Identification of antiviral phytochemicals as a potential SARS-CoV-2 main protease (Mpro) inhibitor using docking and molecular dynamics simulations

Investigating the Antiviral Properties of Nyctanthes arbor-tristis Linn against the Ebola, SARS-CoV-2, Nipah, and Chikungunya Viruses: A Computational Simulation Study

Background: The hunt for naturally occurring antiviral compounds to combat viral infection was expedited when COVID-19 and Ebola spread rapidly. Phytochemicals from Nyctanthes arbor-tristis Linn were evaluated as significant inhibitors of these viruses. Methods: Computational tools and techniques were used to assess the binding pattern of phytochemicals from Nyctanthes arbor-tristis Linn to Ebola virus VP35, SARS-CoV-2 protease, Nipah virus glycoprotein, and chikungunya virus. Results: Virtual screening and AutoDock analysis revealed that arborside-C, beta amyrin, and beta-sitosterol exhibited a substantial binding affinity for specific viral targets. The arborside-C and beta-sitosterol molecules were shown to have binding energies of -8.65 and -9.11 kcal/mol, respectively, when interacting with the major protease. Simultaneously, the medication remdesivir exhibited a control value of -6.18 kcal/mol. The measured affinity of phytochemicals for the other investigated targets was -7.52 for beta-amyrin against Ebola and -6.33 kcal/mol for nicotiflorin against Nipah virus targets. Additional molecular dynamics simulation (MDS) conducted on the molecules with significant antiviral potential, specifically the beta-amyrin-VP35 complex showing a stable RMSD pattern, yielded encouraging outcomes. Conclusions: Arborside-C, beta-sitosterol, beta-amyrin, and nicotiflorin could be established as excellent natural antiviral compounds derived from Nyctanthes arbor-tristis Linn. The virus-suppressing phytochemicals in this plant make it a compelling target for both in vitro and in vivo research in the future.

Harnessing Brazilian biodiversity database: identification of flavonoids as potential inhibitors of SARS-CoV-2 main protease using computational approaches and all-atom molecular dynamics simulation

SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is the etiological agent responsible for the global outbreak of COVID-19 (Coronavirus Disease 2019). The main protease of SARS-CoV-2, Mpro, is a key enzyme that plays a vital role in mediating viral replication and transcription. In this study, a comprehensive computational approach was employed to investigate the binding affinity, selectivity, and stability of natural product candidates as potential new antivirals acting on the viral polyprotein processing mediated by SARS-CoV-2 Mpro. A library of 288 flavonoids extracted from Brazilian biodiversity was screened to select potential Mpro inhibitors. An initial filter based on Lipinski's rule of five was applied, and 204 compounds that did not violate any of the Lipinski rules were selected. The compounds were then docked into the active site of Mpro using the GOLD program, and the poses were subsequently re-scored using MM-GBSA (Molecular Mechanics Generalized Born Surface Area) binding free energy calculations performed by AmberTools23. The top five flavonoids with the best MM-GBSA binding free energy values were selected for analysis of their interactions with the active site residues of the protein. Next, we conducted a toxicity and drug-likeness analysis, and non-toxic compounds were subjected to molecular dynamics simulation and free energy calculation using the MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. It was observed that the five selected flavonoids had lower MM-GBSA binding free energy with Mpro than the co-crystal ligand. Furthermore, these compounds also formed hydrogen bonds with two important residues, Cys145 and Glu166, in the active site of Mpro. Two compounds that passed the drug-likeness filter showed stable conformations during the molecular dynamics simulations. Among these, NuBBE_867 exhibited the best MM-PBSA binding free energy value compared to the crystallographic inhibitor. Therefore, this study suggests that NuBBE_867 could be a potential inhibitor against the main protease of SARS-CoV-2 and may be further examined to confirm our results.

