What is the purpose of SuperSweet?
The perception of sweet taste, mainly associated with advantageous food, has had an important evolutionary influence on different physiological
regulation mechanisms. Today, the replacement of sugar and other carbohydrates by e.g. artificial sweetener as dominating component in food is a
general goal to prevent caries, obesity and associated diseases. On the other hand, carbohydrates play an important role in immune recognition
and tumour response.
Currently, the sweetness receptor, which is a heterodimer of two transmembrane proteins and has three different binding sites, has not been
crystallized and is therefore unavailable in the Protein Data Bank.
is not only a comprehensive knowledge base for existing carbohydrates and artificial
sweeteners but also includes a homology modelled sweet taste receptor and docked binders.
The understanding of compounds binding to the receptor is of relevance not only for the development of safe sweeteners but also because
their chemical structures range from small molecules to proteins.
Additionally, the database consists of nearly 22.000 carbohydrates and carbohydrate-like structures and sweeteners as well as sweet-like compounds. For each of these compounds structural and physico-chemical properties such as molecular weight, hydrogen bonds and logP value, glycaemic index or sweetness class are available as well as their toxicity profile and predicted targets.
A user-friendly graphical interface allows similiarity searching, visualization of docked sweeteners into the modelled receptor etc. Furthermore, it's possible to search for compounds based on their physico-chemical properties as well as their toxicity class.
A carbohydrate classification tree and the sweetoscope with our top 20 sweeteners allows quick browsing through the database.
How do we define a molecule as being a sweet molecule?
A base set of compounds were identified from Pubchem which were annotated as being sweet. Addional compounds were also found by manual literature searching. The dataset was then expanded by searching for similar compounds using Tanimoto fingerprints.
What information can I get for a compound?
Besides chemical and physicochemical properties of the sweetening compounds the
also stores information about the sweetness, the calories,
therapeutic effect and the metabolism as well as predicted targets and toxicity. The sweetness is stated by the sweetness index which is relative to saccharose ( having an index of 1).
The calories are indicated by the kcal/100g.
Additionally, the mol-Structures or PDB-Files are also available in SuperSweet
What is a Tanimoto Coefficient?
A simple count of shared features (common fragment substructures) can be a measure of
chemical distance when used in some similarity coefficient. Dictionaries of predefined structural
fragments, such as MDL Information Systems MACCS keys, are used to identify features
contained in a molecule. The structural fragments or features that are present in the given
molecule are turned ON (set as 1) and the ones that are absent are kept OFF (set as 0). Thus, for
each molecule one ends up having a string containing 1s and 0s (bit string).
Once the molecules have been represented by such bit-strings the Tanimoto Coefficient can be used
as a measure to assess similarity.
Let's say we are comparing two molecules A and B. If NA is
number of features (ON bits) in A, NB is the number of features (ON bits) in B, and NAB is the
number of features (ON bits) common in both A and B, then, the Tanimoto Coefficient is:
Note that the OFF bits do not determine the similarity. In other words, if some molecular features
are absent in both molecules then that is not taken as an indication of similarity between the two.
What are sweet-like compounds?
Compounds in the class "Sweet-like" where found by doing an extensive similarity search using two houndred compounds as a base which are known to be sweet. Only those compounds with a structural similarity of 95% or more were considered for this class.
How are the compounds classified and how can I use the sweet tree?
A carbohydrate classification tree and the browsing feature allow quick requests to be made to the database. The sweetener classification tree consists in the first step of three different main classes: carbohydrates, peptides and small molecules. By clicking on one of these classes the user gets the sub-classifications. Clicking on the last tree-level leads to detailed information for all structures belonging to this class.
How was the 3D structure of the sweetness receptor generated?
A model of the sweet taste receptr (SR) was built using an active form of mGluR1 as a template (PDB code: 1EWK
). A multiple sequence alignment was created using MUSCLE
. The alignment can be downloaded here
. Homology modelling was carried out using Modeller in Accelrys Discovery Studio 2.5. T1R2 was built using chain A of 1EWK and T1R3 using chain B. The large insertions in T1R2 and T1R3 compared to the template were removed from the final model. Lastly, clashes were removed from sidechains and the structure minimized by carrying out 100 steps of both steepest descent and conjugant gradient minimization.
How were the small molecules docked onto the sweet taste receptor model?
Docking of the small compounds into the homology modeled receptor was done using the docking program GOLD 4.1.1. In order to define the binding site of the sweet taste receptor, the template structure (mGluR1 containing a glutamate bound to each chain) was superimposed onto the homology model of the sweet taste receptor and the glutamate molecules copied over to the homology model. The binding sites of the sweet taste receptor were then defined by using the glutamate molecules as reference ligands; all atoms within 5Ã… of the glutamate molecule formed the binding sites for the docking experiments. For each small molecule, 100 docking runs were performed. A previous docking study showed that the sweet taste receptorâ€™s active site in the closed protomer is too small to host some of the larger synthetic sweeteners and is only able to host four compounds out of those tested: saccharin, alitame, aspartame and 6-Cl-tryptophan (Morini et al., 2005
). Experimental work has shown that aspartame and neotame bind to the T1R2 subunit (Xu et al., 2004
). In accordance with these findings, we therefore docked molecules with a molecular weight greater than 400 kDa into T1R3 (open form) and all other molecules into the pockets of both T1R2 and T1R3. The resulting docking poses were then ranked using the GoldScore fitness function. The best scoring docking pose for each molecule can be viewed using a Jmol applet and the respective structure files are also available for download.
How were the toxicity classes of the compounds computed?
ProTox (M. Drwal, P. Banerjee, M. Dunkel, M. Wettig and R. Preissner, (NAR) 2014
) is the first freely available web server for toxicity prediction method based on chemical similarity and the identification of toxic fragments and demonstrates good performance in comparison to available QSAR-based methods. ProTox prediccts the median oral lethal doses (LD50 values) and toxicity classes in rodents. In addition to the oral toxicity prediction, the web server indicates possible toxicity targets based on a collection of protein-ligand-based pharmacophores ('toxicophores') and therefore provides suggestions for the mechanism of toxicity development. SuperSweet database contains the toxicity class for each compound predicted using ProTox methodology. However, the absence of such toxicity prediction or alert for a compound should not be taken as an indication of safety.
No. of compounds per tox class in SuperSweet
|Tox Class 1||1
|Tox Class 2||326
|Tox Class 3||185
|Tox Class 4||1328
|Tox Class 5||5720
|Tox Class 6||12796
How were the targets computed?
The target prediction is based on similarity distribution among the ligand of the molecular targets and was computed using SuperPred web server (Nickel J., Gohlke B.-O., Ehreman J., Banerjee P., Rong W.W., Goede A., Dunkel M. and Preissner R., (NAR) 2014
), which has an overall prediction accuracy of 75.1% and includes e-values for each prediction. The distributions of ligand similarity scores are utilized for estimating individual thresholds probabilities for a specific target. The extended connectivity fingerprints (ECFP4) were calculated for the database molecules in order to compute the target prediction. All the SuperSweet compounds were screened against a chemical space containing 1800 mammalian proteins, 361,000 chemical compounds and 665,000 compound-target interactions. The target prediction was successfully achieved for 15,309 compounds in the SuperSweet database. Examples of some of the sweetening agents associated that may interact with cytochrome P450 family of enzymes as predicted includes: aspartame, neotame, aspartam-acesulfame salt, advantame.