Decoding Polyphenol-Protein Interactions with Deep Learning: From Molecular Mechanisms to Food Applications

TL;DR

This paper reviews how deep learning techniques are transforming the prediction and analysis of polyphenol-protein interactions, which are crucial for food functionality and health, by improving accuracy and scalability over traditional methods.

Contribution

It critically assesses current deep learning frameworks for polyphenol-protein interactions and proposes future directions including multimodal data integration and benchmark datasets.

Findings

Deep learning improves prediction accuracy of PhPIs.

DL reduces experimental redundancy in studying PhPIs.

Limitations include data availability and quality issues.

Abstract

Polyphenols and proteins are essential biomolecules that influence food functionality and, by extension, human health. Their interactions -- hereafter referred to as PhPIs (polyphenol-protein interactions) -- affect key processes such as nutrient bioavailability, antioxidant activity, and therapeutic efficacy. However, these interactions remain challenging due to the structural diversity of polyphenols and the dynamic nature of protein binding. Traditional experimental techniques like nuclear magnetic resonance (NMR) and mass spectrometry (MS), along with computational tools such as molecular docking and molecular dynamics (MD), have offered important insights but face constraints in scalability, throughput, and reproducibility. This review explores how deep learning (DL) is reshaping the study of PhPIs by enabling efficient prediction of binding sites, interaction affinities, and MD using high-dimensional bio- and chem-informatics data. While DL enhances prediction accuracy and reduces experimental redundancy, its effectiveness remains limited by data availability, quality, and representativeness, particularly in the context of natural products. We critically assess current DL frameworks for PhPIs analysis and outline future directions, including multimodal data integration, improved model generalizability, and development of domain-specific benchmark datasets. This synthesis offers guidance for researchers aiming to apply DL in unraveling structure-function relationships of polyphenols, accelerating discovery in nutritional science and therapeutic development.