A breakthrough computational strategy for efficient enzymatic digestion of walnut protein to prepare antioxidant peptides

The conventional natural bioactive peptides (NBAPs) enzymatic preparation process is labor-intensive and time-consuming, limiting its application and development. This study proposes an efficient computational strategy (CAE-VD) that integrates a high-accuracy (98.18 %) deep learning model (convolutional auto-encoder, CAE) with virtual digestion (VD) to prepare NBAPs with specified activities. CAE predicts the activity of peptides generated by VD, guiding enzyme selection. CAE-VD identified alkaline protease as the most suitable enzyme for enzymatic preparation of walnut-derived antioxidant peptides compared to pepsin and trypsin, which was confirmed by DPPH and ABTS radical scavenging assays and statistical analyses of peptides. As an emerging computer technology, CAE-VD will apply to other NBAPs. This study demonstrates the efficacy of integrating deep learning with virtual digestion in guiding the enzymatic preparation of NBAPs and highlights the potential of applying advanced computational techniques in the food industry.