Combination of QSAR, molecular docking, molecular dynamic simulation and MM-PBSA: analogues of lopinavir and favipiravir as potential drug candidates against COVID-19

In silico and in vitro evaluation of designed fluconazole analogues as lanosterol 14α-demethylase inhibitors

The drugs fighting against aggressive fungal infections are in limited number, therefore, extensive research is obligatory to develop new therapeutic strategies. Fluconazole (FLZ) is a clinically approved drug, but resistant drug against most fungal pathogens, thus it is vital to identify more compounds that can better check the fungal growth. Analogue-based drug designing is a quick and economical way since it has inherent drug-like properties of marketed drugs. This study aims to generate and evaluate analogues of FLZ with better potency against fungal-borne infections. A total of 3307 analogues of FLZ were developed from six scaffold structures. Only 390 compounds passed Lipinski's rule, of which 247 analogues exhibited lower docking scores than FLZ with 5FSA. These inhibitors were further subjected to pharmacokinetics property evaluation and cytotoxicity test and it was found that only 46 analogues were suitable for further evaluation. Based on the molecular docking score of the best two analogues, 6f (-12.7 kcal/mol) and 8f (-12.8 kcal/mol) were selected for molecular dynamics and in-vitro studies. Antifungal activities of both compounds against 4 strains of Candida albicans were evaluated by disc diffusion assay and micro broth dilution assay and Minimum inhibitory concentrations (MICs) for 6f and 8f were observed as 256 µg/ml against 4719, 4918 and 5480 strains but the MIC was extended to 512 µg/ml for strain 3719. Both analogues exhibited low antifungal activities as compared to FLZ (8-16 µg/ml). The interaction of 6f with Mycostatin was also performed using a chequerboard assay that was found additive.Communicated by Ramaswamy H. Sarma.

Hydroxamic acid derivatives as selective HDAC3 inhibitors: computer-aided drug design strategies

Histone deacetylases (HDACs) are critical epigenetic drug targets that have gained significant attention in the scientific community for the treatment of cancer. The currently marketed HDAC inhibitors lack selectivity for the various HDAC isoenzymes. Here, we describe our protocol for the discovery of novel potential hydroxamic acid based HDAC3 inhibitors through pharmacophore modeling, virtual screening, docking, molecular dynamics (MD) simulation and toxicity studies. The ten pharmacophore hypotheses were established, and their reliability was validated by different ROC (receiving operator curve) analysis. Among them, the best model (Hypothesis 9 or RRRA) was employed for searching SCHEMBL, ZINC and MolPort database to screen out hit molecules as selective HDAC3 inhibitors, followed by different docking stages. MD simulation (50 ns) and MMGBSA study were performed to study the stability of ligand binding modes and with the help of trajectory analysis, to calculate the ligand-receptor complex RMSD (root-mean-square deviation), RMSF (root-mean-square fluctuation) and H-bond distance, etc. Finally, in-silico toxicity studies were performed on top screened molecules and compared with reference drug SAHA and established structure-activity relationship (SAR). The results indicated that compound 31, with high inhibitory potency and less toxicity (probability value 0.418), is suitable for further experimental analysis.Communicated by Ramaswamy H. Sarma.

Screening of Multitarget-Directed Natural Compounds as Drug Candidates for Alzheimer's Disease Using In Silico Techniques: Their Extraction and In Vitro Validation

