Molecular docking, binding mode analysis, molecular dynamics, and prediction of ADMET/toxicity properties of selective potential antiviral agents against SARS-CoV-2 main protease: an effort toward drug repurposing to combat COVID-19

Searching possible SARS-CoV-2 main protease inhibitors in constituents from herbal medicines using in silico studies

The largest threat to civilization since the Second World War is the spread of the new coronavirus disease (COVID-19). Therefore, there is an urgent need for innovative therapeutic medicines to treat COVID-19. Reusing bio-actives is a workable and efficient strategy in the battle against new epidemics because the process of developing new drugs is time-consuming. This research aimed to identify which herbal remedies had the highest affinity for the receptor and assess a variety of them for potential targets to suppress the SARS-CoV-2 Mpro. The use of AutoDock Vina for structure-based virtual screening was done first due to the importance of protein interactions in the development of drugs. Molecular docking was used in the comparative study to assess 89 different chemicals from medicinal herbs. To anticipate their effectiveness against the primary protease of SARS-CoV-2, more analysis was done on the ADMET profile, drug-likeness, and Lipinski's rule of five. The next step involved three replicas of 100 ns-long molecular dynamics simulations on the potential candidates, which were preceded by calculations of the binding free energy of MM-GBSA. The outcomes showed that Achyrodimer A, Cinchonain Ib, Symphonone F, and Lupeol acetate all performed well and had the highest 6LU7 binding affinities. Using RMSD, RMSF, and protein-ligand interactions, the stability of the protein-ligand complex was assessed. The studies indicate that bioactive substances obtained from herbal medicines may function as a COVID-19 therapeutic agent, necessitating additional wet lab research to confirm their therapeutic potential, efficacy, and pharmacological capacity against the condition.Communicated by Ramaswamy H. Sarma.

Cheminformatics approach to identify andrographolide derivatives as dual inhibitors of methyltransferases (nsp14 and nsp16) of SARS-CoV-2

The Covid-19 pandemic outbreak has accelerated tremendous efforts to discover a therapeutic strategy that targets severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to control viral infection. Various viral proteins have been identified as potential drug targets, however, to date, no specific therapeutic cure is available against the SARS-CoV-2. To address this issue, the present work reports a systematic cheminformatic approach to identify the potent andrographolide derivatives that can target methyltransferases of SARS-CoV-2, i.e. nsp14 and nsp16 which are crucial for the replication of the virus and host immune evasion. A consensus of cheminformatics methodologies including virtual screening, molecular docking, ADMET profiling, molecular dynamics simulations, free-energy landscape analysis, molecular mechanics generalized born surface area (MM-GBSA), and density functional theory (DFT) was utilized. Our study reveals two new andrographolide derivatives (PubChem CID: 2734589 and 138968421) as natural bioactive molecules that can form stable complexes with both proteins via hydrophobic interactions, hydrogen bonds and electrostatic interactions. The toxicity analysis predicts class four toxicity for both compounds with LD50 value in the range of 500-700 mg/kg. MD simulation reveals the stable formation of the complex for both the compounds and their average trajectory values were found to be lower than the control inhibitor and protein alone. MMGBSA analysis corroborates the MD simulation result and showed the lowest energy for the compounds 2734589 and 138968421. The DFT and MEP analysis also predicts the better reactivity and stability of both the hit compounds. Overall, both andrographolide derivatives exhibit good potential as potent inhibitors for both nsp14 and nsp16 proteins, however, in-vitro and in vivo assessment would be required to prove their efficacy and safety in clinical settings. Moreover, the drug discovery strategy aiming at the dual target approach might serve as a useful model for inventing novel drug molecules for various other diseases.