Large-scale computational screening of Indian medicinal plants reveals Cassia angustifolia to be a potentially anti-diabetic

Researchers are investigating the medicinal properties of herbal plants throughout the world, which often leads to the discovery of novel plants and their chemicals for prophylactic needs of humans. Natural phytochemicals continue to be sought as alternative treatments for various diseases because of their non-toxic and therapeutic properties. In recent years, computational phytochemistry has enabled large-scale screening of phytochemicals, enabling researchers to pursue a wide range of therapeutic research alternatives to traditional ethnopharmacology. We propose to identify an anti-diabetic plant by computational screening on Indian herbal plants in conjunction with experimental characterization and biological validation. The methodology involves the creation of an in-house Indian herbal plant database. Molecular docking is used to screen against alpha amylase for anti-diabetic prophylaxis. Cassia angustifolia was chosen because its phytochemicals are able to bind to alpha amylase. Plants were experimentally extracted, botanically studied and their biological activity was evaluated. Further, the use of molecular dynamics was then applied to pinpoint the phytochemicals responsible for the affinity of alpha amylase. Results in the phytochemical analysis of the extracts revealed strong presence of alkaloids, flavonoids and cardiac glycosides. Moreover, alpha amylase biological activity with C. angustifolia extracts of chloroform, hexane and ethyl acetate demonstrated activity of 3.26, 8.01 and 30.33 µg/ml validating computational predictions. In conclusion, this study developed, validated computational predictions of identifying potential anti-diabetic plants 'Cassia angustifolia' from house herbal databases. Hope this study shall inspire explore plant therapeutic repurposing using computational methods of drug discovery.Communicated by Ramaswamy H. Sarma.

Deciphering insights into the binding mechanism and plasticity of Telacebec with M. tuberculosis cytochrome bcc-aa3 supercomplex through an unbiased molecular dynamics simulation, free-energy analysis, and DFT study

The cytochrome bcc-aa3 supercomplex, a key component in the electron transport chain pathway involved in bacterial energy production and homeostasis, is a clinically validated target for tuberculosis (TB), leading to Telacebec (Q203). Telacebec is a potent candidate drug under Phase II clinical development for the treatment of drug-sensitive and drug-resistant TB. Recently, the cryo-electron microscopy structure of this supercomplex from Mycobacterium tuberculosis (Mtb) complexed with Q203 was resolved at 6.9 Å resolution (PDB ID: 7E1W). To understand the binding site (QP site) flexibility and Q203's stability at the QP site of the Mtb cytochrome bcc complex, we conducted molecular dynamics (MD) simulation and free energy analysis on this complex in an explicit hydrated lipid bilayer environment for 500 ns. Through this study, the persistence of a range of direct and indirect interactions was observed over the course of the simulation. The significance of the interactions with His375, Tyr161, Ala178, Ala179, Ile183, His355, Leu356, and Thr313 is underlined. Electrostatic energy was the primary source of the net binding free energy, regardless of the important interacting residues. The overall binding free energy for Q203 was -112.84 ± 7.73 kcal/mol, of which the electrostatic and lipophilic energy contributions were -116.31 ± 1.14 and -21.32 ± 2.35 kcal/mol, respectively. Meanwhile, DFT calculations were utilized to elucidate Q203's molecular properties. Overall, this study deciphers key insights into the cytochrome bcc-aa3 supercomplex with Q203 on the ground of molecular mechanics and quantum mechanics that may facilitate structure-based drug design and optimization for the discovery of the next-generation antitubercular drug(s).Communicated by Ramaswamy H. Sarma.

