Identification of doxorubicin as a potential therapeutic against SARS-CoV-2 (COVID-19) protease: a molecular docking and dynamics simulation studies

Cefoperazone targets D-alanyl-D-alanine carboxypeptidase (DAC) to control Morganella morganii-mediated infection: a subtractive genomic and molecular dynamics approach

Morganella morganii is a Gram-negative bacterial pathogen that causes bacteremia, urinary tract infections, intra-abdominal infections, chorioamnionitis, neonatal sepsis, and newborn meningitis. To control this bacterial pathogen a total of 3565 putative proteins targets in Morganella morganii were screened using comparative subtractive analysis of biochemical pathways annotated by the KEGG that did not share any similarities with human proteins. One of the targets, D-alanyl-D-alanine carboxypeptidase DacB [Morganella] was observed to be implicated in the majority of cell wall synthesis pathways, leading to its selection as a novel pharmacological target. The drug that interacted optimally with the identified target was observed to be Cefoperazone (DB01329) with the estimated free energy of binding -8.9 Kcal/mol. During molecular dynamics simulations; it was observed that DB01328-2exb and DB01329-2exb complexes showed similar values as the control FMX-2exb complex near 0.2 nm with better stability. Furthermore, MMPBSA total free energy calculation showed better binding energy than the control complex for DB01329-2exb interaction i.e. -31.50 (±0.93) kcal/mol. Our presented research suggested that D-alanyl-D-alanine carboxypeptidase DacB could be a therapeutic target and cefoperazone could be a promising ligand to inhibit the D-alanyl-D-alanine carboxypeptidase DacB protein of Morganella morganii. To identify prospective therapeutic and vaccine targets in Morganella morganii, this is the first computational and subtractive genomics investigation of various metabolic pathways exploring other therapeutic targets of Morganella morganii. In vitro/in vivo experimental validation of the identified target D-alanyl-D-alanine carboxypeptidase and the design of its inhibitors is suggested to figure out the best dose, the drug's effectiveness, and its toxicity.Communicated by Ramaswamy H. Sarma.

Gene crosstalk between COVID-19 and preeclampsia revealed by blood transcriptome analysis

Background

The extensive spread of coronavirus disease 2019 (COVID-19) has led to a rapid increase in global mortality. Preeclampsia is a commonly observed pregnancy ailment characterized by high maternal morbidity and mortality rates, in addition to the restriction of fetal growth within the uterine environment. Pregnant individuals afflicted with vascular disorders, including preeclampsia, exhibit an increased susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection via mechanisms that have not been fully delineated. Additionally, the intricate molecular mechanisms underlying preeclampsia and COVID-19 have not been fully elucidated. This study aimed to discern commonalities in gene expression, regulators, and pathways shared between COVID-19 and preeclampsia. The objective was to uncover potential insights that could contribute to novel treatment strategies for both COVID-19 and preeclampsia.

Method

Transcriptomic datasets for COVID-19 peripheral blood (GSE152418) and preeclampsia blood (GSE48424) were initially sourced from the Gene Expression Omnibus (GEO) database. Subsequent to that, we conducted a subanalysis by selecting females from the GSE152418 dataset and employed the "Deseq2" package to identify genes that exhibited differential expression. Simultaneously, the "limma" package was applied to identify differentially expressed genes (DEGs) in the preeclampsia dataset (GSE48424). Following that, an intersection analysis was conducted to identify the common DEGs obtained from both the COVID-19 and preeclampsia datasets. The identified shared DEGs were subsequently utilized for functional enrichment analysis, transcription factor (TF) and microRNAs (miRNA) prediction, pathway analysis, and identification of potential candidate drugs. Finally, to validate the bioinformatics findings, we collected peripheral blood mononuclear cell (PBMC) samples from healthy individuals, COVID-19 patients, and Preeclampsia patients. The abundance of the top 10 Hub genes in both diseases was assessed using real-time quantitative polymerase chain reaction (RT-qPCR).

