Basic taste qualities like sour, salty, sweet, bitter and umami serve specific functions in identifying food components found in the diet of humans and animals, and are recognized by proteins in the oral cavity. Recognition of bitter taste and aversion to it are thought to protect the organism against the ingestion of poisonous food compounds, which are often bitter. Interestingly, bitter taste receptors are expressed not only in the mouth but also in extraoral tissues, such as the gastrointestinal tract, indicating that they may play a role in digestive and metabolic processes. BitterDB database, available at http://bitterdb.agri.huji.ac.il/bitterdb/, includes over 550 compounds that were reported to taste bitter to humans. The compounds can be searched by name, chemical structure, similarity to other bitter compounds, association with a particular human bitter taste receptor, and so on. The database also contains information on mutations in bitter taste receptors that were shown to influence receptor activation by bitter compounds. The aim of BitterDB is to facilitate studying the chemical features associated with bitterness. These studies may contribute to predicting bitterness of unknown compounds, predicting ligands for bitter receptors from different species and rational design of bitterness modulators.

BitterDB (http://bitterdb.agri.huji.ac.il) was introduced in 2012 as a central resource for information on bitter-tasting molecules and their receptors. The information in BitterDB is frequently used for choosing suitable ligands for experimental studies, for developing bitterness predictors, for analysis of receptors promiscuity and more. Here, we describe a major upgrade of the database, including significant increase in content as well as new features. BitterDB now holds over 1000 bitter molecules, up from the initial 550. When available, quantitative sensory data on bitterness intensity as well as toxicity information were added. For 270 molecules, at least one associated bitter taste receptor (T2R) is reported. The overall number of ligand–T2R associations is now close to 800. BitterDB was extended to several species: in addition to human, it now holds information on mouse, cat and chicken T2Rs, and the compounds that activate them. BitterDB now provides a unique platform for structure-based studies with high-quality homology models, known ligands, and for the human receptors also data from mutagenesis experiments, information on frequently occurring single nucleotide polymorphisms and links to expression levels in different tissues.

Summary: PDZBase is a database that aims to contain all known PDZ-domain-mediated protein–protein interactions. Currently, PDZBase contains approximately 300 such interactions, which have been manually extracted from >200 articles. The database can be queried through both sequence motif and keyword-based searches, and the sequences of interacting proteins can be visually inspected through alignments (for the comparison of several interactions), or as residue-based diagrams including schematic secondary structure information (for individual complexes). Availability: http://icb.med.cornell.edu/services/pdz/start. Contact: pdzbase@med.cornell.edu.

An important question in taste research is how 25 receptors of the human TAS2R family detect thousands of structurally diverse compounds. An answer to this question may arise from the observation that TAS2Rs in general are broadly tuned to interact with numerous substances. Ultimately, interaction with chemically diverse agonists requires architectures of binding pockets tailored to combine flexibility with selectivity. The present study determines the structure of hTAS2R binding pockets. We focused on a subfamily of closely related hTAS2Rs exhibiting pronounced amino acid sequence identities but unique agonist activation spectra. The generation of chimeric and mutant receptors followed by calcium imaging analyses identified receptor regions and amino acid residues critical for activation of hTAS2R46, -R43, and -R31. We found that the carboxyl-terminal regions of the investigated receptors are crucial for agonist selectivity. Intriguingly, exchanging two residues located in transmembrane domain seven between hTAS2R46, activated by strychnine, and hTAS2R31, activated by aristolochic acid, was sufficient to invert agonist selectivity. Further mutagenesis revealed additional positions involved in agonist interaction. The transfer of functionally relevant amino acids identified in hTAS2R46 to the corresponding positions of hTAS2R43 and -R31 resulted in pharmacological properties indistinguishable from the parental hTAS2R46. In silico modeling of hTAS2R46 allowed us to visualize the putative mode of interaction between agonists and hTAS2Rs. Detailed structure-function analyses of hTAS2Rs may ultimately pave the way for the development of specific antagonists urgently needed for more sophisticated analyses of human bitter taste perception.

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.