Zaverdas Harry, Stojceski Filip, Romero-Zaliz Rocío, Androutsos Lampros, Makrygiannis Pantelis, Pallante Lorenzo, Martos Vanessa, Grasso Gianvito, Deriu Marco A, Theofilatos Konstantinos, Mavroudi Seferina
InSyBio PC, Patras, Greece.
Department of Medicine, School of Health Sciences, University of Patras, Patras, Greece.
NPJ Sci Food. 2025 Jul 1;9(1):113. doi: 10.1038/s41538-025-00478-9.
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.
对驱动味觉感知的分子机制的理解可能对公共卫生产生广泛影响。本研究旨在通过解析味觉受体相互作用组来扩展对味觉受体相关分子途径的理解。为此，我们提出了一种综合机器学习方法，使用集成进化算法准确预测和量化蛋白质-蛋白质相互作用。利用61种功能直系同源、序列、共表达和结构特征，挖掘并表征了1,647,374个阳性和894,213个阴性经实验验证的蛋白质-蛋白质相互作用。二元分类器显著提高了现有方法的准确性，重建了完整的味觉受体相互作用组，并与估计阳性相互作用结合强度的回归器相结合。对得分最高的蛋白质-蛋白质相互作用进行分子动力学研究，验证了TAS2R41的新相互作用。重建的TR相互作用组可以促进对与味觉相关的分子病理生理机制的研究、美味营养密集型食品的开发以及个性化营养标志物的识别。