Alzheimer's disease (AD) is a neurodegenerative disorder that impairs neurocognitive function. Acetylcholinesterase (AChE) and β-site APP cleaving enzyme 1 (BACE1) are the two main proteins implicated in AD. Indeed, the major available commercial drugs (donepezil, rivastigmine, and galantamine) against Alzheimer's are AChE inhibitors. However, none of these drugs are known to reverse or reduce the pathophysiological condition of the disease since there are multiple contributing factors to AD. Therefore, there is a need to develop a multitarget-directed ligand approach for its treatment. In the present study, plant bioactive compounds were screened for their AChE and BACE1 inhibition potential by conducting molecular docking studies. Considering their docking score and pharmacokinetic properties, limonin, peimisine, serratanine B, and withanolide A were selected as the lead compounds. Molecular dynamics simulations of these protein-ligand complexes confirmed the conformational and energetically stabilized enzyme-inhibitor complexes. The inhibition potential of the lead compounds was validated by in vitro enzyme assay. Withanolide A inhibited AChE (IC50 value of 107 μM) and showed mixed-type inhibition. At this concentration, it inhibited BACE1 activity by 57.10% and was stated as most effective. Both the compounds, as well as their crude extracts, were found to have no cytotoxic effect on the SH-SY5Y cell line.

The design of compounds with desirable properties - The anti-HIV case study

Efficacy and safety are among the most desirable characteristics of an ideal drug. The tremendous increase in computing power and the entry of artificial intelligence into the field of computational drug design are accelerating the process of identifying, developing, and optimizing potential drugs. Here, we present novel approach to design new molecules with desired properties. We combined various neural networks and linear regression algorithms to build models for cytotoxicity and anti-HIV activity based on Continual Molecular Interior analysis (CoMIn) and Cinderella's Shoe (CiS) derived molecular descriptors. After validating the reliability of the models, a genetic algorithm was coupled with the Des-Pot Grid algorithm to generate new molecules from a predefined pool of molecular fragments and predict their bioactivity and cytotoxicity. This combination led to the proposal of 16 hit molecules with high anti-HIV activity and low cytotoxicity. The anti-SARS-CoV-2 activity of the hits was predicted.

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.

Identification of novel potential inhibitor of thymidylate kinase from Variola virus

A hit compound was designed using Fragment Based Drug Designing (FBDD) approach, density functional theory (DFT) calculations were performed to find the structural and electronic properties. Additionally, pharmacokinetic properties were studied to understand the biological response of the compound. Docking studies were carried out with the protein structure of VrTMPK and HssTMPK with the reported hit compound. The favored docked complex was further carried to perform MD simulations; the RMSD plot and H-bond analysis was done for 200 ns. Also, MM-PBSA was done to understand the binding energy constituents and stability of the complex. A comparative study of the designed hit compound was done with FDA approved Tecovirimat. As a result, it was found that the reported compound (POX-A)is a potential selective inhibitor for Variola virus. Hence, it can be used to study further in vivo and in vitro behavior of the compound.Communicated by Ramaswamy H. Sarma.

Identification of novel inhibitors against hantaviruses through 2D fingerprinting and molecular modeling approaches

With the immensely growing outbreaks of hantavirus with still no effective treatment available, there is an urgent need of exploring new computational approaches which will target potential virulent proteins that will eventually reduce its growth. In this study, an envelope glycoprotein, Gn, was targeted. The glycoproteins, which are the sole targets of neutralizing antibodies, drive virus entry via receptor-mediated endocytosis and endosomal membrane fusion. Inhibitors are herein proposed to negate its action mechanism. On the basis of the scaffolds of favipiravir, a FDA compound already used against hantavirus, a library was designed using a 2D fingerprinting approach. Upon molecular docking analysis, the top four docked compounds-(1) favipiravir (-4.5 kcal/mol), (2) N-hydroxy-3-oxo-3, 4-dihydropyrazine-2-carboxamide (-4.7 kcal/mol), (3) N, 5, 6-trimethyl-2-oxo-1H-pyrazine-3-carboxamide (-4.5 kcal/mol), and (4) 3-propyl-1H-pyrazin-2-one (-3.8)-were prioritized on the basis of the lowest binding energies score. Through molecular docking, the best-categorized compound was subjected to molecular dynamics simulation for a 100-ns time span. Molecular dynamics sheds light on each ligand behavior within the active site. Among the four complexes, only favipiravir and 6320122 compound were found to be stable inside the pocket. This is due to the presence of common rings, pyrazine and carboxamide ring, which make a significant interaction with active key residues Furthermore, the MMPB/GBSA binding free energy analysis calculated for all complexes supported the dynamics results by calculating the most stable values for favipiravir complex (-9.9933 and -8.6951 kcal/mol) and for 6320122 compound complex (-13.8675 and -9.3439 kcal/mol), which demonstrated that the selected compounds have a proper binding affinity with the targeted proteins. The hydrogen bond analysis similarly revealed a strong bonding interaction. The results yielded a strong interaction between the enzyme and the inhibitor throughout the simulation; thus, the inhibitor has the potential to become a lead compound and could be subjected to experimental evaluation to unveil their blockage ability.

