Most babies can be described as “sweet” – meaning that they look cute. But that isn’t the only reason: Soon after birth, babies show a preference for sweet solutions, such as breast milk. When they grow older, most children – as one can notice from their demands for purchases when taken to a supermarket – crave candy and desserts, so much so that their parents worry about how much sugar they ingest. This inclination carries on in most adults.
Taste buds on the tip of the tongue are sensors that detect sugar and other sweet things in foods; when any type of sugar touches the tongue, the taste buds send a signal to the brain, which reacts by giving us a sense of pleasure by stimulating the “feel-good” neurotransmitters called dopamine and serotonin. This is apparently an evolutionary urge, since human hunter-gatherers predisposed to eat sweet foods probably had a better chance of survival. Sweets are probably addictive, as eating them activates the same receptors in your brain that hard drugs do.
We are likely to be “programmed” for the degree of how much we like sweets, because scientists have found that genetic factors account for about 30% of the variance in sweet taste perception among people for both natural and artificial sugars.
But since excessive sweets can lead to overweight, dental cavities and metabolic disease, scientists have long been looking for natural and artificial compounds that have a sweet taste but do not harm health. Stevia, a natural sweetener and sugar substitute derived from the leaves of the plant species Stevia rebaudiana that is native to certain parts of South America, has 30 to 150 times the sweetness of sugar and does not add calories, but some people object to an aftertaste, and chemical sweeteners are controversial. Thus the search for a better sweetener that is not harmful to health goes on.
Now, a three-dimensional model of the receptor responsible for sensing the sweet taste in the tongue has been constructed in lab of Prof. Masha Niv at the Hebrew University of Jerusalem’s Faculty of Agriculture. She and her team discovered that the model makes possible the scanning of huge databases to extract sweet compounds much faster than existing technologies.
The search for “the next stevia” is underway, with many researchers investing their efforts in locating natural, inexpensive, sugar-free, low-calorie sweeteners with no aftertaste. The biggest obstacle is the time it takes to find such new sweeteners. In addition, the brain receptor’s spatial structure for sweet taste has not been revealed to date. Therefore, creativity is needed to find new compounds that can create the desired taste.
In a recent study titled “Structure-based screening for discovery of sweet compounds” published with numerous diagrams in the scientific journal Food Chemistry, Niv of the Agriculture Faculty’s Institute of Biochemistry, Food and Nutrition and her doctoral student Yaron Ben Shoshan-Galeczky developed a computerized, 3D model of the receptor responsible for sensing the sweet taste.
“Because the sweet-taste receptor structure has not been experimentally solved yet, a possible approach to finding sweet molecules is virtual screening using compatibility of candidate molecules to… models of the sugar-binding site,” they wrote.
In a computational method, these materials are spatially suitable to find the link in the receptor model. The most reliable spatial model chosen is the one that was able to extract known sweeteners, but not “distracting” substances (similar in spatial form to sweet but not actually sweet). Using such a model in particular –and computational methods in general – made it possible for the team to scan at high speeds huge databases to extract sweet compounds.
In the next phase of the study, the 3D model was used to scan an electronic “library” containing some 40,000 substances in food. For the 400 materials best-matched to the receptor, databases and patents were searched. From this search it emerged that for dozens of the materials discovered in the scan, patent applications were recently filed as sweeteners. This indicates the reliability of the model and its ability to classify materials as sweet from a very large database.
According to the researchers, “such studies, which are based on computational methods that are currently used mainly in drug development significantly reduce the amount of resources and time invested in the search for new sweeteners. and this is the most significant result of this study.”