Discovery of the molecular mechanisms of the novel chalcone-based Magnaporthe oryzae inhibitor C1 using transcriptomic profiling and co-expression network analysis

Methoxy Chalcone Derivatives: Promising Antimicrobial Agents Against Phytopathogens

Chalcone (E)-1,3-diphenyl-prop-2-en-1-one and a series of 14 methoxylated derivatives have been synthesized via Claisen-Schmidt aldol condensation and characterized by FTIR, CG/MS/DIC, 1D (1H and 13C), 2D (COSY, HSQC, and HMBC) NMR, and EMAR techniques. All molecules were tested at 1 mM concentration for antifungal (Sclerotium sp., Macrophomina phaesolina and Colletotrichum gloeosporioides), antibacterial (Acidovorax citrulli two strains), and antiprotozoal (Phytomonas serpens) activities. Unmodified chalcone (CH0) and derivatives CH1, CH2, CH8 stood out in terms of antifungal activity. CH0 presented IC50 values of 47.3 μM (9.8 μg/mL) for the fungus C. gloeosporioides. In addition, fluorescence microscopy indicated that CH0 promoted loss of hyphal cell membrane integrity. The CH1 and CH2 derivatives promoted the inhibition of Sclerotium sp. with IC50 of 127.5 μM (32.9 μg/mL) and 110.4 μM (29.6 μg/mL), respectively. All molecules showed high activity against the phytoparasite P. serpens with IC50 values of 0.98, 2.40, 10.25, and 3.11 μM for the derivatives CH2, CH3, CH5 and CH14 respectively. The results demonstrated that derivatives methoxylated in both rings (CH2) as well as derivatives with a furan ring associated with the methoxy group in ring A, as well as unmodified chalcone can be promising agricultural fungicides for controlling the fungi studied.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions

The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.