In silico protein engineering shows that novel mutations affecting NAD+ binding sites may improve phosphite dehydrogenase stability and activity

Pseudomonas stutzeri phosphite dehydrogenase (PTDH) catalyzes the oxidation of phosphite to phosphate in the presence of NAD, resulting in the formation of NADH. The regeneration of NADH by PTDH is greater than any other enzyme due to the substantial change in the free energy of reaction (G°' = - 63.3 kJ/mol). Presently, improving the stability of PTDH is for a great importance to ensure an economically viable reaction process to produce phosphite as a byproduct for agronomic applications. The binding site of NAD⁺ with PTDH includes thirty-four residues; eight of which have been previously mutated and characterized for their roles in catalysis. In the present study, the unexplored twenty-six key residues involved in the binding of NAD⁺ were subjected to in silico mutagenesis based on the physicochemical properties of the amino acids. The effects of these mutations on the structure, stability, activity, and interaction of PTDH with NAD⁺ were investigated using molecular docking, molecular dynamics simulations, free energy calculations, and secondary structure analysis. We identified seven novel mutations, A155I, G157I, L217I, P235A, V262I, I293A, and I293L, that reduce the compactness of the protein while improving PTDH stability and binding to NAD⁺.

3D-QSAR Studies, Molecular Docking, Molecular Dynamic Simulation, and ADMET Proprieties of Novel Pteridinone Derivatives as PLK1 Inhibitors for the Treatment of Prostate Cancer

Overexpression of polo-like kinase 1 (PLK1) has been found in many different types of cancers. With its essential role in cell proliferation, PLK1 has been determined to be a broad-spectrum anti-cancer target. In this study, 3D-QSAR, molecular docking, and molecular dynamics (MD) simulations were applied on a series of novel pteridinone derivatives as PLK1 inhibitors to discover anti-cancer drug candidates. In this work, three models—CoMFA (Q² = 0.67, R² = 0.992), CoMSIA/SHE (Q² = 0.69, R² = 0.974), and CoMSIA/SEAH (Q² = 0.66, R² = 0.975)—of pteridinone derivatives were established. The three models that were established gave Rpred2 = 0.683, Rpred 2= 0.758, and Rpred 2= 0.767, respectively. Thus, the predictive abilities of the three proposed models were successfully evaluated. The relations between the different champs and activities were well-demonstrated by the contour chart of the CoMFA and CoMSIA/SEAH models. The results of molecular docking indicated that residues R136, R57, Y133, L69, L82, and Y139 were the active sites of the PLK1 protein (PDB code: 2RKU), in which the more active ligands can inhibit the enzyme of PLK1. The results of the molecular dynamic MD simulation diagram were obtained to reinforce the previous molecular docking results, which showed that both inhibitors remained stable in the active sites of the PLK1 protein (PDB code: 2RKU) for 50 ns. Finally, a check of the ADME-Tox properties of the two most active molecules showed that molecular N° 28 could represent a good drug candidate for the therapy of prostate cancer diseases.

