Automated identification of the genetic variants of TAS2R38 bitter taste receptor with supervised learning

Lala Chaimae Naciri; Mariano Mastinu; Roberto Crnjar; Iole Tomassini Barbarossa; Melania Melis

doi:10.1016/j.csbj.2023.01.029

Automated identification of the genetic variants of TAS2R38 bitter taste receptor with supervised learning

Comput Struct Biotechnol J. 2023 Jan 22:21:1054-1065. doi: 10.1016/j.csbj.2023.01.029. eCollection 2023.

Authors

Lala Chaimae Naciri¹, Mariano Mastinu¹, Roberto Crnjar¹, Iole Tomassini Barbarossa¹, Melania Melis¹

Affiliation

¹ Department of Biomedical Sciences, University of Cagliari, Monserrato, CA 09042, Italy.

Abstract

Several studies were focused on the genetic ability to taste the bitter compound 6-n-propylthiouracil (PROP) to assess the inter-individual taste variability in humans, and its effect on food predilections, nutrition, and health. PROP taste sensitivity and that of other chemical molecules throughout the body are mediated by the bitter receptor TAS2R38, and their variability is significantly associated with TAS2R38 genetic variants. We recently automatically identified PROP phenotypes with high precision using Machine Learning (mL). Here we have used Supervised Learning (SL) algorithms to automatically identify TAS2R38 genotypes by using the biological features of eighty-four participants. The catBoost algorithm was the best-suited model for the automatic discrimination of the genotypes. It allowed us to automatically predict the identification of genotypes and precisely define the effectiveness and impact of each feature. The ratings of perceived intensity for PROP solutions (0.32 and 0.032 mM) and medium taster (MT) category were the most important features in training the model and understanding the difference between genotypes. Our findings suggest that SL may represent a trustworthy and objective tool for identifying TAS2R38 variants which, reducing the costs and times of molecular analysis, can find wide application in taste physiology and medicine studies.

Keywords: Individual taste variability; PROP taster categories; Supervised Learning (SL); TAS2R38; Taste intensity Ratings.