Unveiling novel antioxidant peptides from silk fibroin proteins: An integrated in silico and in vitro study

Antioxidant peptides exhibit significant potential in combating degenerative diseases by effectively mitigating oxidative stress. In this study, we developed a machine-learning model for screening antioxidant peptides, achieving a Matthews correlation coefficient of 0.892 ± 0.033 and surpassing the state-of-the-art (SOTA) models. Through in silico screening, seven novel antioxidant peptides derived from silk fibroin proteins (SFP) were identified (i.e., DEDY, NEEY, GAGRGY, ITRNHDQCR, VDHNL, QGDY, and DDY) and subsequently synthesized. Among them, all except for GAGRGY and QGDY demonstrated notable antioxidant activity in ABTS free radical assays, which were 1.26–3.25 times higher than that of glutathione. All seven antioxidant peptides effectively protected erythrocytes from oxidative damage. This protective capacity is likely attributed to their ability to bind free radicals and regulate the Keap1-Nrf2 pathway. Overall, this study presents an effective strategy for discovering antioxidant peptides from SFP and provides strong experimental validation for testing the effectiveness of the machine learning model.