Protective Effects of Rhamnetin in Carbapenem-Resistant Acinetobacter baumannii-Induced Sepsis Model and the Underlying Mechanism

Carbapenem-resistant Acinetobacter baumannii (CRAB) is a well-known harmful bacterium that causes severe health disorders and dysregulates the host immune response associated with inflammation. Upon examining the suppressive activity of natural flavonoid rhamnetin on various pro-inflammatory cytokines in a CRAB-induced septic shock mouse model, we found that rhamnetin inhibited the production of IL-1β and IL-18, two pro-inflammatory cytokines associated with pyroptotic cell death, a process dependent on caspase-1. In this study, we investigated the antioxidant and anti-apoptotic activities of rhamnetin and the underlying mechanism of action in a CRAB infection. In the CRAB-induced septic shock mouse model, rhamnetin reduced the level of lipopolysaccharide (LPS) in lung lysates, resulting in the inhibition of TLR4-mediated inflammatory signaling. Notably, rhamnetin reduced intracellular reactive oxygen species (ROS) generation in macrophages and inhibited apoptotic and pyroptotic cell injury induced by CRAB infection. Therefore, rhamnetin inhibited LPS-induced pro-inflammatory mediators, hindering apoptotic and pyroptotic processes and contributing to a recovery effect in CRAB-induced sepsis mice by suppressing oxidative stress. Taken together, our study presents the potential role of rhamnetin in protecting against oxidative damage induced by CRAB infection through a TLR4 and ROS-mediated pyroptotic pathway, showing an alternative mechanism for sepsis prevention. Therefore, rhamnetin is a promising therapeutic candidate for treating CRAB-induced sepsis.

Phytoconstituents as potential therapeutic agents against COVID-19: a computational study on inhibition of SARS-CoV-2 main protease

The Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) has become a global health crisis, and the urgent need for effective treatments is evident. One potential target for COVID-19 therapeutics is the main protease (Mpro) of SARS‑CoV‑2, an essential enzyme for viral replication. Natural compounds have been explored as a source of potential inhibitors for Mpro due to their safety and availability. In this study, we employed a computational approach to screen a library of phytoconstituents and identified potential Mpro inhibitors based on their binding affinities and molecular interactions. The top-ranking compounds were further validated through molecular dynamics simulations (MDS) and free energy calculations. As a result of the above procedures, we identified two phytoconstituents, Khelmarin B and Neogitogenin, with appreciable binding affinity and specificity towards the Mpro binding pocket. Our results suggest that Khelmarin B and Neogitogenin could potentially serve as Mpro inhibitors and have the potential to be developed as COVID-19 therapeutics. Further experimental studies are required to confirm the efficacy and safety of these compounds.Communicated by Ramaswamy H. Sarma.

Molecular docking and dynamics simulation of Orthosiphon stamineus against SGLT1 and SGLT2

Orthosiphon stamineus Benth a traditional medicine used in the treatment of diabetes and kidney diseases. Sodium-glucose co-transporter (SGLT1 and SGLT2) inhibitors are the novel group of drugs used to treat patients with type 2 diabetes mellitus. In this study 20 phytochemical compounds from Orthosiphon stamineus Benth were obtained from 3 databases viz Dr.Duke's phytochemical, Ethno botanical database and IMPPAT. They were subjected to physiochemical, drug likeliness, and ADMET and toxicity predictions. Homology modeling and molecular docking against SGLT1 and SGLT2 were performed and the stability of the selected drug molecule was validated by molecular dynamic (MD) simulation for 200 ns. Among the 20 compounds, 14-Dexo-14-O-acetylorthosiphol Y alone showed higher binding affinity with SGLT1 and SGLT2 protein with the binding energy of -9.6 and -11.4 Kcal/mol respectively and had highest affinity towards SGLT2 inhibitor. This compound also satisfied Lipinski rule of 5 and had a good ADMET profile. The compound is non-toxic to marine organisms and to normal cell lines and non-mutagenic. The RMSD value attained equilibrium at 150 ns with the stability around 4.8 Å and no significant deviation was reported from 160 to 200 ns for SGLT2. Our study suggests that 14-Dexo-14-O-acetylorthosiphol Y showed promising results against the SGLT2 and could be considered as a potent anti-diabetic drug.Communicated by Ramaswamy H. Sarma.