A subunit vaccine against pneumonia: targeting Streptococcus pneumoniae and Klebsiella pneumoniae

Community-acquired pneumonia is primarily caused by Streptococcus pneumoniae and Klebsiella pneumoniae, two pathogens that have high morbidity and mortality rates. This is largely due to bacterial resistance development against current antibiotics and the lack of effective vaccines. The objective of this work was to develop an immunogenic multi-epitope subunit vaccine capable of eliciting a robust immune response against S. pneumoniae and K. pneumoniae. The targeted proteins were the pneumococcal surface proteins (PspA and PspC) and choline-binding protein (CbpA) of S. pneumoniae and the outer membrane proteins (OmpA and OmpW) of K. pneumoniae. Different computational approaches and various immune filters were employed for designing a vaccine. The immunogenicity and safety of the vaccine were evaluated by utilizing many physicochemical and antigenic profiles. To improve structural stability, disulfide engineering was applied to a portion of the vaccine structure with high mobility. Molecular docking was performed to examine the binding affinities and biological interactions at the atomic level between the vaccine and Toll-like receptors (TLR2 and 4). Further, the dynamic stabilities of the vaccine and TLRs complexes were investigated by molecular dynamics simulations. While the immune response induction capability of the vaccine was assessed by the immune simulation study. Vaccine translation and expression efficiency was determined through an in silico cloning experiment utilizing the pET28a(+) plasmid vector. The obtained results revealed that the designed vaccine is structurally stable and able to generate an effective immune response to combat pneumococcal infection.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13721-023-00416-3.

Recognizing novel drugs against Keap1 in Alzheimer's disease using machine learning grounded computational studies

Alzheimer's disease (AD) is the most common neurodegenerative disorder in the world, affecting an estimated 50 million individuals. The nerve cells become impaired and die due to the formation of amyloid-beta (Aβ) plaques and neurofibrillary tangles (NFTs). Dementia is one of the most common symptoms seen in people with AD. Genes, lifestyle, mitochondrial dysfunction, oxidative stress, obesity, infections, and head injuries are some of the factors that can contribute to the development and progression of AD. There are just a few FDA-approved treatments without side effects in the market, and their efficacy is restricted due to their narrow target in the etiology of AD. Therefore, our aim is to identify a safe and potent treatment for Alzheimer's disease. We chose the ursolic acid (UA) and its similar compounds as a compounds' library. And the ChEMBL database was adopted to obtain the active and inactive chemicals against Keap1. The best Quantitative structure-activity relationship (QSAR) model was created by evaluating standard machine learning techniques, and the best model has the lowest RMSE and greatest R2 (Random Forest Regressor). We chose pIC50 of 6.5 as threshold, where the top five potent medicines (DB06841, DB04310, DB11784, DB12730, and DB12677) with the highest predicted pIC50 (7.091184, 6.900866, 6.800155, 6.768965, and 6.756439) based on QSAR analysis. Furthermore, the top five medicines utilize as ligand molecules were docked in Keap1's binding region. The structural stability of the nominated medications was then evaluated using molecular dynamics simulations, RMSD, RMSF, Rg, and hydrogen bonding. All models are stable at 20 ns during simulation, with no major fluctuations observed. Finally, the top five medications are shown as prospective inhibitors of Keap1 and are the most promising to battle AD.

An understanding of coronavirus and exploring the molecular dynamics simulations to find promising candidates against the Mpro of nCoV to combat the COVID-19: A systematic review

