With twice the number of cancer’s deaths, cardiovascular diseases have become the leading cause of death worldwide. Atherosclerosis, in particular, is a progressive, chronic inflammatory cardiovascular disease caused by persistent damage to blood vessels due to elevated cholesterol levels and hyperlipidemia. This condition is characterized by an increase in serum cholesterol, triglycerides, and low-density lipoprotein, and a decrease in high-density lipoprotein. Although existing therapies with hypolipidemic effects can improve the living standards of patients with cardiovascular diseases, the drugs currently used in clinical practice have certain side effects, which insists on the need for the development of new types of drugs with lipid-lowering effects. Some marine-derived substances have proven hypolipidemic activities with fewer side effects and stand as a good alternative for drug development. Recently, there have been thousands of studies on substances with lipid-lowering properties of marine origin, and some are already implemented in clinical practice. Here, we summarize the active components of marine-derived products having a hypolipidemic effect. These active constituents according to their source are divided into algal, animal, plant and microbial and contribute to the development and utilization of marine medicinal products with hypolipidemic effects.

Target identification of small molecules is an important and still changeling work in the area of drug discovery, especially for botanical drug development. Indistinct understanding of the relationships of ligand–protein interactions is one of the main obstacles for drug repurposing and identification of off-targets. In this study, we collected 9063 crystal structures of ligand-binding proteins released from January, 1995 to April, 2021 in PDB bank, and split the complexes into 5133 interaction pairs of ligand atoms and protein fragments (covalently linked three heavy atoms) with interatomic distance ≤5 Å. The interaction pairs were grouped into ligand atoms with the same SYBYL atom type surrounding each type of protein fragment, which were further clustered via Bayesian Gaussian Mixture Model (BGMM). Gaussian distributions with ligand atoms ≥20 were identified as significant interaction patterns. Reliability of the significant interaction patterns was validated by comparing the difference of number of significant interaction patterns between the docked poses with higher and lower similarity to the native crystal structures. Fifty-one candidate targets of brucine, strychnine and icajine involved in Semen Strychni (Mǎ Qián Zǐ) and eight candidate targets of astragaloside-IV, formononetin and calycosin-7-glucoside involved in Astragalus (Huáng Qí) were predicted by the significant interaction patterns, in combination with docking, which were consistent with the therapeutic effects of Semen Strychni and Astragalus for cancer and chronic pain. The new strategy in this study improves the accuracy of target identification for small molecules, which will facilitate discovery of botanical drugs.

No abstract

Background: Accurate target identification of small molecules and downstream target annotation are important in pharmaceutical research and drug development.Methods: We present TAIGET, a friendly and easy to operate graphical web interface, which consists of a docking module based on AutoDock Vina and LeDock, a target screen module based on a Bayesian–Gaussian mixture model (BGMM), and a target annotation module derived from >14,000 cancer-related literature works.Results: TAIGET produces binding poses by selecting ≤5 proteins at a time from the UniProt ID-PDB network and submitting ≤3 ligands at a time with the SMILES format. Once the identification process of binding poses is complete, TAIGET then screens potential targets based on the BGMM. In addition, three medical experts and 10 medical students curated associations among drugs, genes, gene regulation, cancer outcome phenotype, 2,170 cancer cell types, and 73 cancer types from the PubMed literature, with the aim to construct a target annotation module. A target-related PPI network can be visualized by an interactive interface.Conclusion: This online tool significantly lowers the entry barrier of virtual identification of targets for users who are not experts in the technical aspects of virtual drug discovery. The web server is available free of charge at http://www.taiget.cn/.

No abstract