AI-driven drug repurposing and binding pose meta dynamics identifies novel targets for Monkeypox virus

Monkeypox virus (MPXV) was confirmed in May 2022 and designated a global health emergency by WHO in July 2022. MPX virions are big, enclosed, brick-shaped, and contain a linear, double-stranded DNA genome as well as enzymes. MPXV particles bind to the host cell membrane via a variety of viral-host protein interactions. As a result, the wrapped structure is a potential therapeutic target. DeepRepurpose, an artificial intelligence-based compound-viral proteins interaction framework, was used via a transfer learning setting to prioritize a set of FDA approved and investigational drugs which can potentially inhibit MPXV viral proteins. To filter and narrow down the lead compounds from curated collections of pharmaceutical compounds, we used a rigorous computational framework that included homology modeling, molecular docking, dynamic simulations, binding free energy calculations, and binding pose metadynamics. We identified Elvitegravir as a potential inhibitor of MPXV virus using our comprehensive pipeline.

In silico Screening of Potential SARS-CoV-2 Main Protease Inhibitors from Thymus schimperi

Background

COVID-19 is still instigating significant social and economic chaos worldwide; however, there is no approved antiviral drug yet. Here, we used in silico analysis to screen potential SARS-CoV-2 main protease (M^pro) inhibitors extracted from the essential oil of Thymus schimperi which could contribute to the discovery of potent anti-SARS-CoV-2 phytochemicals.

Methods

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of compounds were determined through SwissADME and ProToxII servers. AutoDock tools were used for molecular docking analysis studies, while Chimera, DS studio, and LigPlot were used for post-docking studies. Molecular dynamic simulations were performed for 200 ns under constant pressure.

Results

All compounds exhibited a bioavailability score of ≥0.55 entailing that at least 55% of the drugs can be absorbed unchanged. Only five (9%), nine (16%) and two (3.6%) of the compounds showed active hepatotoxicity, carcinogenicity, and immunotoxicity, respectively. Except for flourazophore P, which showed a little mutagenicity, all other compounds did not show mutagenic properties. On the other hand, only pinene beta was found to have a little cytotoxicity. Five compounds demonstrated effective binding to the catalytic dyad of the SARS-CoV-2 M^pro substrate binding pocket, while two of them (geranylisobutanoate and 3-octane) are found to be the best hits that formed hydrogen bonds with Glu¹⁶⁶ and Ser¹⁴⁴ of SARS-CoV-2 M^pro.

Conclusion

Based on our in silico analysis, top hits from Thymus schimperi may serve as potential anti-SARS-CoV-2 compounds. Further in vitro and in vivo studies are recommended to characterize these compounds for clinical applications.

Identification of potential inhibitors of omicron variant of SARS-Cov-2 RBD based virtual screening, MD simulation, and DFT

Emergence of the SARS-CoV-2 Omicron variant of concern (VOC; B.1.1.529) resulted in a new peak of the COVID-19 pandemic, which called for development of effective therapeutics against the Omicron VOC. The receptor binding domain (RBD) of the spike protein, which is responsible for recognition and binding of the human ACE2 receptor protein, is a potential drug target. Mutations in receptor binding domain of the S-protein have been postulated to enhance the binding strength of the Omicron VOC to host proteins. In this study, bioinformatic analyses were performed to screen for potential therapeutic compounds targeting the omicron VOC. A total of 92,699 compounds were screened from different libraries based on receptor binding domain of the S-protein via docking and binding free energy analysis, yielding the top 5 best hits. Dynamic simulation trajectory analysis and binding free energy decomposition were used to determine the inhibitory mechanism of candidate molecules by focusing on their interactions with recognized residues on receptor binding domain. The ADMET prediction and DFT calculations were conducted to determine the pharmacokinetic parameters and precise chemical properties of the identified molecules. The molecular properties of the identified molecules and their ability to interfere with recognition of the human ACE2 receptors by receptor binding domain suggest that they are potential therapeutic agents for SARS-CoV-2 Omicron VOC.

