The sweet taste receptor, a heterodimeric G protein coupled receptor (GPCR) protein, formed by the T1R2 and T1R3 subunits, recognizes several sweet compounds including carbohydrates, amino acids, peptides, proteins, and synthetic sweeteners. Its similarity with the metabotropic glutamate mGluR1 receptor allowed us to build homology models. All possible dimers formed by combinations of the human T1R2 and T1R3 subunits, modeled on the A (closed) or B (open) chains of the extracellular ligand binding domain of the mGluR1 template, yield four ligand binding sites for low-molecular-weight sweeteners. These sites were probed by docking a set of molecules representative of all classes of sweet compounds and calculating the free energy of ligand binding. These sites are not easily accessible to sweet proteins, but docking experiments in silico showed that sweet proteins can bind to a secondary site without entering the deep cleft. Our models account for many experimental observations on the tastes of sweeteners, including sweetness synergy, and can help to design new sweeteners.

Bitter taste is an innately aversive taste modality that is considered to protect animals from consuming toxic compounds. Yet, bitterness is not always noxious and some bitter compounds have beneficial effects on health. Hundreds of bitter compounds were reported (and are accessible via the BitterDB http://bitterdb.agri.huji.ac.il/dbbitter.php), but numerous additional bitter molecules are still unknown. The dramatic chemical diversity of bitterants makes bitterness prediction a difficult task. Here we present a machine learning classifier, BitterPredict, which predicts whether a compound is bitter or not, based on its chemical structure. BitterDB was used as the positive set, and non-bitter molecules were gathered from literature to create the negative set. Adaptive Boosting (AdaBoost), based on decision trees machine-learning algorithm was applied to molecules that were represented using physicochemical and ADME/Tox descriptors. BitterPredict correctly classifies over 80% of the compounds in the hold-out test set, and 70–90% of the compounds in three independent external sets and in sensory test validation, providing a quick and reliable tool for classifying large sets of compounds into bitter and non-bitter groups. BitterPredict suggests that about 40% of random molecules, and a large portion (66%) of clinical and experimental drugs, and of natural products (77%) are bitter.

The hypoglycemic effect in humans of Moringa oleifera (MO) leaf powder has, to date, been poorly investigated. We assessed the chemical composition of MO leaf powder produced at Saharawi refugee camps, its in vitro ability to inhibit α-amylase activity, and its sensory acceptability in food. We then evaluated its effect on postprandial glucose response by randomly administering, on 2 different days, a traditional meal supplemented with 20 g of MO leaf powder (MOR20), or not (control meal, CNT), to 17 Saharawi diabetics and 10 healthy subjects. Capillary glycaemia was measured immediately before the meal and then at 30 min intervals for 3 h. In the diabetic subjects the postprandial glucose response peaked earlier with MOR20 compared to CNT and with lower increments at 90, 120, and 150 min. The mean glycemic meal response with MOR20 was lower than with CNT. The healthy subjects showed no differences. Thus, MO leaf powder could be a hypoglycemic herbal drug. However, given the poor taste acceptability of the 20 g MO meal, lower doses should be evaluated. Moreover, the hypoglycemic effects of MO leaf powder should also be demonstrated by trials evaluating its long-term effects on glycaemia.

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Propolis is used as folk medicine due to its spectrum of alleged biological and pharmaceutical properties and it is a complex matrix not still totally characterized. Two batches of propolis coming from two different environments (plains of Po Valley and the hilly Ligurian–Piedmont Apennines) of Northern Italy were characterized using different analytical methods: Spectrophotometric analysis of phenols, flavones and flavonols, and DPPH radical scavenging activity, HPLC, NMR, HSPME and GC–MS and HPLC–MS Orbitrap. Balsam and moisture content were also considered. No statistical differences were found at the spectrophotometric analysis; balsam content did not vary significantly. The most interesting findings were in the VOCs composition, with the Po Valley samples containing compounds of the resins from leaf buds of Populus nigra L. The hills (Appennines) samples were indeed characterize by the presence of phenolic glycerides already found in mountain environments. HPLC–Q-Exactive-Orbitrap®–MS analysis is crucial in appropriate recognition of evaluate number of metabolites, but also NMR itself could give more detailed information especially when isomeric compounds should be identified. It is necessary a standardized evaluation to protect and valorize this production and more research on propolis characterization using different analytical techniques.