In silico validation of hyaluronic acid - drug conjugates based targeted drug delivery for the treatment of COVID-19

The impact of COVID-19 urges scientists to develop targeted drug delivery to manage Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral infections with a fast recovery rate. The aim of the study is to develop Hyaluronic Acid (HA) drug conjugates of viral drugs to target two important enzymes (Mpro and PLpro) of SARS-CoV-2. Three antiviral drugs, namely Dexamethasone (DEX), Favipiravir (FAV), and Remdesivir (REM), were chosen for HA conjugation due to their reactive functional groups. Free forms of drugs (DEX, FAV, REM) and HA drug conjugates (HA-DEX, HA-FAV, HA-REM, HA-RHA, HA-RHE) were validated against Mpro (PDB ID 6LU7) and PLpro (PDB 7LLZ), which play an essential role in the replication and reproduction of the SARS-CoV-2 virus. The results of the present study revealed that HA-drug conjugates possess higher binding affinity and the best docking score towards the Mpro and PLpro target proteins of SARS-CoV-2 than their free forms of drugs. ADMET screening resulted that HA-drug conjugates exhibited better pharmacokinetic profiles than their pure forms of drugs. Further, molecular dynamic simulation studies, essential dynamics and free energy landscape analyses show that HA antiviral drug conjugates possess good trajectories and energy status, with the PLpro target protein (PDB 7LLZ) of SARS-CoV-2 through long-distance (500 ns) simulation screening. The research work recorded the best drug candidate for Cell-Targeted Drug Delivery (CTDD) for SARS-CoV-2-infected cells through hyaluronic acid conjugates of antiviral drugs.Communicated by Ramaswamy H. Sarma.

Immunomodulatory and anticytokine therapeutic potential of three Indian spices constituents and its hyaluronic acid conjugates for prevention and post COVID-19 complications: a computational modeling approach

Targeted drug delivery to SARS-CoV-2 host target proteins for preventing or blocking COVID-19 infection is making serious concern during COVID-19 pandemic and its consequent waves around the globe. People seek reliable, effective folkloric preventive medication for immediate and precautionary relief from COVID-19. These folkloric medicines were effective and saved many patients during the past COVID-19 pandemic. The current research study aims to deliver antiviral Indian spices phytocompounds and their hyaluronic acid conjugates to human host target proteins (ACE-2, TNF-α, IL-6, IL-1β, PAR-1) of SARS-CoV-2 to inhibit virus propagation and also to regulate early clinical complications of COVID-19. Targeted drug delivery of hyaluronic acid conjugated traditional natural bioactive agent produces more effective and fewer side effects in delivering novel drugs to human host proteins of COVID-19. In silico molecular docking study of six phytocompounds from three Indian spices and standard drug atazanavir and its hyaluronic acid conjugates reveals that phytocompounds and its hyaluronic acid conjugates possess high affinity to binding pockets of SARS-CoV-2 human host targets with more binding affinity scores. Most notably HA cyclocurcumin exhibit a docking score -9.9 kcal/mol against ACE-2 (PDB ID 1R42) target protein similarly HA-Hydrazinocurcumin exhibit a docking score -9.8 kcal/mol against PAR-1 (PDB ID 3VW7). ADMET validation of phytocompounds and their hyaluronic acid conjugates reveals its best pharmacokinetic profile over standard antiviral drug especially HA cyclocurcumin conjugate possesses high HIA (86%) and good pharmacokinetic profiles. DFT analysis affirms the reason behind the higher binding affinity of hyaluronic acid conjugates of spices phytocompounds towards all screened target proteins especially HA-hydrazinocurcumin conjugate possess high softness (19.1570 eV) and low hardness (0.0522 eV) values. Finally, MD simulation of best-docked compounds against ACE-2 and PAR-1 target protein revealed that hyaluronic acid conjugates of Indian spices compounds exhibit stable RMSD values and more protein-ligand interactions during simulation than hyaluronic acid conjugates of drug atazanavir.Communicated by Ramaswamy H. Sarma.