Pathfinder-Driven Chemical Space Exploration and Multiparameter Optimization in Tandem with Glide/IFD and QSAR-Based Active Learning Approach to Prioritize Design Ideas for FEP+ Calculations of SARS-CoV-2 PLpro Inhibitors

A global pandemic caused by the SARS-CoV-2 virus that started in 2020 and has wreaked havoc on humanity still ravages up until now. As a result, the negative impact of travel restrictions and lockdowns has underscored the importance of our preparedness for future pandemics. The main thrust of this work was based on addressing this need by traversing chemical space to design inhibitors that target the SARS-CoV-2 papain-like protease (PLpro). Pathfinder-based retrosynthesis analysis was used to generate analogs of GRL-0617 using commercially available building blocks by replacing the naphthalene moiety. A total of 10 models were built using active learning QSAR, which achieved good statistical results such as an R2 > 0.70, Q2 > 0.64, STD Dev < 0.30, and RMSE < 0.31, on average for all models. A total of 35 ideas were further prioritized for FEP+ calculations. The FEP+ results revealed that compound 45 was the most active compound in this series with a ΔG of −7.28 ± 0.96 kcal/mol. Compound 5 exhibited a ΔG of −6.78 ± 1.30 kcal/mol. The inactive compounds in this series were compound 91 and compound 23 with a ΔG of −5.74 ± 1.06 and −3.11 ± 1.45 kcal/mol. The combined strategy employed here is envisaged to be of great utility in multiparameter lead optimization efforts, to traverse chemical space, maintaining and/or improving the potency as well as the property space of synthetically aware design ideas.

Computational investigation of natural compounds as potential main protease (Mpro) inhibitors for SARS-CoV-2 virus

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is significantly impacting human lives, overburdening the healthcare system and weakening global economies. Plant-derived natural compounds are being largely tested for their efficacy against COVID-19 targets to combat SARS-CoV-2 infection. The SARS-CoV-2 Main protease (M^pro) is considered an appealing target because of its role in replication in host cells. We curated a set of 7809 natural compounds by combining the collections of five databases viz Dr Duke's Phytochemical and Ethnobotanical database, IMPPAT, PhytoHub, AromaDb and Zinc. We applied a rigorous computational approach to identify lead molecules from our curated compound set using docking, dynamic simulations, the free energy of binding and DFT calculations. Theaflavin and ginkgetin have emerged as better molecules with a similar inhibition profile in both SARS-CoV-2 and Omicron variants.

Computational molecular interaction between SARS-CoV-2 main protease and theaflavin digallate using free energy perturbation and molecular dynamics

Our objective was to identify the molecule which can inhibit SARS-CoV-2 main protease and can be easily procured. Natural products may provide such molecules and can supplement the current custom chemical synthesis-based drug discovery for this objective. A combination of docking approaches, scoring functions, classical molecular dynamic simulation, binding pose metadynamics, and free energy perturbation calculations have been employed in this study. Theaflavin digallate has been observed in top-scoring compounds after the three independent virtual screening simulations of 598435 compounds (unique 27256 chemical entities). The main protease-theaflavin digallate complex interacts with critical active site residues of the main protease in molecular dynamics simulation independent of the explored computational framework, simulation time, initial structure, and force field used. Theaflavin digallate forms approximately three hydrogen bonds with Glutamate166 of main protease, primarily through hydroxyl groups in the benzene ring of benzo(7)annulen-6-one, along with other critical residues. Glu166 is the most critical amino acid for main protease dimerization, which is necessary for catalytic activity. The estimated binding free energy, calculated by Amber and Schrodinger MMGBSA module, reflects a high binding free energy between theaflavin digallate and main protease. Binding pose metadynamics simulation shows the highly persistent H-bond and a stable pose for the theaflavin digallate-main protease complex. Using method control, experimental controls, and test set, alchemical transformation studies confirm high relative binding free energy of theaflavin digallate with the main protease. Computational molecular interaction suggests that theaflavin digallate can inhibit the main protease of SARS-CoV-2.