Result

A total of 355 overlapping DEGs were identified in both preeclampsia and COVID-19 datasets. Subsequent ontological analysis, encompassing Gene Ontology (GO) functional assessment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, revealed a significant association between the two conditions. Protein-protein interactions (PPIs) were constructed using the STRING database. Additionally, the top 10 hub genes (MRPL11, MRPS12, UQCRH, ATP5I, UQCRQ, ATP5D, COX6B1, ATP5O, ATP5H, NDUFA6) were selected based on their ranking scores using the degree algorithm, which considered the shared DEGs. Moreover, transcription factor-gene interactions, protein-drug interactions, co-regulatory networks of DEGs and miRNAs, and protein-drug interactions involving the shared DEGs were also identified in the datasets. Finally, RT-PCR results confirmed that 10 hub genes do exhibit distinct expression profiles in the two diseases.

Conclusion

This study successfully identified overlapping DEGs, functional pathways, and regulatory elements between COVID-19 and preeclampsia. The findings provide valuable insights into the shared molecular mechanisms and potential therapeutic targets for both diseases. The validation through RT-qPCR further supports the distinct expression profiles of the identified hub genes in COVID-19 and preeclampsia, emphasizing their potential roles as biomarkers or therapeutic targets in these conditions.

In Silico and In Vitro Investigation of Phytochemicals Against Shrimp AHPND Syndrome Causing PirA/B Toxins of Vibrio parahaemolyticus

In Southeast Asia, the penaeid shrimp aquaculture production faces a new pandemic bacterial disease called acute hepatopancreatic necrosis disease (AHPND). The highly profitable pacific white shrimp, Litopenaeus vannamei, has become a challenging species due to severe lethal infections. Recent research has identified a critical pathogen, Vibrio parahaemolyticus, which caused significant loss in the shrimp industry. The disease pathway involves a virulence plasmid encoding binary protein toxins (PirA/B) that cause cell death. The protein toxins were inherited and conjugatively transferred to other Vibrio species through a post-segregational killing system. In this study, "in silico" (Glide, 2021) analysis identified four phytocompounds as myricetin (Myr), ( +)-taxifolin (TF), (-)-epigallocatechin gallate (EGCG), and strychnine (STN) which could be most effective against both the toxins concerning its docking score and affinity. The interactions of complexes and the critical amino acids involved in docking were analyzed using the Discovery Studio (version 2016). Molecular dynamic studies showed lower root mean square deviations (RMSD) and improved stabilization of ( +)-taxifolin (TF) and (-)-epigallocatechin-3-gallate (EGCG) against both the protein toxins. The antibacterial potential of all four selected compounds had tested against pathogenic strains of V. parahaemolyticus through minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). The best MBC results were observed at concentrations of 1 mg/mL for EGCG and 1.25 mg/mL for TF. Moreover, the complete reduction of viable cell counts in the in vitro bactericidal activity had recorded after 24 h of incubation.

Integrating population-level and cell-based signatures for drug repositioning

Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Furthermore, drugs with genetic evidence are more likely to progress successfully through clinical trials towards FDA approval. Exploiting these developments, single gene-based drug repositioning methods have been implemented, but approaches leveraging the entire spectrum of molecular signatures are critically underexplored. Most multi-gene-based approaches rely on differential gene expression (DGE) analysis, which is prone to identify the molecular consequence of disease and renders causal inference challenging. We propose a framework TReD (Transcriptome-informed Reversal Distance) that integrates population-level disease signatures robust to reverse causality and cell-based drug-induced transcriptome response profiles. TReD embeds the disease signature and drug profile in a high-dimensional normed space, quantifying the reversal potential of candidate drugs in a disease-related cell screen assay. The robustness is ensured by evaluation in additional cell screens. For an application, we implement the framework to identify potential drugs against COVID-19. Taking transcriptome-wide association study (TWAS) results from four relevant tissues and three DGE results as disease features, we identify 37 drugs showing potential reversal roles in at least four of the seven disease signatures. Notably, over 70% (27/37) of the drugs have been linked to COVID-19 from other studies, and among them, eight drugs are supported by ongoing/completed clinical trials. For example, TReD identifies the well-studied JAK1/JAK2 inhibitor baricitinib, the first FDA-approved immunomodulatory treatment for COVID-19. Novel potential candidates, including enzastaurin, a selective inhibitor of PKC-beta which can be activated by SARS-CoV-2, are also identified. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screens that can accelerate the search for new therapeutic strategies.