Integrated molecular and quantum mechanical approach to identify novel potent natural bioactive compound against 2'-O-methyltransferase (nsp16) of SARS-CoV-2

With the advent of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak, efforts are still in progress to find out a functional cure for the infection. Among the various protein targets, nsp16 capping protein is one of the vital targets for drug development as it protects the virus against the host cell nucleases and evading innate immunity. The nsp16 protein forms a heterodimer with a co-factor nsp10 and triggers 2'-O-methyltransferase activity which catalyzes the conversion of S-adenosyl methionine into S-adenosyl homocysteine. The free methyl group is transferred to the 2'-O position on ribose sugar at the 5' end of mRNA to form the cap-1 structure which is essential for replication of the virus and evading the innate immunity of the host. In this study, we identify a potential lead natural bioactive compound against nsp16 protein by systematic cheminformatic analysis of more than 144k natural compounds. Virtual screening, molecular docking interactions, ADMET profiling, molecular dynamics (MD) simulations, molecular mechanics-generalized born surface area (MM-GBSA), free energy analysis and density functional theory analysis were used to discover the potential lead compound. Our investigation revealed that ZINC8952607 (methyl-[(6-methyl-2,3,4,9-tetrahydro-1H-carbazol-1-yl)aminomethyl]BLAHone) has the greatest binding affinity and best pharmacokinetic parameters due to presence of carbazol and BLAHone (biaryl moiety). Further, time-dependent MD simulation analysis substantiates the stability and rigidness of nsp16 protein even after interaction with the lead compound. We believe that the compound ZINC8952607 might establish as a novel natural drug candidate against CoVID-19 infection.Communicated by Ramaswamy H. Sarma.

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Quinazolin-4-one/3-cyanopyridin-2-one Hybrids as Dual Inhibitors of EGFR and BRAFV600E: Design, Synthesis, and Antiproliferative Activity

A novel series of hybrid compounds comprising quinazolin-4-one and 3-cyanopyridin-2-one structures has been developed, with dual inhibitory actions on both EGFR and BRAFV600E. These hybrid compounds were tested in vitro against four different cancer cell lines. Compounds 8, 9, 18, and 19 inhibited cell proliferation significantly in the four cancer cells, with GI50 values ranging from 1.20 to 1.80 µM when compared to Doxorubicin (GI50 = 1.10 µM). Within this group of hybrids, compounds 18 and 19 exhibited substantial inhibition of EGFR and BRAFV600E. Molecular docking investigations provided confirmation that compounds 18 and 19 possess the capability to inhibit EGFR and BRAFV600E. Moreover, computational ADMET prediction indicated that most of the newly synthesized hybrids have low toxicity and minimal side effects.

Repurposing the in-house generated Alzheimer's disease targeting molecules through computational and preliminary in-vitro studies for the management of SARS-coronavirus-2

Covid-19 was declared a world pandemic. Recent studies demonstrated that Covid-19 impairs CNS activity by crossing the blood-brain barrier and ensuing cognitive impairment. In this study, we have utilized Covid-19 main protease (Mpro) as a biological target to repurpose our previously reported multifunctional compounds targeting Alzheimer's disease. Molecular docking, spatial orientation, molecular dynamics simulation, MM-GBSA energy calculation, and DFT studies were carried out with these molecules. Among all the compounds, F27, F44, and F56 exhibited higher binding energy (- 8.03, - 8.65, and - 8.68 kcal/mol, respectively) over the co-crystal ligand O6K (- 7.00 kcal/mol). In MD simulation, compounds F27, F44, and F56 could make a stable complex with Mpro target throughout the simulation. The compounds were synthesized following reported methods and subjected for cytotoxicity, and assessment of their capability to cross the blood-brain barrier in PAMPA assay, and antioxidant property evaluation through DPPH assay. The compounds F27, F44, and F56 exhibited cytocompatibility with the SiHA cell line and also displayed significant antioxidant properties with IC50 = 45.80 ± 0.27 μM, 44.42 ± 0.30 μM, and 42.74 ± 0.23 μM respectively. In the PAMPA assays, the permeability coefficient (Pe) value of F27, F44, and F56 lies in the acceptable range (Pe > 4). The results of the computational and preliminary in-vitro studies strongly corroborate the potential of F27, F44, and F56 as a lead for further optimization in treating the CNS complications associated with Covid-19.

