In silico polypharmacology: retrospective recognition vs. rational design

Polypharmacology of some medicinal plant metabolites against SARS-CoV-2 and host targets: Molecular dynamics evaluation of NSP9 RNA binding protein

Medicinal plants as rich sources of bioactive compounds are now being explored for drug development against COVID-19. 19 medicinal plants known to exhibit antiviral and anti-inflammatory effects were manually curated, procuring a library of 521 metabolites; this was virtually screened against NSP9, including some other viral and host targets and were evaluated for polypharmacological indications. Leads were identified via rigorous scoring thresholds and ADMET filtering. MM-GBSA calculation was deployed to select NSP9-Lead complexes and the complexes were evaluated for their stability and protein-ligand communication via MD simulation. We identified 5 phytochemical leads for NSP9, 23 for Furin, 18 for ORF3a, and 19 for IL-6. Ochnaflavone and Licoflavone B, obtained from Lonicera japonica (Japanese Honeysuckle) and Glycyrrhiza glabra (Licorice), respectively, were identified to have the highest potential polypharmacological properties for the aforementioned targets and may act on multiple pathways simultaneously to inhibit viral entry, replication, and disease progression. Additionally, MD simulation supports the robust stability of Ochnaflavone and Licoflavone B against NSP9 at the active sites via hydrophobic interactions, H-bonding, and H-bonding facilitated by water. This study promotes the initiation of further experimental analysis of natural product-based anti-COVID-19 therapeutics.

How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases

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Five proteins related to diabetic disease were selected from Protein Data Bank.

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Binding scores were calculated for five proteins with 169 fullerene derivatives.

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Correlation between drug-like descriptors and binding scores activity was examined.

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The contribution of descriptors to protein-ligand binding was demonstrated.

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The QSARs models for prediction of binding scores activity were built.

Fullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their potential inhibitory effect on therapeutic targets associated with diabetic disease, as well as analysis of protein–ligand binding in order to identify the key binding characteristics of FDs.

Therapeutic drug compounds when entering the biological system usually inevitably encounter and interact with a vast variety of biomolecules that are responsible for many different functions in organisms. Protein biomolecules are the most important functional components and used in this study as target structures. The structures of proteins [(PDB ID: 1BMQ, 1FM6, 1GPB, 1H5U, 1US0)] belonging to the class of anti-diabetes targets were obtained from the Protein Data Bank (PDB). Protein binding activity data (binding scores) were calculated for the dataset of 169 FDs related to these five proteins. Subsequently, the resulting data were analyzed using various machine learning and cheminformatics methods, including artificial neural network algorithms for variable selection and property prediction.

The Quantitative Structure-Activity Relationship (QSAR) models for prediction of binding scores activity were built up according to five Organization for Economic Co-operation and Development (OECD) principles.

All the data obtained can provide important information for further potential use of FDs with different functional groups as promising medical antidiabetic agents. Binding scores activity can be used for ranking of FDs in terms of their inhibitory activity (pharmacological properties) and potential toxicity.

Cardiovascular Disease Chemogenomics Knowledgebase-guided Target Identification and Drug Synergy Mechanism Study of an Herbal Formula

Combination therapy is a popular treatment for various diseases in the clinic. Among the successful cases, Traditional Chinese Medicinal (TCM) formulae can achieve synergistic effects in therapeutics and antagonistic effects in toxicity. However, characterizing the underlying molecular synergisms for the combination of drugs remains a challenging task due to high experimental expenses and complication of multicomponent herbal medicines. To understand the rationale of combination therapy, we investigated Sini Decoction, a well-known TCM consisting of three herbs, as a model. We applied our established diseases-specific chemogenomics databases and our systems pharmacology approach TargetHunter to explore synergistic mechanisms of Sini Decoction in the treatment of cardiovascular diseases. (1) We constructed a cardiovascular diseases-specific chemogenomics database, including drugs, target proteins, chemicals, and associated pathways. (2) Using our implemented chemoinformatics tools, we mapped out the interaction networks between active ingredients of Sini Decoction and their targets. (3) We also in silico predicted and experimentally confirmed that the side effects can be alleviated by the combination of the components. Overall, our results demonstrated that our cardiovascular disease-specific database was successfully applied for systems pharmacology analysis of a complicated herbal formula in predicting molecular synergetic mechanisms, and led to better understanding of a combinational therapy.