Deep Learning-Driven Optimization of Antihypertensive Properties from Whey Protein Hydrolysates: A Multienzyme Approach.

This study utilized deep learning to optimize antihypertensive peptides from whey protein hydrolysate. Using the Large Language Models (LLMs), we identified an optimal multienzyme combination (MC5) with an ACE inhibition rate of 89.08% at a concentration of 1 mg/mL, significantly higher than single-enzyme hydrolysis. MC5 (1 mg/mL) exhibited excellent biological stability, with the ACE inhibition decreasing by only 6.87% after simulated digestion. In in vivo experiments, MC5 reduced the systolic and diastolic blood pressure of hypertensive rats to 125.00 and 89.00 mmHg, respectively. MC5 significantly lowered inflammatory markers (TNF-α and IL-6) and increased antioxidant enzyme activity (SOD, GSH-Px, GR, and CAT). Compared to the MC group, the MC5 group showed significantly reduced serum renin and ET-1 levels by 1.25-fold and 1.04-fold, respectively, while serum NO content increased by 3.15-fold. Furthermore, molecular docking revealed four potent peptides (LPEW, LKPTPEGDL, LNYW, and LLL) with high ACE binding affinity. This approach demonstrated the potential of combining computational methods with traditional hydrolysis processes to develop effective dietary interventions for hypertension.