The first infection case of new coronavirus was reported at the end of 2019 and after then, the cases are reported in all nations across the world in a very short period. Further, the regular news of mutations in the virus has made life restricted with appropriate behavior. To date, a new strain (Omicron and its new subvariant Omicron XE) has brought fear amongst us due to a higher trajectory of increase in the number of cases. The researchers thus started giving attention to this viral infection and discovering drug-like candidates to cure the infections. Finding a drug for any viral infection is not an easy task and takes plenty of time. Therefore, computational chemistry/bioinformatics is followed to get promising molecules against viral infection. Molecular dynamics (MD) simulations are being explored to get drug candidates in a short period. The molecules are screened via molecular docking, which provides preliminary information which can be further verified by molecular dynamics (MD) simulations. To understand the change in structure, MD simulations generated several trajectories such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bonding, and radius of gyration for the main protease (Mpro) of the new coronavirus (nCoV) in the presence of small molecules. Additionally, change in free energy for the formation of complex of Mpro of nCoV with the small molecule can be determined by applying molecular mechanics with generalized born and surface area solvation (MM-GBSA). Thus, the promising molecules can be further explored for clinical trials to combat coronavirus disease-19 (COVID-19).

Identification of Drug Combination Therapies for SARS-CoV-2: A Molecular Dynamics Simulations Approach

Purpose

The development of effective treatments for coronavirus infectious disease 19 (COVID-19) caused by SARS-Coronavirus-2 was hindered by the little data available about this virus at the start of the pandemic. Drug repurposing provides a good strategy to explore approved drugs' possible SARS-CoV-2 antiviral activity. Moreover, drug synergism is essential in antiviral treatment due to improved efficacy and reduced toxicity. In this work, we studied the effect of approved and investigational drugs on one of SARS-CoV-2 essential proteins, the main protease (Mpro), in search of antiviral treatments and/or drug combinations.

Methods

Different possible druggable sites of Mpro were identified and screened against an in-house library of more than 4000 chemical compounds. Molecular dynamics simulations were carried out to explore conformational changes induced by different ligands' binding. Subsequently, the inhibitory effect of the identified compounds and the suggested drug combinations on the Mpro were established using a 3CL protease (SARS-CoV-2) assay kit.

Results

Three potential inhibitors in three different binding sites were identified; favipiravir, cefixime, and carvedilol. Molecular dynamics simulations predicted the synergistic effect of two drug combinations: favipiravir/cefixime, and favipiravir/carvedilol. The in vitro inhibitory effect of the predicted drug combinations was established on this enzyme.

Conclusion

In this work, we could study one of the promising SARS-CoV-2 viral protein targets in searching for treatments for COVID-19. The inhibitory effect of several drugs on Mpro was established in silico and in vitro assays. Molecular dynamics simulations showed promising results in predicting the synergistic effect of drug combinations.

Effects of the structure of lipid-based agents in their complexation with a single stranded mRNA fragment: a computational study

In this work we employed fully atomistic molecular dynamics simulations, aiming towards a better understanding of the mechanisms associated with the formation and the stability of lipid-based RNA nanoassemblies, in an aqueous environment. We examined two groups of lipid-based complexation agents, differing in the degree of hydrophobicity and in the overall charge. The first group was comprised of cationic ionizable agents while the second included electrically neutral amphoteric phosphatidylcholine lipids. It was found that the overall charge of the complexation agents played the most decisive role in the energetics of the lipid/RNA association, while their degree of hydrophobicity affected their self-assembly and their complexation kinetics. The latter also affected the structural stability of the formed complexes since the water entrapped within the clusters of the less hydrophobic agents appeared to reduce the coherence of the lipid-RNA nanoassemblies. The combined effects of the aforementioned attributes dictated also the RNA conformation after complexation. The results from the present study provide thus new insight towards controlling the morphology, the energetic stability and the structural integrity of the formed complexes.

