Comprehensive computational _target fishing approach to identify Xanthorrhizol putative _targets

Previous study has shown the antimicrobial activities of mucus protein extracted from Anabastestudineus. In this study, we are interested in characterizing the anticancer activity of the A.testudineus antimicrobial peptides (AMPs). The mucus was extracted, fractioned, and subjected to antibacterial activity testing to confirm the fish's AMPs production. The cytotoxic activity of each fraction was also identified. Fraction 2 (F2), which shows toxicity against MCF7 and MDA-MB-231 were sent for peptide sequencing to identify the bioactive peptide. The two peptides were then synthetically produced and subjected to cytotoxic assay to prove their efficacy against cancer cell lines. The IC50 for AtMP1 against MCF7 and MDA-MB-231 were 8.25 ± 0.14 μg/ml and 9.35 ± 0.25 μg/ml respectively, while for AtMP2 it is 5.89 ± 0.14 μg/ml and 6.97 ± 0.24 μg/ml respectively. AtMP1 and AtMP2 treatment for 48 h induced breast cancer cell cycle arrest and apoptosis by upregulating the p53, which lead to upregulate pro-apoptotic BAX gene and downregulate the anti-apoptotic BCL-2 gene, consequently, trigger the activation of the caspase-3. This interaction was supported by docking analysis (QuickDBD, HPEPDOCK, and ZDOCK) and immunoprecipitation. This study provided new prospects in the development of highly effective and selective cancer therapeutics based on antimicrobial peptides.

In spite of the large number of repositioned drugs and direct-acting antivirals in clinical trials for the management of the ongoing COVID-19 pandemic, there are few cost-effective therapeutic options for severe acute respiratory syndrome (SARS) coronavirus 2 (SCoV2) infection. In this paper, we show that xanthorrhizol (XNT), a bisabolane-type sesquiterpenoid compound isolated from the Curcuma xanthorrhizza Roxb., a ginger-line plant of the family Zingiberaceae, displays a potent antiviral efficacy in vitro against SCoV2 and other related coronaviruses, including SARS-CoV-1 (SCoV1) and a common cold-causing human coronavirus. XNT reduced infectious SCoV2 titer by ~3-log10 at 20 μM and interfered with the replication of the SCoV1 subgenomic replicon, while it had no significant antiviral effects against hepatitis C virus and noroviruses. Further, XNT exerted similar antiviral functions against SCoV2 variants, such as a GH clade strain and a delta strain currently predominant worldwide. Neither SCoV2 entry into cells nor the enzymatic activity of viral RNA polymerase (Nsp12), RNA helicase (Nsp13), or the 3CL main protease (Nsp5) was inhibited by XNT. While its CoV replication inhibitory mechanism remains elusive, our results demonstrate that the traditional folk medicine XNT could be a promising antiviral candidate that inhibits a broad range of SCoV2 variants of concern and other related CoVs.

In silico _target fishing, whose aim is to identify possible protein _targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose _target is still unknown. Moreover, _target fishing can be employed for the identification of off _targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, _target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug _targets. While experimental _target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein _targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make _target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main _target fishing approach and as a further development of already applied strategies. This review reports on the main in silico _target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing _target fishing studies.