Design, Synthesis, and Biological Evaluation of Novel 3-Cyanopyridone/Pyrazoline Hybrids as Potential Apoptotic Antiproliferative Agents Targeting EGFR/BRAFV600E Inhibitory Pathways

A series of novel 3-cyanopyridone/pyrazoline hybrids (21-30) exhibiting dual inhibition against EGFR and BRAFV600E has been developed. The synthesized target compounds were tested in vitro against four cancer cell lines. Compounds 28 and 30 demonstrated remarkable antiproliferative activity, boasting GI50 values of 27 nM and 25 nM, respectively. These hybrids exhibited dual inhibitory effects on both EGFR and BRAFV600E pathways. Compounds 28 and 30, akin to Erlotinib, displayed promising anticancer potential. Compound 30 emerged as the most potent inhibitor against cancer cell proliferation and BRAFV600E. Notably, both compounds 28 and 30 induced apoptosis by elevating levels of caspase-3 and -8 and Bax, while downregulating the antiapoptotic Bcl2 protein. Molecular docking studies confirmed the potential of compounds 28 and 30 to act as dual EGFR/BRAFV600E inhibitors. Furthermore, in silico ADMET prediction indicated that most synthesized 3-cyanopyridone/pyrazoline hybrids exhibit low toxicity and minimal adverse effects.

Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices

The continuous approval of covalent drugs in recent years for the treatment of diseases has led to an increased search for covalent agents by medicinal chemists and computational scientists worldwide. In the computational parlance, molecular docking which is a popular tool to investigate the interaction of a ligand and a protein target, does not account for the formation of covalent bond, and the increasing application of these conventional programs to covalent targets in early drug discovery practice is a matter of utmost concern. Thus, in this comprehensive review, we sought to educate the docking community about the realization of covalent docking and the existence of suitable programs to make their future virtual-screening events on covalent targets worthwhile and scientifically rational. More interestingly, we went beyond the classical description of the functionality of covalent-docking programs down to selecting the 'best' program to consult with during a virtual-screening campaign based on receptor class and covalent warhead chemistry. In addition, we made a highlight on how covalent docking could be achieved using random conventional docking software. And lastly, we raised an alert on the growing erroneous molecular docking practices with covalent targets. Our aim is to guide scientists in the rational docking pursuit when dealing with covalent targets, as this will reduce false-positive results and also increase the reliability of their work for translational research.

Design, Synthesis, Antiproliferative Actions, and DFT Studies of New Bis-Pyrazoline Derivatives as Dual EGFR/BRAFV600E Inhibitors

Some new Bis-pyrazoline hybrids 8-17 with dual EGFR and BRAFV600E inhibitors have been developed. The target compounds were synthesized and tested in vitro against four cancer cell lines. Compounds 12, 15, and 17 demonstrated strong antiproliferative activity with GI50 values of 1.05 µM, 1.50 µM, and 1.20 µM, respectively. Hybrids showed dual inhibition of EGFR and BRAFV600E. Compounds 12, 15, and 17 inhibited EGFR-like erlotinib and exhibited promising anticancer activity. Compound 12 is the most potent inhibitor of cancer cell proliferation and BRAFV600E. Compounds 12 and 17 induced apoptosis by increasing caspase 3, 8, and Bax levels, and resulted in the downregulation of the antiapoptotic Bcl2. The molecular docking studies verified that compounds 12, 15, and 17 have the potential to be dual EGFR/BRAFV600E inhibitors. Additionally, in silico ADMET prediction revealed that most synthesized bis-pyrazoline hybrids have low toxicity and adverse effects. DFT studies for the two most active compounds, 12 and 15, were also carried out. The values of the HOMO and LUMO energies, as well as softness and hardness, were computationally investigated using the DFT method. These findings agreed well with those of the in vitro research and molecular docking study.

Broad-Spectrum Antivirals Derived from Natural Products

Scientific advances have led to the development and production of numerous vaccines and antiviral drugs, but viruses, including re-emerging and emerging viruses, such as SARS-CoV-2, remain a major threat to human health. Many antiviral agents are rarely used in clinical treatment, however, because of their inefficacy and resistance. The toxicity of natural products may be lower, and some natural products have multiple targets, which means less resistance. Therefore, natural products may be an effective means to solve virus infection in the future. New techniques and ideas are currently being developed for the design and screening of antiviral drugs thanks to recent revelations about virus replication mechanisms and the advancement of molecular docking technology. This review will summarize recently discovered antiviral drugs, mechanisms of action, and screening and design strategies for novel antiviral agents.

