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Computational and network pharmacology analysis of bioflavonoids as possible natural antiviral compounds in COVID-19.

ABSTRACT: Bioflavonoids are the largest group of plant-derived polyphenolic compounds with diverse biological potential and have also been proven efficacious in the treatment of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS). The present investigation validates molecular docking, simulation, and MM-PBSA studies of fifteen bioactive bioflavonoids derived from plants as a plausible potential antiviral in the treatment of COVID-19. Molecular docking studies for 15 flavonoids on the three SARS CoV-2 proteins, non-structural protein-15 Endoribonuclease (NSP15), the receptor-binding domain of spike protein (RBD of S protein), and main protease (M^pro/3CL^pro) were performed and selected protein-ligand complexes were subjected to Molecular Dynamics simulations. The molecular dynamics trajectories were subjected to free energy calculation by the MM-PBSA method. All flavonoids were further assessed for their effectiveness as adjuvant therapy by network pharmacology analysis on the target proteins. The network pharmacology analysis suggests the involvement of selected bioflavonoids in the modulation of multiple signaling pathways like p53, FoxO, MAPK, Wnt, Rap1, TNF, adipocytokine, and leukocyte transendothelial migration which plays a significant role in immunomodulation, minimizing the oxidative stress and inflammation. Molecular docking and molecular dynamics simulation studies illustrated the potential of glycyrrhizic acid, amentoflavone, and mulberroside in inhibiting key SARS-CoV-2 proteins and these results could be exploited further in designing future ligands from natural sources.

SUBMITTER: Patil R

PROVIDER: S-EPMC7756171 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Lithospermum erythrorhizon (LE) is known in Korean traditional medicine for its potent therapeutic effect and antiviral activity. Currently, coronavirus (COVID-19) disease is a developing global pandemic that can cause pneumonia. A precise study of the infection and molecular pathway of COVID-19 is therefore obviously important. The compounds of LE were identified from the Natural Product Activity and Species Source (NPASS) database and screened by SwissADME. The targets interacted with the compounds and were selected using the Similarity Ensemble Approach (SEA) and Swiss Target Prediction (STP) methods. PubChem was used to classify targets linked to COVID-19. The protein-protein interaction (PPI) networks and signaling pathways-targets-bioactive compounds (STB) networks were constructed by RPackage. Lastly, we performed the molecular docking test (MDT) to verify the binding affinity between significant complexes through AutoDock 1.5.6. The Natural Product Activity and Species Source (NPASS) revealed a total of 82 compounds from LE, which interacted with 1262 targets (SEA and STP), and 249 overlapping targets were identified. The 19 final overlapping targets from the 249 targets and 356 COVID-19 targets were ultimately selected. A bubble chart exhibited that inhibition of the MAPK signaling pathway could be a key mechanism of LE on COVID-19. The three key targets (RELA, TNF, and VEGFA) directly related to the MAPK signaling pathway, and methyl 4-prenyloxycinnamate, tormentic acid, and eugenol were related to each target and had the most stable binding affinity. The three bioactive effects on the three key targets might be synergistic effects to alleviate symptoms of COVID-19 infection. Overall, this study shows that LE can play a role in alleviating COVID-19 symptoms, revealing that the three components (bioactive compounds, targets, and mechanism) are the most significant elements of LE against COVID-19. However, the promising mechanism of LE on COVID-19 is only predicted on the basis of mining data; the efficacy of the chemical compounds and the affinity between compounds and the targets in experiment was ignored, which should be further substantiated through clinical trials.

Project description:ObjectiveBased on bioinformatics and network pharmacology, the treatment of Saussurea involucrata (SAIN) on novel coronavirus (COVID-19) was evaluated by the GEO clinical sample gene difference analysis, compound-target molecular docking, and molecular dynamics simulation. role in the discovery of new targets for the prevention or treatment of COVID-19, to better serve the discovery and clinical application of new drugs.Materials and methodsTaking the Traditional Chinese Medicine System Pharmacology Database (TCMSP) as the starting point for the preliminary selection of compounds and targets, we used tools such as Cytoscape 3.8.0, TBtools 1.098, AutoDock vina, R 4.0.2, PyMol, and GROMACS to analyze the compounds of SAIN and targets were initially screened. To further screen the active ingredients and targets, we carried out genetic difference analysis (n = 72) through clinical samples of COVID-19 derived from GEO and carried out biological process (BP) analysis on these screened targets (P ≤ 0.05)., gene = 9), KEGG pathway analysis (FDR≤0.05, gene = 9), protein interaction network (PPI) analysis (gene = 9), and compounds-target-pathway network analysis (gene = 9), to obtain the target Point-regulated biological processes, disease pathways, and compounds-target-pathway relationships. Through the precise molecular docking between the compounds and the targets, we further screened SAIN's active ingredients (Affinity ≤ -7.2 kcal/mol) targets and visualized the data. After that, we performed molecular dynamics simulations and consulted a large number of related Validation of the results in the literature.ResultsThrough the screening, analysis, and verification of the data, it was finally confirmed that there are five main active ingredients in SAIN, which are Quercitrin, Rutin, Caffeic acid, Jaceosidin, and Beta-sitosterol, and mainly act on five targets. These targets mainly regulate Tuberculosis, TNF signaling pathway, Alzheimer's disease, Pertussis, Toll-like receptor signaling pathway, Influenza A, Non-alcoholic fatty liver disease (NAFLD), Neuroactive ligand-receptor interaction, Complement and coagulation cascades, Fructose and mannose metabolism, and Metabolic pathways, play a role in preventing or treating COVID-19. Molecular dynamics simulation results show that the four active ingredients of SAIN, Quercitrin, Rutin, Caffeic acid, and Jaceosidin, act on the four target proteins of COVID-19, AKR1B1, C5AR1, GSK3B, and IL1B to form complexes that can be very stable in the human environment. Tertiary structure exists.ConclusionOur study successfully explained the effective mechanism of SAIN in improving COVID-19, and at the same time predicted the potential targets of SAIN in the treatment of COVID-19, AKR1B1, IL1B, and GSK3B. It provides a new basis and provides great support for subsequent research on COVID-19.

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Computational and network pharmacology analysis of bioflavonoids as possible natural antiviral compounds in COVID-19.

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