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

Area of Science:

• Biotechnology
• Nutritional Science
• Computational Biology

Background:

• Hypertension is a global health concern.
• Dietary peptides offer a promising avenue for blood pressure management.
• Optimizing peptide production requires advanced methodologies.

Purpose of the Study:

• To leverage deep learning and Large Language Models (LLMs) for optimizing antihypertensive peptides from whey protein hydrolysate.
• To evaluate the efficacy and stability of the optimized peptide combination.
• To investigate the underlying mechanisms of action in a hypertensive rat model.

Main Methods:

• Utilized Large Language Models (LLMs) to determine an optimal multienzyme combination (MC5).
• Assessed Angiotensin-Converting Enzyme (ACE) inhibition rate and biological stability through simulated digestion.
• Conducted in vivo studies on hypertensive rats, measuring blood pressure, inflammatory markers, antioxidant enzymes, and key vasoactive substances.
• Performed molecular docking to identify specific high-affinity binding peptides.

Main Results:

• MC5 achieved an 89.08% ACE inhibition rate, significantly outperforming single-enzyme hydrolysis.
• MC5 demonstrated excellent biological stability, with only a 6.87% decrease in ACE inhibition post-digestion.
• In vivo studies showed MC5 reduced systolic and diastolic blood pressure to 125.00 and 89.00 mmHg, respectively.
• MC5 modulated inflammatory markers, enhanced antioxidant enzyme activity, and favorably altered renin-angiotensin-aldosterone system (RAAS) components and nitric oxide (NO) levels.
• Identified four potent antihypertensive peptides (LPEW, LKPTPEGDL, LNYW, LLL) via molecular docking.

Conclusions:

• Deep learning and LLMs are effective tools for optimizing enzyme combinations in peptide hydrolysis for antihypertensive applications.
• The optimized whey protein hydrolysate (MC5) shows significant potential as a dietary intervention for hypertension.
• This integrated approach combining computational methods and enzymatic hydrolysis offers a novel strategy for developing functional foods and nutraceuticals.