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.

Pan-Genomics of Escherichia albertii for Antibiotic Resistance Profiling in Different Genome Fractions and Natural Product Mediated Intervention: In Silico Approach

Escherichia albertii is an emerging, enteric pathogen of significance. It was first isolated in 2003 from a pediatric diarrheal sample from Bangladesh. In this study, a comprehensive in silico strategy was followed to first list out antibiotic-resistant genes from core, accessory and unique genome fractions of 95 available genomes of E. albertii. Then, 56 drug targets were identified from the core essential genome. Finally, ZipA, an essential cell division protein that stabilizes the FtsZ protofilaments by cross-linking them and serves as a cytoplasmic membrane anchor for the Z ring, was selected for further downstream processing. It was computationally modeled using a threading approach, followed by virtual screening of two phytochemical libraries, Ayurvedic (n = 2103 compounds) and Traditional Chinese Medicine (n = 36,043 compounds). ADMET profiling, followed by PBPK modeling in the central body compartment, in a population of 250 non-diseased, 250 cirrhotic and 250 renally impaired people was attempted. ZINC85624912 from Chinese medicinal library showed the highest bioavailability and plasma retention. This is the first attempt to simulate the fate of natural products in the body through PBPK. Dynamics simulation of 20 ns for the top three compounds from both libraries was also performed to validate the stability of the compounds. The obtained information from the current study could aid wet-lab scientists to work on the scaffold of screened drug-like compounds from natural resources and could be useful in our quest for therapy against antibiotic-resistant E. albertii.

QSAR, simulation techniques, and ADMET/pharmacokinetics assessment of a set of compounds that target MAO-B as anti-Alzheimer agent

Background

Alzheimer’s disease (AD), the most common cause of dementia in the elderly, is a progressive neurodegenerative disorder that gradually affects cognitive function and eventually causes death. Most approved drugs can only treat the disease alleviating the disease symptoms; therefore, there is a need to develop drugs that can treat this illness holistically. The medical community is searching for new drugs and new drug targets to cure this disease. In this study, QSAR, molecular docking evaluation, and ADMET/pharmacokinetics assessment were used as modeling methods to identify the compounds with outstanding physicochemical properties.

Results

The 37 MAO-B compounds were screened using the aforementioned methods and yielded a model with the following molecular properties: AATS1v, AATS3v, GATS4m, and GATS6e. Good statistical values were R2train = 0.69, R2adj = 0.63, R2pred = 0.57, LOF = 0.23, and RMSE = 0.38. The model was validated using an evaluation set that confirmed its robustness. The molecular docking was also utilized using crystal structure of human monoamine oxidase B in complex with chlorophenylchromone-carboxamide with ID code of 6FW0, and three compounds were identified with outstanding high binding affinity (13 = − 30.51 kcal mol−1, 31 = − 31.85 kcal mol−1, and 33 = − 33.70 kcal mol−1), and better than the Eldepryl (referenced) drug (− 11.40 kcal mol−1).

Conclusions

These three compounds (13, 31, and 33) were analyzed for ADMET/pharmacokinetics evaluation and found worthy of further analysis as promising drug candidates to cure AD and could also serve as a template to design several monoamine oxidase B inhibitors in the future to cure AD.

Molecular docking and identification of G-protein-coupled receptor 120 (GPR120) agonists as SARS COVID-19 MPro inhibitors

COVID-19 has become a pandemic, and any new drug for treating the disease could save millions of lives. Several drugs already in use for other diseases and medical conditions are repurposed for treating COVID-19 in an attempt to find treatment for the disease without spending research time on ADME TOX and other studies on side effects. In this exercise, the drugs repurposed are from antiviral, antibiotics, antiviral for HIV and HCV, anti-cancer, natural medicines, etc. Possible repurposing anti-diabetic GPR-120 agonists used as for SAR-CoV-2 is attempted in the study by carrying out docking of 68 GPR-120 agonists. Ten of these compounds were found to have docking scores −8.3 to −8.0, and the best docking score was observed for an arylsulfonamide and a biarylpropanoic acid belonging to GPR120 agonists previously evaluated for the treatment of type II diabetes. These GPR120 agonists could serve as start point for novel inhibitors for the discovery of drugs to treat COVID-19.

