No abstract

Marine and terrestrial organisms are an unlimited source of active secondary metabolites. They are characterized by high chemical diversity, biochemical specificity, and other molecular features that make them a valid starting point for lead generation in the drug discovery process. Considering the complex structural architecture and the high number of stereogenic centers, compared to the synthetic drugs, the elucidation of the 2D structure and the assessment of the precise 3D arrangements of natural products (NPs) provide essential insights into ligand‐target interaction which can drive a rational lead optimization process. In this regard, the combination of NMR spectroscopy with modern computational techniques represents one of the most effective approaches to achieve this task. The DFT/NMR integrated method, and its application, in combination with the accurate ROE‐distance analysis, have been employed as valid support, both to clarify the configuration and conformational profile of the active secondary metabolites and to shed more light on the molecular basis of their mechanism of action. Indeed, the comprehension at a molecular level of their interactions with specific targets involved in pathologies, like inflammation and cancer, represents a cornerstone for further investigations through computational tools like molecular docking and molecular dynamics. These high‐efficiency in silico techniques play a crucial part in the drug discovery process as well as in lead optimization procedures. Furthermore, this integrated approach provides the identification of novel chemical platforms disclosing new bioactive molecules with enhanced potency and selectivity.

New computational protocol applied to a repurposing campaign against SARS-CoV-2.

The object of the study was to estimate the long-lasting effects induced by ammonium glycyrrhizinate (AG) after a single administration in mice using animal models of pain and inflammation together with biochemical and docking studies. A single intraperitoneal injection of AG was able to produce anti-inflammatory effects in zymosan-induced paw edema and peritonitis. Moreover, in several animal models of pain, such as the writhing test, the formalin test, and hyperalgesia induced by zymosan, AG administered 24 h before the tests was able to induce a strong antinociceptive effect. Molecular docking studies revealed that AG possesses higher affinity for microsomal prostaglandin E synthase type-2 compared to type-1, whereas it seems to locate better in the binding pocket of cyclooxygenase (COX)-2 compared to COX-1. These results demonstrated that AG induced anti-inflammatory and antinociceptive effects until 24–48 h after a single administration thanks to its ability to bind the COX/mPGEs pathway. Taken together, all these findings highlight the potential use of AG for clinical treatment of pain and/or inflammatory-related diseases.

The estimation of the binding of a set of molecules against BRD9 protein was carried out through an in silico molecular dynamics-driven exhaustive analysis to guide the identification of potential novel ligands. Starting from eight crystal structures of this protein co-complexed with known binders and one apo form, we conducted an exhaustive molecular docking/molecular dynamics (MD) investigation. To balance accuracy and an affordable calculation time, the systems were simulated for 100 ns in explicit solvent. Moreover, one complex was simulated for 1 µs to assess the influence of simulation time on the results. A set of MD-derived parameters was computed and compared with molecular docking-derived and experimental data. MM-GBSA and the per-residue interaction energy emerged as the main indicators for the good interaction between the specific binder and the protein counterpart. To assess the performance of the proposed analysis workflow, we tested six molecules featuring different binding affinities for BRD9, obtaining promising outcomes. Further insights were reported to highlight the influence of the starting structure on the molecular dynamics simulations evolution. The data confirmed that a ranking of BRD9 binders using key parameters arising from molecular dynamics is advisable to discard poor ligands before moving on with the synthesis and the biological tests.

Structure-based virtual screening is highly used in the early stages of drug discovery to identify new putative lead compounds for a given target. However, when a small molecule elicits a biological effect, but its target is unknown, or the side effects it causes arise from its undesired interaction with unknown counterparts, the identification of its interacting targets represents an indispensable task. The computational procedure named inverse virtual screening, which relies on docking a molecule (or a small set of compounds) against panels of target proteins to select the most promising complexes, could be useful to overcome these issues. Panels can contain thousands of proteins, and they must be correctly prepared to assure the best docking performance. Therefore, the preparation of panels of proteins collected in the Protein Data Bank (www.rcsb.org), if manually performed, may be costly in terms of time and efforts, and this can limit the applicability of this approach in high-throughput virtual screening workflows. We here show an automated workflow to speed up panel preparation and development, and to test its performance, this protocol was initially applied to a panel of 628 viral proteins and, afterward, to a panel of transferase proteins (2789 entries) to perform a large inverse virtual screening study, testing a small set of compounds synthesized in our laboratory. Tankyrase 2 (PARP 5b) was selected as their preferred target of interaction, and the predicted binding was validated by means of surface plasmon resonance experiments. This protocol is useful for the rapid identification of the interacting target for a bioactive compound; accordingly, it facilitates the re-evaluation of the pharmacological activity of known active compounds, addressing the repurposing and the polypharmacology concepts.

No abstract

Cannabis sativa L. is a plant belonging to the Cannabaceae family, cultivated for its psychoactive cannabinoid (Δ9-THC) concentration or for its fiber and nutrient content in industrial use. Industrial hemp shows a low Δ9-THC level and is a valuable source of phytochemicals, mainly represented by cannabinoids, flavones, terpenes, and alkaloids, with health-promoting effects. In the present study, we investigated the phytochemical composition of leaves of the industrial hemp cultivar Futura 75, a monoecious cultivar commercially used for food preparations or cosmetic purposes. Leaves are generally discarded, and represent waste products. We analyzed the methanol extract of Futura 75 leaves by HPLC and NMR spectroscopy and the essential oil by GC-MS. In addition, in order to compare the chemical constituents, we prepared the water infusion. One new cannabinoid derivative (1) and seven known components, namely, cannabidiol (2), cannabidiolic acid (3), β-cannabispirol (4), β-cannabispirol (5), canniprene (6), cannabiripsol (7), and cannflavin B (8) were identified. The content of CBD was highest in all preparations. In addition, we present the outcomes of a computational study focused on elucidating the role of 2α-hydroxy-Δ3,7-cannabitriol (1), CBD (2), and CBDA (3) in inflammation and thrombogenesis.