Decoding polyphenol-protein interactions with deep learning: From molecular mechanisms to food applications.

Area of Science:

• Biochemistry
• Computational Biology
• Food Science

Background:

• Polyphenol-Protein Interactions (PhPIs) are vital for food functionality, nutrient bioavailability, antioxidant activity, and therapeutic efficacy.
• Studying PhPIs is complex due to the structural diversity of polyphenols and the dynamic nature of protein binding.
• Traditional experimental (NMR, MS) and computational (docking, MD) methods offer insights but have limitations in scalability, throughput, and reproducibility.

Purpose of the Study:

• To review the application of deep learning (DL) in studying PhPIs.
• To explore how DL enables efficient prediction of binding sites, interaction affinities, and molecular dynamics using bio- and cheminformatics data.
• To identify current limitations and future directions for DL in PhPIs research.

Main Methods:

• Review of existing literature on DL applications in PhPIs analysis.
• Assessment of DL frameworks for predicting binding sites, affinities, and molecular dynamics.
• Critical evaluation of data requirements and model generalizability.

Main Results:

• DL significantly enhances prediction accuracy and reduces experimental redundancy in PhPIs studies.
• DL leverages high-dimensional bio- and cheminformatics data for efficient analysis.
• DL's effectiveness is currently constrained by data availability, quality, and representativeness, particularly for natural products.

Conclusions:

• DL is a transformative tool for understanding PhPIs, accelerating discoveries in nutritional science and therapeutic development.
• Future research should focus on multimodal data integration, improving model generalizability, and creating domain-specific benchmark datasets.
• Addressing data limitations is crucial for unlocking the full potential of DL in PhPIs research.