Evaluation of apigenin-based biflavonoid derivatives as potential therapeutic agents against viral protease (3CLpro) of SARS-CoV-2 via molecular docking, molecular dynamics and quantum mechanics studies

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the causative agent of the pandemic COVID-19 disease that affects human respiratory function. Despite the scientific progression made in the development of the vaccine, there is an urgent need for the discovery of antiviral drugs for better performance at different stages of SARS-CoV-2 reproduction. The main protease (Mpro or 3CLpro) plays a pivotal role in the life cycle of the virus, making it an attractive target for the development of antiviral agents effective against the new strains of coronaviruses (CoVs). In this study, a series of apigenin-based natural biflavonoid derivatives as potential inhibitors of coronaviruses 3CLpro was investigated by in silico approaches. For this purpose, the molecular docking was performed to analyze the interaction of the natural biflavonoids with SARS-Cov-2 main protease and for further investigation, docking to the 3CLpro of SARS-CoV and MERS-CoV. Based on docking scores and comparison with the reference inhibitors (ritonavir and lopinavir), more than half of the biflavonoids had strong interactions with the residues of the binding pocket of the coronaviruses 3CLpro and exhibited better binding affinities toward the main protease than ritonavir and lopinavir. The top biflavonoids were further explored through molecular dynamics simulation, binding free energy calculation and residual energy contributions estimated by the MM-PBSA. Also, drug likeness property investigation by Swiss ADME tools and density functional theory (DFT) calculations were performed. The results confirmed that the 3CLpro-amentoflavone, 3CLpro-bilobetin, 3CLpro-ginkgetin, and 3CLpro-sotetsuflavone complexes possess a large amount of dynamic properties such as high stability, significant binding energy and fewer conformation fluctuations. Also, the pharmacokinetics and drug-likeness studies and HOMO-LUMO and DFT descriptor values indicated a promising result of the selected natural biflavonoids. Overall findings indicate that the apigenin-based biflavonoids may inhibit COVID-19 by significant interactions in the binding pocket and those results can pave the way in drug discovery although the effectiveness of these bioactive compounds should be further validated by in-vitro and in-vivo investigations. Communicated by Ramaswamy H. Sarma.

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.

Molecular interactions and inhibition of the SARS‐CoV‐2 main protease by a thiadiazolidinone derivative

We report molecular interactions and inhibition of the main protease (M^Pro) of SARS‐CoV‐2, a key enzyme involved in the viral life cycle. By using a thiadiazolidinone (TDZD) derivative as a chemical probe, we explore the conformational dynamics of M^Pro via docking protocols and molecular dynamics simulations in all‐atom detail. We reveal the local and global dynamics of M^Pro in the presence of this inhibitor and confirm the inhibition of the enzyme with an IC₅₀ value of 1.39 ± 0.22 μM, which is comparable to other known inhibitors of this enzyme.

Heme Protein Binding of Sulfonamide Compounds: A Correlation Study by Spectroscopic, Calorimetric, and Computational Methods

Protein-ligand interaction studies are useful to determine the molecular mechanism of the binding phenomenon, leading to the establishment of the structure-function relationship. Here, we report the binding of well-known antibiotic sulfonamide drugs (sulfamethazine, SMZ; and sulfadiazine, SDZ) with heme protein myoglobin (Mb) using spectroscopic, calorimetric, ζ potential, and computational methods. Formation of a 1:1 complex between the ligand and Mb through well-defined equilibrium was observed. The binding constants obtained between Mb and SMZ/SDZ drugs were on the order of 10⁴ M^-1. SMZ with two additional methyl (-CH₃) substitutions has higher affinity than SDZ. Upon drug binding, a notable loss in the helicity (via circular dichroism) and perturbation of the three-dimensional (3D) protein structure (via infrared and synchronous fluorescence experiments) were observed. The binding also indicated the dominance of non-polyelectrolytic forces between the amino acid residues of the protein and the drugs. The ligand-protein binding distance signified high probability of energy transfer between them. Destabilization of the protein structure upon binding was evident from differential scanning calorimetry results and ζ potential analyses. Molecular docking presented the best probable binding sites of the drugs inside protein pockets. Thus, the present study explores the potential binding characteristics of two sulfonamide drugs (with different substitutions) with myoglobin, correlating the structural and energetic aspects.

