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 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

SUBMITTER: Zaverdas H 

PROVIDER: S-EPMC12217742 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature