Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions
Abstract The understanding of the molecular mechanisms that drive taste perception can have broad implications for public health. This study aims to expand the understanding of taste receptor-associated molecular pathways by resolving the taste receptor interactome. To this end, we propose a compreh...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Science of Food |
| Online Access: | https://doi.org/10.1038/s41538-025-00478-9 |
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| author | Harry Zaverdas Filip Stojceski Rocío Romero-Zaliz Lampros Androutsos Pantelis Makrygiannis Lorenzo Pallante Vanessa Martos Gianvito Grasso Marco A. Deriu Konstantinos Theofilatos Seferina Mavroudi |
| author_facet | Harry Zaverdas Filip Stojceski Rocío Romero-Zaliz Lampros Androutsos Pantelis Makrygiannis Lorenzo Pallante Vanessa Martos Gianvito Grasso Marco A. Deriu Konstantinos Theofilatos Seferina Mavroudi |
| author_sort | Harry Zaverdas |
| collection | DOAJ |
| description | Abstract The understanding of the molecular mechanisms that drive taste perception can have broad implications for public health. This study aims to expand the understanding of taste receptor-associated molecular pathways by resolving the taste receptor interactome. To this end, we propose a comprehensive machine learning approach to accurately predict and quantify protein-protein interactions using an ensemble evolutionary algorithm. 1,647,374 positive and 894,213 negative experimentally verified protein-protein interactions were mined and characterized using 61 functional orthology, sequence, co-expression and structural features. The binary classifier significantly improved the accuracy of existing methods, reconstructing the full taste receptor interactome and was combined with a regressor that estimates the binding strength of positive interactions. Molecular dynamics investigation of top-scoring protein-protein interactions verified novel interactions of TAS2R41. The reconstructed TR interactome can catalyze the study of molecular pathophysiological mechanisms related to taste, the development of flavorsome nutrient-dense food products and the identification of personalized nutrition markers. |
| format | Article |
| id | doaj-art-d439cde5ffac4e16b8ba295eb0abb50c |
| institution | Kabale University |
| issn | 2396-8370 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Science of Food |
| spelling | doaj-art-d439cde5ffac4e16b8ba295eb0abb50c2025-08-20T04:01:42ZengNature Portfolionpj Science of Food2396-83702025-07-019111610.1038/s41538-025-00478-9Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactionsHarry Zaverdas0Filip Stojceski1Rocío Romero-Zaliz2Lampros Androutsos3Pantelis Makrygiannis4Lorenzo Pallante5Vanessa Martos6Gianvito Grasso7Marco A. Deriu8Konstantinos Theofilatos9Seferina Mavroudi10InSyBio PCDalle Molle Institute for Artificial Intelligence USI-SUPSI, Polo universitario Lugano—Campus EstDepartment of Computer Science and AI, Research Center in Information and Communication Technologies (CITIC), Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of GranadaInSyBio PCInSyBio PCPolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di TorinoDepartment of Plant Physiology, University of GranadaDalle Molle Institute for Artificial Intelligence USI-SUPSI, Polo universitario Lugano—Campus EstPolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di TorinoSchool of Cardiovascular and Metabolic Medicine & Sciences, King’s College LondonDepartment of Nursing, School of Health Rehabilitation Sciences, University of PatrasAbstract The understanding of the molecular mechanisms that drive taste perception can have broad implications for public health. This study aims to expand the understanding of taste receptor-associated molecular pathways by resolving the taste receptor interactome. To this end, we propose a comprehensive machine learning approach to accurately predict and quantify protein-protein interactions using an ensemble evolutionary algorithm. 1,647,374 positive and 894,213 negative experimentally verified protein-protein interactions were mined and characterized using 61 functional orthology, sequence, co-expression and structural features. The binary classifier significantly improved the accuracy of existing methods, reconstructing the full taste receptor interactome and was combined with a regressor that estimates the binding strength of positive interactions. Molecular dynamics investigation of top-scoring protein-protein interactions verified novel interactions of TAS2R41. The reconstructed TR interactome can catalyze the study of molecular pathophysiological mechanisms related to taste, the development of flavorsome nutrient-dense food products and the identification of personalized nutrition markers.https://doi.org/10.1038/s41538-025-00478-9 |
| spellingShingle | Harry Zaverdas Filip Stojceski Rocío Romero-Zaliz Lampros Androutsos Pantelis Makrygiannis Lorenzo Pallante Vanessa Martos Gianvito Grasso Marco A. Deriu Konstantinos Theofilatos Seferina Mavroudi Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions npj Science of Food |
| title | Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions |
| title_full | Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions |
| title_fullStr | Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions |
| title_full_unstemmed | Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions |
| title_short | Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions |
| title_sort | unravelling the human taste receptor interactome machine learning and molecular modelling insights into protein protein interactions |
| url | https://doi.org/10.1038/s41538-025-00478-9 |
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