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

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

In Silico Study of the Interactions of Anle138b Isomer, an Inhibitor of Amyloid Aggregation, with Partner Proteins

Herein, we aimed to highlight current "gaps" in the understanding of the potential interactions between the Anle138b isomer ligand, a promising agent for clinical research, and the intrinsically disordered alpha-synuclein protein. The presence of extensive unstructured areas in alpha-synuclein determines its existence in the cell of partner proteins, including the cyclophilin A chaperone, which prevents the aggregation of alpha-synuclein molecules that are destructive to cell life. Using flexible and cascaded molecular docking techniques, we aimed to expand our understanding of the molecular architecture of the protein complex between alpha-synuclein, cyclophilin A and the Anle138b isomer ligand. We demonstrated the possibility of intricate complex formation under cellular conditions and revealed that the main interactions that stabilize the complex are hydrophobic and involve hydrogen.

Pea eggplant (Solanum torvum Swartz) is a source of plant food polyphenols with SARS-CoV inhibiting potential

Background

Pea eggplant (Solanum torvum Swartz) commonly known as turkey berry or 'terung pipit' in Malay is a vegetable plant widely consumed by the local community in Malaysia. The shrub bears pea-like turkey berry fruits (TBFs), rich in phytochemicals of medicinal interest. The TBF phytochemicals hold a wide spectrum of pharmacological properties. In this study, the TBF phytochemicals' potential inhibitory properties were evaluated against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) of the Coronavirus disease 2019 (COVID-19). The TBF polyphenols were screened against SARS-CoV receptors via molecular docking and the best receptor-ligand complex was validated further by molecular dynamics (MD) simulation.

Method

The SARS-CoV receptor structure files (viral structural components) were retrieved from the Protein Data Bank (PDB) database: membrane protein (PDB ID: 3I6G), main protease (PDB ID: 5RE4), and spike glycoproteins (PDB ID: 6VXX and 6VYB). The receptor binding pocket regions were identified by Discovery Studio (BIOVIA) for targeted docking with TBF polyphenols (genistin, kaempferol, mellein, rhoifolin and scutellarein). The ligand and SARS-CoV family receptor structure files were pre-processed using the AutoDock tools. Molecular docking was performed with the Lamarckian genetic algorithm using AutoDock Vina 4.2 software. The best pose (ligand-receptor complex) from the molecular docking analysis was selected based on the minimum binding energy (MBE) and extent of structural interactions, as indicated by BIOVIA visualization tool. The selected complex was validated by a 100 ns MD simulation run using the GROMACS software. The dynamic behaviour and stability of the receptor-ligand complex were evaluated by the root mean square displacement (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), solvent accessible surface volume (SASV) and number of hydrogen bonds.

Results

At RMSD = 0, the TBF polyphenols showed fairly strong physical interactions with SARS-CoV receptors under all possible combinations. The MBE of TBF polyphenol-bound SARS CoV complexes ranged from -4.6 to -8.3 kcal/mol. Analysis of the structural interactions showed the presence of hydrogen bonds, electrostatic and hydrophobic interactions between the receptor residues (RR) and ligands atoms. Based on the MBE values, the 3I6G-rhoifolin (MBE = -8.3 kcal/mol) and 5RE4-genistin (MBE = -7.6 kcal/mol) complexes were ranked with the least value. However, the latter showed a greater extent of interactions between the RRs and the ligand atoms and thus was further validated by MD simulation. The MD simulation parameters of the 5RE4-genistin complex over a 100 ns run indicated good structural stability with minimal flexibility within genistin binding pocket region. The findings suggest that S. torvum polyphenols hold good therapeutics potential in COVID-19 management.

