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通过改进的体学技术探索新型ANGICon-EIP:深度学习策略可以作为体结构和功能预测的核心突破吗?

Wei Jia¹, Jian Peng², Yan Zhang³

¹School of Food and Bioengineering, Shaanxi University of Science and Technology, Xi'an 710021, China; Inspection and Testing Center of Fuping County (Shaanxi goat milk product quality supervision and Inspection Center), Weinan 711700, China; Shaanxi Research Institute of Agricultural Products Processing Technology, Xi'an 710021, China.

Food research international (Ottawa, Ont.)

|November 21, 2023

概括

从山羊奶中发现新的抗高血压对于管理高血压至关重要. 深度学习模型在有效识别这些内源性血管新素-I转化酶抑制 (ANGICon-EIPs) 方面表现有前途.

科学领域:

生物化学和分子生物学
营养科学营养科学
生物信息学是一种生物信息学.

背景情况:

乳制品衍生的血管素-1转化酶抑制 (ANGICon-EIPs) 为高血压管理提供了一种安全的饮食方法.
短链,特别是来自山羊奶等来源的内源性,由于肠道吸收增加,具有显著的抗高血压潜力.
目前对内源性ANGICon-EIP的研究受限于提取和丰富的挑战.

研究的目的:

审查和概述先进的预处理策略,以发现新的内源性ANGICon-EIPs.
探索数据采集方法和用于预测结构和功能的计算工具.
突出深度学习在加速新型ANGICon-EIPs识别方面的潜力.

主要方法:

对改善的提取和丰富的治疗前策略的审查.
对生物活性的数据采集技术的分析.
评估深度学习算法,包括卷积神经网络 (CNN) 和多标签深度学习 (MLBP),用于预测的功能和结构 (例如,APPTEST).

主要成果:

深度学习模型在预测多个功能方面表现出很高的准确性,MLBP达到0.708的准确性.
在预测功能方面,CNN模型也表现出强的表现.
APPTEST模型准确地预测了结构,对于5-40个氨基酸的,达到1.96 Å的平均骨干根平均平方偏差 (RMSD).

结论:

深度学习代表了对新型内源性ANGICon-EIP的成本效益和有效发现的关键进步.
对各种神经网络架构的进一步探索将加强对抗高血压的识别.
本综述为未来对来自饮食来源的内源性ANGICon-EIP的研究提供了框架.

关键词:

抗胰岛素-I转化酶抑制类.深度学习是一种深度学习.内源性类是内源性的类.质谱测量质量谱测量类组学类组学类组学在光谱图书馆搜索,搜索光谱图书馆.

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