Diabetes is a complex metabolic disorder affecting an ever-increasing population. Molecular mimics of glucagon-like peptide 1 (GLP-1), which activate the pancreatic GLP-1 receptor (GLP-1R) to stimulate insulin secretion, are commonly prescribed to treat type 2 diabetes and obesity. Despite the positive outcomes associated with GLP-1R agonists, they have also been linked to nausea, vomiting, allergic reactions, and pancreatitis. Bitter taste receptor 38 (TAS2R38), an upstream modulator of endogenous GLP-1 release, is thus a drug target for type 2 diabetes and obesity. Activation of gut-localized TAS2R38 correlates with a significant increase in the blood concentration of GLP-1 in mice. We aim to identify novel agonists via a virtual ligand screening workflow utilizing molecular dynamics simulations to sample receptor conformations. Patients underserved by limited variety in available drugs may benefit from this alternative approach to treating metabolic disease through TAS2R38 agonism.
Dylan intends to achieve the following milestones:
- Validate TAS2R38 models by docking a library of actives and property-matched decoys and analyzing dock score using ROC-AUC analysis.
- Identify lead compounds (TAS2R38 agonists) via virtual screening a library of fragment compounds against validated receptor model(s).
- Identify hit compounds from a lead-optimized screening library built around the lead compounds’ pharmacophores.