Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions.

Journal: NPJ science of food
Published Date: Jul 1, 2025

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.

Authors

  • Harry Zaverdas
    InSyBio PC, Patras, Greece.
  • Filip Stojceski
    Dalle Molle Institute for Artificial Intelligence USI-SUPSI, Polo universitario Lugano-Campus Est, Lugano, Switzerland.
  • Rocío Romero-Zaliz
    Department 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 Granada, Granada, Spain.
  • Lampros Androutsos
    InSyBio PC, Patras, Greece.
  • Pantelis Makrygiannis
    InSyBio PC, Patras, Greece.
  • Lorenzo Pallante
    PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy.
  • Vanessa Martos
    Department of Plant Physiology, University of Granada, Granada, Spain.
  • Gianvito Grasso
    Dalle Molle Institute for Artificial Intelligence USI-SUPSI, Polo universitario Lugano-Campus Est, Lugano, Switzerland.
  • Marco A Deriu
    PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy.
  • Konstantinos Theofilatos
    King's British Heart Foundation Centre, King's College London, London, UK.
  • Seferina Mavroudi
    Department of Nursing, School of Health Rehabilitation Sciences, University of Patras, Patras, Greece.

Keywords

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