Identification of Doxorubicin as Repurposing Inhibitory Drug for MERS-CoV PLpro

Middle East respiratory syndrome coronavirus (MERS-CoV), belonging to the betacoronavirus genus can cause severe respiratory illnesses, accompanied by pneumonia, multiorgan failure, and ultimately death. CoVs have the ability to transgress species barriers and spread swiftly into new host species, with human-to-human transmission causing epidemic diseases. Despite the severe public health threat of MERS-CoV, there are currently no vaccines or drugs available for its treatment. MERS-CoV papain-like protease (PLpro) is a key enzyme that plays an important role in its replication. In the present study, we evaluated the inhibitory activities of doxorubicin (DOX) against the recombinant MERS-CoV PLpro by employing protease inhibition assays. Hydrolysis of fluorogenic peptide from the Z-RLRGG-AMC-peptide bond in the presence of DOX showed an IC₅₀ value of 1.67 μM at 30 min. Subsequently, we confirmed the interaction between DOX and MERS-CoV PLpro by thermal shift assay (TSA), and DOX increased ΔT_m by ~20 °C, clearly indicating a coherent interaction between the MERS-CoV PL protease and DOX. The binding site of DOX on MERS-CoV PLpro was assessed using docking techniques and molecular dynamic (MD) simulations. DOX bound to the thumb region of the catalytic domain of the MERS-CoV PLpro. MD simulation results showed flexible BL2 loops, as well as other potential residues, such as R231, R233, and G276 of MERS-CoV PLpro. Development of drug repurposing is a remarkable opportunity to quickly examine the efficacy of different aspects of treating various diseases. Protease inhibitors have been found to be effective against MERS-CoV to date, and numerous candidates are currently undergoing clinical trials to prove this. Our effort follows a in similar direction.

Adapted tensor decomposition and PCA based unsupervised feature extraction select more biologically reasonable differentially expressed genes than conventional methods

Tensor decomposition- and principal component analysis-based unsupervised feature extraction were proposed almost 5 and 10 years ago, respectively; although these methods have been successfully applied to a wide range of genome analyses, including drug repositioning, biomarker identification, and disease-causing genes' identification, some fundamental problems have been identified: the number of genes identified was too small to assume that there were no false negatives, and the histogram of P values derived was not fully coincident with the null hypothesis that principal component and singular value vectors follow the Gaussian distribution. Optimizing the standard deviation such that the histogram of P values is as much as possible coincident with the null hypothesis results in an increase in the number and biological reliability of the selected genes. Our contribution was that we improved these methods so as to be able to select biologically more reasonable differentially expressed genes than the state of art methods that must empirically assume negative binomial distributions and dispersion relation, which is required for the selecting more expressed genes than less expressed ones, which can be achieved by the proposed methods that do not have to assume these.

Exploring the Binding Interaction of Active Compound of Pineapple against Foodborne Bacteria and Novel Coronavirus (SARS-CoV-2) Based on Molecular Docking and Simulation Studies

Natural resources, particularly plants and microbes, are an excellent source of bioactive molecules. Bromelain, a complex enzyme mixture found in pineapples, has numerous pharmacological applications. In a search for therapeutic molecules, we conducted an in silico study on natural phyto-constituent bromelain, targeting pathogenic bacteria and viral proteases. Docking studies revealed that bromelain strongly bound to food-borne bacterial pathogens and SARS-CoV-2 virus targets, with a high binding energy of −9.37 kcal/mol. The binding interaction was mediated by the involvement of hydrogen bonds, and some hydrophobic interactions stabilized the complex and molecular dynamics. Simulation studies also indicated the stable binding between bromelain and SARS-CoV-2 protease as well as with bacterial targets which are essential for DNA and protein synthesis and are required to maintain the integrity of membranous proteins. From this in silico study, it is also concluded that bromelain could be an effective molecule to control foodborne pathogen toxicity and COVID-19. So, eating pineapple during an infection could help to interfere with the pathogen attaching and help prevent the virus from getting into the host cell. Further, research on the bromelain molecule could be helpful for the management of COVID-19 disease as well as other bacterial-mediated diseases. Thus, the antibacterial and anti-SARS-CoV-2 virus inhibitory potentials of bromelain could be helpful in the management of viral infections and subsequent bacterial infections in COVID-19 patients.

