Francesco Ferri
Explainable Prediction of Protein-Ligand Interaction in Bitter Taste using Classical Machine Learning and Graph Neural Networks.
Rel. Marco Agostino Deriu, Lorenzo Pallante, Marco Cannariato. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2024
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Abstract
Bitter taste is one of the five basic tastes that humans and other animals can perceive and is often linked to toxins and harmful compounds that can cause poisoning or disease. It involves the detection of diverse chemical compounds, such as alkaloids, phenols, and peptides, by a family of G protein-coupled receptors, known as taste receptor type 2 (TAS2R) or bitter taste receptors. Interestingly, TAS2Rs participate in the regulation of glucose homeostasis, modulation of immune and inflammatory responses, and may have implications for various diseases, such as obesity, diabetes, asthma, and cancer due to their expression in various extra-oral tissues. Therefore, they might represent promising therapeutic targets for several pathologies.
However, the available data on receptor-ligand associations is limited and incomplete, requiring a costly and time consuming in vitro screening of possible TAS2R ligands
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