A prediction method of interaction based on Bilinear Attention Networks for designing polyphenol-protein complexes delivery systems

Polyphenol-protein complexes delivery systems are gaining attention for their potential health benefits and food industry development. However, creating an ideal delivery system requires extensive wet-lab experimentation. To address this, we collected 525 ligand-protein interaction data pairs and established an interaction prediction model using Bilinear Attention Networks. We utilized 10-fold cross validation to address potential overfitting issues in the model, resulting in showed higher average AUROC (0.8443), AUPRC (0.7872), and F1 (0.8164). The optimal threshold (0.3739) was selected for the model to be used for subsequent analysis. Based on the model prediction results and optimal threshold, by verifying experimental analysis, the interaction of paeonol with the following proteins was obtained, including bovine serum albumin (lgK_a = 6.2759), bovine β-lactoglobulin (lgK_a = 6.7479), egg ovalbumin (lgK_a = 5.1806), zein (lgK_a = 6.0122), bovine α-lactalbumin (lgK_a = 3.9170), bovine lactoferrin (lgK_a = 4.5380), the first four proteins are consistent with the predicted results of the model, with lgK_a >5. The established model can accurately and rapidly predict the interaction of polyphenol-protein complexes. This study is the first to combine open ligand-protein interaction experiments with Deep Learning algorithms in the food industry, greatly improving research efficiency and providing a novel perspective for future complex delivery system construction.

Dataset; Deep Learning; Delivery systems; Interaction; Polyphenol-protein complexes.