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

## TL;DR

This study uses machine learning and molecular modeling to map taste receptor interactions, offering insights into taste perception and potential applications in nutrition.

## Contribution

A novel machine learning approach combining evolutionary algorithms and molecular dynamics to predict and validate taste receptor protein-protein interactions.

## Key findings

- A binary classifier improved accuracy in predicting protein-protein interactions for taste receptors.
- Molecular dynamics confirmed novel interactions involving the TAS2R41 receptor.
- The reconstructed interactome can aid in understanding taste-related pathophysiology and personalized nutrition.

## 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.