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深度学习驱动优化乳清蛋白水解剂的抗高血压特性:一种多酶方法
Shuai Jiang1, Fan Mo1, Wenhan Li2
1College of Food Science and Light Industry, Nanjing Tech University, Nanjing 211816, China.
Journal of agricultural and food chemistry
|December 25, 2024
概括
深度学习确定了乳清蛋白中抗高血压的最佳酶组合. 这种新方法显著降低了高血压大鼠的血压和炎症.
科学领域:
- 生物技术是生物技术.
- 营养科学 营养科学
- 计算生物学 计算生物学
背景情况:
- 高血压是一个全球性的健康问题.
- 饮食质为血压管理提供了一个有前途的途径.
- 优化的生产需要先进的方法.
研究的目的:
- 利用深度学习和大型语言模型 (LLM) 来优化乳清蛋白水解剂中的抗高血压.
- 为了评估优化组合的有效性和稳定性.
- 为了研究高血压大鼠模型中作用的潜在机制.
主要方法:
- 使用大型语言模型 (LLM) 来确定最佳的多酶组合 (MC5).
- 通过模拟消化评估了 ангиотензин转化酶 (ACE) 抑制率和生物稳定性.
- 对高血压大鼠进行了体内研究,测量血压,炎症标志物,抗氧化酶和关键血管活性物质.
- 进行分子对接以识别特定的高亲和度结合.
主要成果:
- MC5实现了89.08%的ACE抑制率,显著超过单酶水解.
- MC5表现出极好的生物稳定性,在消化后ACE抑制仅下降6.87%.
- 在体内研究表明,MC5降低了心压和腹压血压,分别为125.00和89.00 mmHg.
- MC5调节了炎症标志物,增强了抗氧化酶活性,并有利地改变了氨酸- ангиотензин-阿尔多斯特系统 (RAAS) 组成部分和氧化 (NO) 水平.
- 通过分子对接识别了四种强大的抗高血压 (LPEW,LKPTPEGDL,LNYW,LLL).
结论:
- 深度学习和LLM是优化化中的酶组合用于抗高血压应用的有效工具.
- 优化的乳清蛋白水解剂 (MC5) 显示出作为高血压的饮食干预措施的巨大潜力.
- 这种结合计算方法和酶解水解的综合方法为开发功能性食品和营养药品提供了一种新的策略.
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