of SARS-CoV-2 infection.