In-silico screening and in-vitro assay show the antiviral effect of Indomethacin against SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worldwide spread of coronavirus disease 19 (COVID-19), and till now, it has caused death to more than 6.2 million people. Although various vaccines and drug candidates are being tested globally with limited to moderate success, a comprehensive therapeutic cure is yet to be achieved. In this study, we applied computational drug repurposing methods complemented with the analyses of the already existing gene expression data to find better therapeutics in treatment and recovery. Primarily, we identified the most crucial proteins of SARS-CoV-2 and host human cells responsible for viral infection and host response. An in-silico screening of the existing drugs was performed against the crucial proteins for SARS-CoV-2 infection, and a few existing drugs were shortlisted. Further, we analyzed the gene expression data of SARS-CoV-2 in human lung epithelial cells and investigated the molecules that can reverse the cellular mRNA expression profiles in the diseased state. LINCS L1000 and Comparative Toxicogenomics Database (CTD) were utilized to obtain two sets of compounds that can be used to counter SARS-CoV-2 infection from the gene expression perspective. Indomethacin, a nonsteroidal anti-inflammatory drug (NSAID), and Vitamin-A were found in two sets of compounds, and in the in-silico screening of existing drugs to treat SARS-CoV-2. Our in-silico findings on Indomethacin were further successfully validated by in-vitro testing in Vero CCL-81 cells with an IC₅₀ of 12 μM. Along with these findings, we briefly discuss the possible roles of Indomethacin and Vitamin-A to counter the SARS-CoV-2 infection in humans.

Molecular Dynamics Simulations of Essential Oil Ingredients Associated with Hyperbranched Polymer Drug Carriers

Our work concerns the study of four candidate drug compounds of the terpenoid family, found as essential oil ingredients in species of the Greek endemic flora, namely carvacrol, p-cymene, γ-terpinene, and thymol, via the simulation method of molecular dynamics. Aquatic solutions of each compound, as well as a solution of all four together in realistic (experimental) proportions, are simulated at atmospheric pressure and 37 °C using an OPLS force field combined with TIP3P water. As verified, all four compounds exhibit a strong tendency to phase-separate, thereby calling for the use of carrier molecules as aids for the drug to circulate in the blood and enter the cells. Systems of two such carrier molecules, the hyperbranched poly(ethylene imine) (HBPEI) polyelectrolyte and hyperbranched polyglycerol (HPG), are examined in mixtures with carvacrol, the most abundant among the four compounds, at a range of concentrations, as well as with all four compounds present in natural proportions. Although a tendency of the terpenoids to cluster separately persists at high concentrations, promising association effects are observed for all drug–polymer ratios. HBPEI systems tend to form diffuse structures comprising small mixed clusters as well as freely floating polymer and essential oil molecules, a finding attributed to the polymer–polymer electrostatic repulsions, which here are only partially screened by the counterions. On the other hand, the electrically neutral HPG molecules cluster together with essential oil species to form a single nanodroplet. Currently, terpenoid–polymer clusters near lipid bilayer membranes are being studied to determine the propensity of the formed complexes to enter cell membranes.

A new glimpse on the active site of SARS-CoV-2 3CLpro, coupled with drug repurposing study

Coronavirus disease 2019 (COVID-19) is caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2). Its main protease, 3C-like protease (3CLpro), is an attractive target for drug design, due to its importance in virus replication. The analysis of the radial distribution function of 159 3CLpro structures reveals a high similarity index. A study of the catalytic pocket of 3CLpro with bound inhibitors reveals that the influence of the inhibitors is local, perturbing dominantly only residues in the active pocket. A machine learning based model with high predictive ability against SARS-CoV-2 3CLpro is designed and validated. The model is used to perform a drug-repurposing study, with the main aim to identify existing drugs with the highest 3CLpro inhibition power. Among antiviral agents, lopinavir, idoxuridine, paritaprevir, and favipiravir showed the highest inhibition potential. Enzyme - ligand interactions as a key ingredient for successful drug design.

