Bitter molecules discovered through iterative virtual and in-vitro screening

The human bitter taste receptors (T2Rs) are a subfamily of G-protein coupled receptors responsible for detecting numerous diverse bitter molecules (as summarized in BitterDB ¹). Some T2Rs, such as the broadly tuned T2R14, also play physiological and pathological roles in extraoral compartments, therefore discovery of agonists and antagonists targeting T2Rs is important both for food research and for pharmacological applications.

Identification of new compounds targeting these receptors is a challenging task due to absent experimental 3D structures. Furthermore, T2Rs display a low sequence identity (~15%) with available GPCR templates, resulting in low resolution homology models. Nevertheless, research on ligand binding to these receptors can be successful when combining computational methods (homology modeling, docking, virtual screening) with experimental data ².

Here, we generated multiple T2R14 models using different methodologies (homology modelling, induced fit docking, normal mode analysis). The evaluation of the receptor model’s ability to discriminate between active compounds and a list of decoys has been performed using docking followed by calculation of statistical parameters (enrichment factors and ROC-AUC). The two homology models showing best performances in agonists or antagonists identification were selected and used to suggest new candidates through virtual screening of a large library of compounds. Some of the predicted agonists were experimentally confirmed to activate T2R14, using a cell-based functional assay, and testing of predicted antagonists is underway. This study a) provides a methodology to increase the homology models reliability through selection of models tailored for the required outcome (active model for agonists identification, inactive model for antagonists) b) offers novel compounds targeting T2R14.

[1] Dagan-Wiener, A., Di Pizio, A., Nissim, I., Bahia, M. S., Dubovski, N., Margulis, E., and Niv, M. Y. (2018), NucleicAcidsRes.

[2] Di Pizio, A., Waterloo, L. A., Brox, R., Löber, S., Weikert, D., Behrens, M., Gmeiner, P., and Niv, M. Y. (2019), CellMolLifeSci.