In silico study on the Hepatitis E virus RNA Helicase and its inhibition by silvestrol, rocaglamide and other flavagline compounds

Hepatitis E Virus (HEV) follows waterborne or zoonotic/foodborne transmission. Genotype 3 HEV infections are worldwide spread, especially in swine populations, representing an emerging threat for human health, both for farm workers and pork meat consumers. Unfortunately, HEV in vitro culture and analysis are still difficult, resulting in a poor understanding of its biology and hampering the implementation of counteracting strategies. Indeed, HEV encodes for only one non-structural multifunctional and multidomain protein (ORF1), which might be a good candidate for anti-HEV drugging strategies. In this context, an in silico molecular modelling approach that consisted in homology modelling to derive the 3D model target, docking study to simulate the binding event, and molecular dynamics to check complex stability over time was used. This workflow succeeded to describe ORF1 RNA Helicase domain from a molecular standpoint allowing the identification of potential inhibitory compounds among natural plant-based flavagline-related molecules such as silvestrol, rocaglamide and derivatives thereof. In the context of scouting potential anti-viral compounds and relying on the outcomes presented, further dedicated investigations on silvestrol, rocaglamide and a promising oxidized derivative have been suggested. For the sake of data reproducibility, the 3D model of HEV RNA Helicase has been made publicly available.

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.

In Silico Analysis Using SARS-CoV-2 Main Protease and a Set of Phytocompounds to Accelerate the Development of Therapeutic Components against COVID-19

SARS-CoV-2, the virus that caused the widespread COVID-19 pandemic, is homologous to SARS-CoV. It would be ideal to develop antivirals effective against SARS-CoV-2. In this study, we chose one therapeutic target known as the main protease (Mpro) of SARS-CoV-2. A crystal structure (Id: 6LU7) from the protein data bank (PDB) was used to accomplish the screening and docking studies. A set of phytocompounds was used for the docking investigation. The nature of the interaction and the interacting residues indicated the molecular properties that are essential for significant affinity. Six compounds were selected, based on the docking as well as the MM-GBSA score. Pentagalloylglucose, Shephagenin, Isoacteoside, Isoquercitrin, Kappa-Carrageenan, and Dolabellin are the six compounds with the lowest binding energies (−12 to −8 kcal/mol) and show significant interactions with the target Mpro protein. The MMGBSA scores of these compounds are highly promising, and they should be investigated to determine their potential as Mpro inhibitors, beneficial for COVID-19 treatment. In this study, we highlight the crucial role of in silico technologies in the search for novel therapeutic components. Computational biology, combined with structural biology, makes drug discovery studies more rigorous and reliable, and it creates a scenario where researchers can use existing drug components to discover new roles as modulators or inhibitors for various therapeutic targets. This study demonstrated that computational analyses can yield promising findings in the search for potential drug components. This work demonstrated the significance of increasing in silico and wetlab research to generate improved structure-based medicines.

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.

SARS-CoV-2 main protease and papain-like protease inhibition by abietane-type diterpenes isolated from the branches of Glyptostrobus pensilis using molecular docking studies

Using various chromatographic methods, five abietane-type diterpenes were isolated from the branches of Glyptostrobus pensilis for the first time. The chemical structures of the isolates were identified by modern spectroscopic techniques, including ¹H and ¹³C nuclear magnetic resonance spectroscopy and by comparison with the literature. In addition, the binding potential of the isolated compounds to replicase protein, SARS-CoV-2 main protease and papain-like protease, were examined using molecular docking studies. In silico results suggested that G. pensilis as well as abietane-types diterpenes are potential candidates for the prevention and treatment of SARS-CoV-2.