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.

Multi-Level Biological Network Analysis and Drug Repurposing Based on Leukocyte Transcriptomics in Severe COVID-19: In Silico Systems Biology to Precision Medicine

The coronavirus disease 2019 (COVID-19) pandemic causes many morbidity and mortality cases. Despite several developed vaccines and antiviral therapies, some patients experience severe conditions that need intensive care units (ICU); therefore, precision medicine is necessary to predict and treat these patients using novel biomarkers and targeted drugs. In this study, we proposed a multi-level biological network analysis framework to identify key genes via protein–protein interaction (PPI) network analysis as well as survival analysis based on differentially expressed genes (DEGs) in leukocyte transcriptomic profiles, discover novel biomarkers using microRNAs (miRNA) from regulatory network analysis, and provide candidate drugs targeting the key genes using drug–gene interaction network and structural analysis. The results show that upregulated DEGs were mainly enriched in cell division, cell cycle, and innate immune signaling pathways. Downregulated DEGs were primarily concentrated in the cellular response to stress, lysosome, glycosaminoglycan catabolic process, and mature B cell differentiation. Regulatory network analysis revealed that hsa-miR-6792-5p, hsa-let-7b-5p, hsa-miR-34a-5p, hsa-miR-92a-3p, and hsa-miR-146a-5p were predicted biomarkers. CDC25A, GUSB, MYBL2, and SDAD1 were identified as key genes in severe COVID-19. In addition, drug repurposing from drug–gene and drug–protein database searching and molecular docking showed that camptothecin and doxorubicin were candidate drugs interacting with the key genes. In conclusion, multi-level systems biology analysis plays an important role in precision medicine by finding novel biomarkers and targeted drugs based on key gene identification.

Discovery of new drug indications for COVID-19: A drug repurposing approach

Motivation

The outbreak of coronavirus health issues caused by COVID-19(SARS-CoV-2) creates a global threat to public health. Therefore, there is a need for effective remedial measures using existing and approved therapies with proven safety measures has several advantages. Dexamethasone (Pubchem ID: CID0000005743), baricitinib(Pubchem ID: CID44205240), remdesivir (PubchemID: CID121304016) are three generic drugs that have demonstrated in-vitro high antiviral activity against SARS-CoV-2. The present study aims to widen the search and explore the anti-SARS-CoV-2 properties of these potential drugs while looking for new drug indications with optimised benefits via in-silico research.

Method

Here, we designed a unique drug-similarity model to repurpose existing drugs against SARS-CoV-2, using the anti-Covid properties of dexamethasone, baricitinib, and remdesivir as references. Known chemical-chemical interactions of reference drugs help extract interactive compounds withimprovedanti-SARS-CoV-2 properties. Here, we calculated the likelihood of these drug compounds treating SARS-CoV-2 related symptoms using chemical-protein interactions between the interactive compounds of the reference drugs and SARS-CoV-2 target genes. In particular, we adopted a two-tier clustering approach to generate a drug similarity model for the final selection of potential anti-SARS-CoV-2 drug molecules. Tier-1 clustering was based on t-Distributed Stochastic Neighbor Embedding (t-SNE) and aimed to filter and discard outlier drugs. The tier-2 analysis incorporated two cluster analyses performed in parallel using Ordering Points To Identify the Clustering Structure (OPTICS) and Hierarchical Agglomerative Clustering (HAC). As a result, itidentified clusters of drugs with similar actions. In addition, we carried out a docking study for in-silico validation of top candidate drugs.

Result

Our drug similarity model highlighted ten drugs, including reference drugs that can act as potential therapeutics against SARS-CoV-2. The docking results suggested that doxorubicin showed the least binding energy compared to reference drugs. Their practical utility as anti-SARS-CoV-2 drugs, either individually or in combination, warrants further investigation.