In Silico Evaluation of Different Flavonoids from Medicinal Plants for Their Potency against SARS-CoV-2

The ongoing pandemic situation of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a global threat to both the world economy and public health. Therefore, there is an urgent need to discover effective vaccines or drugs to fight against this virus. The flavonoids and their medicinal plant sources have already exhibited various biological effects, including antiviral, anti-inflammatory, antioxidant, etc. This study was designed to evaluate different flavonoids from medicinal plants as potential inhibitors against the spike protein (Sp) and main protease (Mpro) of SARS-CoV-2 using various computational approaches such as molecular docking, molecular dynamics. The binding affinity and inhibitory effects of all studied flavonoids were discussed and compared with some antiviral drugs that are currently being used in COVID-19 treatment namely favipiravir, lopinavir, and hydroxychloroquine, respectively. Among all studies flavonoids and proposed antiviral drugs, luteolin and mundulinol exhibited the highest binding affinity toward Mpro and Sp. Drug-likeness and ADMET studies revealed that the chosen flavonoids are safe and non-toxic. One hundred ns-MD simulations were implemented for luteolin-Mpro, mundulinol-Mpro, luteolin-Sp, and mundulinol-Sp complexes and the results revealed strong stability of these flavonoid-protein complexes. Furthermore, MM/PBSA confirms the stability of luteolin and mundulinol interactions within the active sites of this protein. In conclusion, our findings reveal that the promising activity of luteolin and mundulinol as inhibitors against COVID-19 via inhibiting the spike protein and major protease of SARS CoV-2, and we urge further research to achieve the clinical significance of our proposed molecular-based efficacy.

Designing a multi-epitope vaccine candidate to combat MERS-CoV by employing an immunoinformatics approach

Currently, no approved vaccine is available against the Middle East respiratory syndrome coronavirus (MERS-CoV), which causes severe respiratory disease. The spike glycoprotein is typically considered a suitable target for MERS-CoV vaccine candidates. A computational strategy can be used to design an antigenic vaccine against a pathogen. Therefore, we used immunoinformatics and computational approaches to design a multi-epitope vaccine that targets the spike glycoprotein of MERS-CoV. After using numerous immunoinformatics tools and applying several immune filters, a poly-epitope vaccine was constructed comprising cytotoxic T-cell lymphocyte (CTL)-, helper T-cell lymphocyte (HTL)-, and interferon-gamma (IFN-γ)-inducing epitopes. In addition, various physicochemical, allergenic, and antigenic profiles were evaluated to confirm the immunogenicity and safety of the vaccine. Molecular interactions, binding affinities, and the thermodynamic stability of the vaccine were examined through molecular docking and dynamic simulation approaches, during which we identified a stable and strong interaction with Toll-like receptors (TLRs). In silico immune simulations were performed to assess the immune-response triggering capabilities of the vaccine. This computational analysis suggested that the proposed vaccine candidate would be structurally stable and capable of generating an effective immune response to combat viral infections; however, experimental evaluations remain necessary to verify the exact safety and immunogenicity profile of this vaccine.

Topological indices and QSPR/QSAR analysis of some antiviral drugs being investigated for the treatment of COVID-19 patients

The spread of novel virus SARS-CoV-2, well known as COVID-19 has become a major health issue currently which has turned up to a pandemic worldwide. The treatment recommendations are variable. Lack of appropriate medication has worsened the disease. On the basis of prior research, scientists are testing drugs based on medical therapies for SARS and MERS. Many drugs which include lopinavir, ritonavir and thalidomide are listed in the new recommendations. A topological index is a type of molecular descriptor that simply defines numerical values associated with the molecular structure of a compound that is effectively used in modeling many physicochemical properties in numerous quantitative structure-property/activity relationship (QSPR/QSAR) studies. In this study, several degree-based and neighborhood degree sum-based topological indices for several antiviral drugs were investigated by using a M-polynomial and neighborhood M-polynomial methods. In addition, a QSPR was established between the various topological indices and various physicochemical properties of these antiviral drugs along with remdesivir, chloroquine, hydroxychloroquine and theaflavin was performed in order to assess the efficacy of the calculated topological indices. The obtained results reveal that topological indices under study have strong correlation with the physicochemical characteristics of the potential antiviral drugs. A biological activity (pIC50) of these compounds were also investigated by using multiple linear regressions (MLR) analysis.