No abstract

We describe the one-pot synthesis, unique geometries and intriguing host–guest properties of a new class of supramolecular macrocycles – biphen[n]arenes (n = 3, 4).

Diabetes mellitus (DM) is a group of metabolic disorders in which high blood sugar levels occur over a prolonged period. Approximately 4% of the global population is affected by DM. Western medical treatment methods for diabetes including injection or oral hypoglycemic drugs have some toxic or side effects, economic pressures, and so on. Many researchers turn to discover new drugs from natural products or Traditional Chinese Medicine (TCM). Flavonoids are widely distributed in plants, and many studies have shown that flavonoids possess antidiabetic properties, exhibiting not only well-recognized antidiabetic and hypoglycemic activities but also activity in the treatment of diabetic complications. In this review, we systematically summarized anti-diabetic flavonoid compounds based on structure classification by examining the PubMed, Springer Link, Web of Science, and CNKI databases. There are 13 flavonoid compounds listed which have been studied extensively and have antidiabetic features respectively. Apigenin, baicalein, and catechin mainly reduces blood glucose via anti-oxidation; hesperidin is good for diabetic neuropathy; glycyrrhiza flavonoids have a significant effect on gestational DM; quercetin takes advantage of crossing the blood–brain barrier and improving renal function. Some compounds have protective and preventive effects on diabetic complications, such as kaempferol and puerarin which are beneficial to cardiomyopathy; myricetin has therapeutic potential in the treatment of DN; dihydromyricetin might improve CI. It is a pity or might be a pointcut that most studies remain in the animal experimental stage, and further investigation should be carried out.

Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate proteins. Protease -controlled proteolysis plays a key role in the degradation and recycling of proteins, which is essential for various physiological processes. Thus, solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases, as well as for therapeutic target identification and pharmaceutical applicability. Consequently, there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural information. In this study, we present Procleave, a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural information. Structural features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method, which turned out to be critical for the performance of Procleave. The optimal approximations of all structural parameter values were encoded in a conditional random field (CRF) computational framework, alongside sequence and chemical group-based features. Here, we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent tests. Procleave is capable of correctly identifying most cleavage sites in the case study. Importantly, when applied to the human structural proteome encompassing 17,628 protein structures, Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different proteases. Procleave is implemented as a webserver and is freely accessible at http://procleave.erc.monash.edu/ .