Synthesis of indole-based oxadiazoles and their interaction with bacterial peptidoglycan and SARS-CoV-2 main protease: In vitro, molecular docking and in silico ADME/Tox study

In the present study, Indole-based-oxadiazole (1A-17A) compounds were successfully synthesized. The structures of all synthesized compounds were fully characterized by different sophisticated spectroscopic techniques such 1H NMR, 13C NMR, and HREI-MS. Further, the synthesized compounds were explored to investigate their broad-spectrum antibacterial and antibiofilm potential against multidrug resistant Pseudomonas aeruginosa (MDR-PA) and methicillin resistant Staphylococcus aureus (MRSA). The compounds possessed a broad spectrum of antibacterial activity having MIC values of values 1–8 mg/ml against the tested microorganisms. Compound A6 and A7 shows maximum antibacterial activity against MDR-PA, whereas A6, A7 and A11 shows highest activity against MRSA. Furthermore, antibiofilm assay shows that A6, A7 and A11 showed maximum inhibition of biofilm formation and it was found that at 4 mg/ml; A6, A7 and A11 inhibit MRSA biofilm formation by 81.1, 77.5 and 75.9%, respectively; whereas in case of P. aeruginosa; A6 and A7 showed maximum biofilm inhibition and inhibit biofilm formation by 81.5 and 73.7%, respectively. Molecular docking study showed that compounds A6, A7, A8, A10, and A11 had high binding affinity to bacterial peptidoglycan, indicating their potential inhibitory activity against tested bacteria, whereas A6 and A11 were found to be the most effective inhibitors of SARS CoV-2 main protease (3CLpro), with a binding affinity of − 7.78 kcal/mol. Furthermore, SwissADME and pkCSM-pharmacokinetics online tools was applied to calculate the ADME/Tox profile of the synthesized compounds and the toxicity of these chemicals was found to be low. The Lipinski, Veber, Ghose, and Consensus LogP criteria were also used to predict drug-likeness levels of the compounds. Our findings imply that the synthesized compounds could be a useful for the preventing and treating biofilm-related microbial infection as well as SARS-CoV2 infections.

Molybdenum Carbonyl Complexes with Benzimidazole Derivatives Against SARS-CoV-2 by Molecular Docking and DFT/TDDFT Methods

Benzimidazole derivative molecules attract attention of scientists due to their bioactivities. The dramatic changes in recorded activities according to the type and position of the substituents motivate synthesis and analysis of new molecules. Commercial benzimidazole-based molecules have been used in therapeutic procedures. It is known that the activities of metal complexes with benzimidazole derivative ligands have different activities when compared to the benzimidazole main structure. Nowadays, one of the most important health problems is COVID-19, which caused the pandemic that we are still experiencing. Although vaccine studies are important to overcome acute problems, regarding the possible post-vaccination adverse effects, the need for new drugs against the virus is obvious. Considering the urgency and the limited facilities during the pandemic, preliminary in silico studies of candidate molecules are essential. In this study, {[bis-(N-benzylbenzimidazole)] tetracarbonylmolybdenum}, {[bis-(N-4-chlorobenzylbenzimidazole)] tetracarbonylmolybdenum} and {[bis-(N-4-methoxybenzylbenzimidazole)] tetracarbonylmolybdenum} were synthesized and characterized. The optimization and the structural analysis of these molecules were performed by DFT/TDDFT methods. The molecules were docked into SARS coronavirus main peptidase (PDB ID: 2gtb), COVID-19 main protease in complex with Z219104216 (PDB ID: 5r82), COVID-19 main protease in complex with an inhibitor N3 (PDB ID: 6lu7) and Papain-like protease of SARS-CoV-2 (PDB ID: 6w9c) crystal structures for evaluation of their anti-viral activity.