Nrf2 activator PB125® as a carnosic acid-based therapeutic agent against respiratory viral diseases, including COVID-19

PB125® is a phytochemical composition providing potent Nrf2 activation as well as a number of direct actions that do not involve Nrf2. Nrf2 is a transcription actor that helps maintain metabolic balance by providing redox-sensitive expression of numerous genes controlling normal day-to-day metabolic pathways. When ordinary metabolism is upset by extraordinary events such as injury, pathogenic infection, air or water pollution, ingestion of toxins, or simply by the slow but incessant changes brought about by aging and genetic variations, Nrf2 may also be called into action by the redox changes resulting from these events, whether acute or chronic. A complicating factor in all of this is that Nrf2 levels decline with aging, leaving the elderly less able to maintain proper redox balance. The dysregulated gene expression that results can cause or exacerbate a wide variety of pathological conditions, including susceptibility to viral infections. This review examines the characteristics desirable in Nrf2 activators that have therapeutic potential, as well as some of the patterns of dysregulated gene expression commonly observed during pulmonary infections and the normalizing effects possible by judicious use of phytochemicals to increase the activation level of available Nrf2.

Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions

Identifying drug-target protein association pairs is a prerequisite and a crucial task in drug discovery and development. Numerous computational models, based on different assumptions and algorithms, have been proposed as an alternative to the laborious, costly, and time-consuming traditional wet-lab methods. Most proposed methods focus on separated drug and target descriptors, calculated, respectively, from chemical structures and protein sequences, and fail to introduce and extract features where the interaction information is embedded. In this paper, we propose a new three-step method based on matrix factorisation and generative adversarial network (GAN) for drug-target interaction prediction. Firstly, the matrix factorisation technique is used to capture and extract the joint interaction feature, for both drugs and targets, from the drug-target interaction matrix. Then, a GAN is introduced for data augmentation. It generates a fake positive sample similar to the real positive sample (known interactions) in order to balance the samples, allow the exploitation of the entire negative sample, and increase the data size for an accurate prediction. Finally, a fully connected four-layer neural network is built for classification. Experimental results illustrate a higher prediction performance of the proposed method compared to shallow classifiers and to state-of-the-art methods with an accuracy higher than 97%. Moreover, the data generation effect is confirmed by evaluating the proposed method with and without the generation step. These results demonstrated the efficiency of the latent interaction features and data generation on predicting new drugs or repurposing existing drugs. Overview of the WGANMF-DTI workflow for the Drug-Target Interaction Prediction task.

Can Antiviral Activity of Licorice Help Fight COVID-19 Infection?

The phytotherapeutic properties of Glycyrrhiza glabra (licorice) extract are mainly attributed to glycyrrhizin (GR) and glycyrrhetinic acid (GA). Among their possible pharmacological actions, the ability to act against viruses belonging to different families, including SARS coronavirus, is particularly important. With the COVID-19 emergency and the urgent need for compounds to counteract the pandemic, the antiviral properties of GR and GA, as pure substances or as components of licorice extract, attracted attention in the last year and supported the launch of two clinical trials. In silico docking studies reported that GR and GA may directly interact with the key players in viral internalization and replication such as angiotensin-converting enzyme 2 (ACE2), spike protein, the host transmembrane serine protease 2, and 3-chymotrypsin-like cysteine protease. In vitro data indicated that GR can interfere with virus entry by directly interacting with ACE2 and spike, with a nonspecific effect on cell and viral membranes. Additional anti-inflammatory and antioxidant effects of GR cannot be excluded. These multiple activities of GR and licorice extract are critically re-assessed in this review, and their possible role against the spread of the SARS-CoV-2 and the features of COVID-19 disease is discussed.