Genomic, transcriptomic, and metabolomic analysis of Oldenlandia corymbosa reveals the biosynthesis and mode of action of anti-cancer metabolites.

Plants accumulate a vast array of secondary metabolites, which constitute a natural resource for pharmaceuticals. Oldenlandia corymbosa belongs to the Rubiaceae family, and has been used in traditional medicine to treat different diseases, including cancer. However, the active metabolites of the plant, their biosynthetic pathway and mode of action in cancer are unknown. To fill these gaps, we exposed this plant to eight different stress conditions and combined different omics data capturing gene expression, metabolic profiles, and anti-cancer activity. Our results show that O. corymbosa extracts are active against breast cancer cell lines and that ursolic acid is responsible for this activity. Moreover, we assembled a high-quality genome and uncovered two genes involved in the biosynthesis of ursolic acid. Finally, we also revealed that ursolic acid causes mitotic catastrophe in cancer cells and identified three high-confidence protein binding targets by Cellular Thermal Shift Assay (CETSA) and reverse docking. Altogether, these results constitute a valuable resource to further characterize the biosynthesis of active metabolites in the Oldenlandia group, while the mode of action of ursolic acid will allow us to further develop this valuable compound.

Proposing drug fragments for dengue virus NS5 protein.

Dengue fever is a febrile illness caused by Dengue Virus, which belongs to the Flaviviridae family. Among its proteome, the nonstructural protein 5 (NS5) is the biggest and most conserved. It has a primer-independent RNA-dependent RNA polymerase (RdRp) domain at its C-Terminus. Zou et al. studied the biological relevance of the two conserved cavities (named A and B) within the NS5 proteins of dengue virus (DENV) and West Nile Virus (WNV) using mutagenesis and revertant analysis and found four mutations located at cavity B having effects on viral replication. They recommended Cavity B, but not Cavity A as a potential target for drugs against flavivirus RdRp. In this study, we virtually screened the MayBridge drug fragments dataset for potential small molecule binders of cavity B using both AutoDock Vina, the standard docking tool, and QuickVina 2, our previously developed tool. We selected 16 fragments that appeared in the top 100 docking results of each of the representative structures of NS5. Visual inspection suggests that they have reasonable binding poses. The 16 predicted fragments are plausible drug candidates and should be considered for further validation, optimization, and linking to come up with a suitable inhibitor of dengue virus.

Reduction of false positives in structure-based virtual screening when receptor plasticity is considered.

Structure-based virtual screening for selecting potential drug candidates is usually challenged by how numerous false positives in a molecule library are excluded when receptor plasticity is considered. In this study, based on the binding energy landscape theory, a hypothesis that a true inhibitor can bind to different conformations of the binding site favorably was put forth, and related strategies to defeat this challenge were devised; reducing false positives when receptor plasticity is considered. The receptor in the study is the influenza A nucleoprotein, whose oligomerization is a requirement for RNA binding. The structural flexibility of influenza A nucleoprotein was explored by molecular dynamics simulations. The resultant distinctive structures and the crystal structure were used as receptor models in docking exercises in which two binding sites, the tail-loop binding pocket and the RNA binding site, were targeted with the Otava PrimScreen1 diversity-molecule library using the GOLD software. The intersection ligands that were listed in the top-ranked molecules from all receptor models were selected. Such selection strategy successfully distinguished high-affinity and low-affinity control molecules added to the molecule library. This work provides an applicable approach for reducing false positives and selecting true ligands from molecule libraries.