Evaluating cepharanthine analogues as natural drugs against SARS-CoV-2.

Cepharanthine (CEP) is a natural biscoclaurine alkaloid of plant origin and was recently demonstrated to have anti-severe acute respiratory syndrome coronavirus 2 (anti-SARS-CoV-2) activity. In this study, we evaluated whether natural analogues of CEP may act as potential anti-coronavirus disease 2019 drugs. A total of 24 compounds resembling CEP were extracted from the KNApSAcK database, and their binding affinities to target proteins, including the spike protein and main protease of SARS-CoV-2, NPC1 and TPC2 in humans, were predicted via molecular docking simulations. Selected analogues were further evaluated by a cell-based SARS-CoV-2 infection assay. In addition, the efficacies of CEP and its analogue tetrandrine were assessed. A comparison of the docking conformations of these compounds suggested that the diphenyl ester moiety of the molecules was a putative pharmacophore of the CEP analogues.

Protein druggability assessment for natural products using in silico simulation: A case study with estrogen receptor and the flavonoid genistein.

Traditional herbal medicine (THM) comprises a vast number of natural compounds. Most of them are metabolized into different structures after administration, which makes the clarification of THM's mode of action more complicated. To evaluate the biological activities of those components and metabolites, in silico simulation technology is helpful. We focused on mixed-solvent molecular dynamics (MD) simulation for druggability assessment of natural products. Mixed-solvent MD is an in silico simulation method for the exploration of ligand-binding sites on target proteins, which uses water and an organic molecule mixture. The selection of organic small molecules is an important factor for predicting the characteristics of natural products. In this study, we used the known crystal structure of estrogen receptors with genistein as a test case and explored fragments reflecting the characteristics of natural products. We found that structures with a 4-pyrone structure are more often included in the natural products database compared with the DrugBank database, and we selectively detected the known-binding sites of estrogen receptor α and ß. The results indicate that the 4-pyrone structure might be promising for predicting the protein druggability of flavonoids. Additionally, mixed-solvent MD simulation discriminates the selectivity of genistein between estrogen receptor ß and α, indicating that the simulation can be evaluated using indices that differ from those of traditional ligand docking. Although this approach is still in its early stages, it has the potential to provide valuable information for understanding the diverse biological activities of natural products.

Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target.

A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.

Extended template-based modeling and evaluation method using consensus of binding mode of GPCRs for virtual screening.

G-protein-coupled receptors (GPCRs) are a pharmaceutically important protein family because they mediate numerous physiological functions. The crystal structures of several GPCR subtypes have been determined recently, encouraging efforts to apply structure-based virtual screening (SBVS) along with ligand-based virtual screening (LBVS) to improve the hit rate of active ligands from large chemical libraries. Three-dimensional models are also necessary for GPCR targets whose structures are unknown. Current challenges include the selection of structural templates from available structurally known GPCRs to use for accurate modeling and understanding the diversity of sites recognizing distinct ligands. We have developed and validated an extended template-based modeling and evaluation method for SBVS. Models were generated using a fragmental template procedure in addition to typical template-based modeling methods. The reliability of the models was evaluated using a virtual screening test with known active ligands and decoys and the consensus of the binding mode using the protein-ligand interaction fingerprint (PLIF) derived from the results of docking simulations. This novel workflow was applied to three targets with known structures (human dopamine receptor 3, human histamine H1 receptor, and human delta opioid receptor) and to a target with an unknown structure (human serotonin 2A receptor). In each case, model structures having high ligand selectivity with consensus binding mode were generated.

RCAI-37, 56, 59, 60, 92, 101, and 102, cyclitol and carbasugar analogs of KRN7000: their synthesis and bioactivity for mouse lymphocytes to produce Th1-biased cytokines.

Cyclitol [RCAI-37 (1), 59 (5), 92 (7), and 102 (2)] and carbasugar analogs [RCAI-56 (3), 60 (4), and 101 (6)] of KRN7000 were synthesized through coupling reactions of the corresponding cyclitol or carbasugar derivatives with a cyclic sulfamidate (9) as the key step. Bioassay showed RCAI-56 (3, carbagalactose analog of KRN7000), 59 (5, 1-deoxy-neo-inositol analog), and 92 (7, 1-O-methylated 5) to be remarkably potent stimulants of mouse lymphocytes to produce Th1-biased cytokines, such as interferon-gamma, in vivo. RCAI-60 (4, carbafucose analog) and RCAI-101 (6, 6-O-methylated 3) showed strong bioactivity, on the other hands, RCAI-37 (1, myo-inositol analog) and 102 (2, neo-inositol analog) induced little cytokine production.