Comprehensive investigation of RNA-sequencing dataset reveals the hub genes and molecular mechanisms of coronavirus disease 2019 acute respiratory distress syndrome

The goal of this study is to reveal the hub genes and molecular mechanisms of the coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS) based on the genome-wide RNA sequencing dataset. The RNA sequencing dataset of COVID-19 ARDS was obtained from GSE163426. A total of 270 differentially expressed genes (DEGs) were identified between COVID-19 ARDS and control group patients. Functional enrichment analysis of DEGs suggests that these DEGs may be involved in the following biological processes: response to cytokine, G-protein coupled receptor activity, ionotropic glutamate receptor signalling pathway and G-protein coupled receptor signalling pathway. By using the weighted correlation network analysis approach to analyse these DEGs, 10 hub DEGs that may play an important role in COVID-19 ARDS were identified. A total of 67 potential COVID-19 ARDS targetted drugs were identified by a complement map analysis. Immune cell infiltration analysis revealed that the levels of T cells CD4 naive, T cells follicular helper, macrophages M1 and eosinophils in COVID-19 ARDS patients were significantly different from those in control group patients. In conclusion, this study identified 10 COVID-19 ARDS-related hub DEGs and numerous potential molecular mechanisms through a comprehensive analysis of the RNA sequencing dataset and also revealed the difference in immune cell infiltration of COVID-19 ARDS.

Therapeutic development by repurposing drugs targeting SARS-CoV-2 spike protein interactions by simulation studies

The human-to-human transmitted respiratory illness in COVID-19 affected by the pathogenic Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2), which appeared in the last of December 2019 in Wuhan, China, and rapidly spread in many countries. Thereon, based on the urgent need for therapeutic molecules, we conducted in silico based docking and simulation molecular interaction studies on repurposing drugs, targeting SARS-CoV-2 spike protein. Further, the best binding energy of doxorubicin interacting with virus spike protein (PDB: 6VYB) was observed to be -6.38 kcal/mol and it was followed by exemestane and gatifloxacin. The molecular simulation dynamics analysis of doxorubicin, Reference Mean Square Deviation (RMSD), Root Mean Square fluctuation (RMSF), Radius of Gyration (Rg), and formation of hydrogen bonds plot interpretation suggested, a significant deviation and fluctuation of Doxorubicin-Spike RBD complex during the whole simulation period. The Rg analysis has stated that the Doxorubicin-Spike RBD complex was stable during 15,000-35,000 ps MDS. The results have suggested that doxorubicin could inhibit the virus spike protein and prevent the access of the SARS-CoV-2 to the host cell. Thus, in-vitro/in-vivo research on these drugs could be advantageous to evaluate significant molecules that control the COVID-19 disease.

In Silico Investigation on the Interaction of Chiral Phytochemicals from Opuntia ficus-indica with SARS-CoV-2 Mpro

Opuntia ficus-indica is a cactaceous plant native to America but, nowadays, widely found worldwide, having been the most common domesticated species of cactus grown as a crop plant in semiarid and arid parts of the globe, including several Mediterranean basin countries. Opuntia ficus-indica can be regarded as a medicinal plant, being source of numerous bioactive phytochemicals such as vitamins, polyphenols, and amino acids. The urgent need for therapeutic treatments for the COronaVIrus Disease 19 (COVID-19), caused by the Severe Acute Respiratory Syndrome (SARS)-Coronavirus (CoV)-2, justifies the great attention currently being paid not only to repurposed antiviral drugs, but also to natural products and herbal medications. In this context, the anti-COVID-19 utility of Opuntia ficus-indica as source of potential antiviral drugs was investigated in this work on the basis of the activity of some of its phytochemical constituents. The antiviral potential was evaluated in silico in docking experiments with Mpro, i.e., the main protease of SARS-CoV-2, that is one of the most investigated protein targets of therapeutic strategies for COVID-19. By using two web-based molecular docking programs (1-Click Mcule and COVID-19 Docking Server), we found, for several flavonols and flavonol glucosides isolated from Opuntia ficus-indica, good binding affinities for Mpro, and in particular, binding energies lower than −7.0 kcal/mol were predicted for astragalin, isorhamnetin, isorhamnetin 3-O-glucoside, 3-O-caffeoyl quinic acid, and quercetin 5,4′-dimethyl ether. Among these compounds, the chiral compound astragalin showed in our in silico studies the highest affinity for Mpro (−8.7 kcal/mol) and also a low toxicity profile, emerging, thus, as an interesting protease inhibitor candidate for anti-COVID-19 strategies.