School 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.
School of Food and Bioengineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
Food Res Int. 2023 Dec;174(Pt 1):113640. doi: 10.1016/j.foodres.2023.113640. Epub 2023 Oct 27.
Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypertensive benefits due to their direct intestinal absorption. Our previous explorations have confirmed that endogenous goat milk short-chain peptides are also an essential source of ANGICon-EIPs. Nonetheless, there are limited explorations on endogenous ANGICon-EIPs owing to the limitations of the extraction and enrichment of endogenous peptides, currently. This review outlined ameliorated pre-treatment strategies, data acquisition methods, and tools for the prediction of peptide structure and function, aiming to provide creative ideas for discovering novel ANGICon-EIPs. Currently, deep learning-based peptide structure and function prediction algorithms have achieved significant advancements. The convolutional neural network (CNN) and peptide sequence-based multi-label deep learning approach for determining the multi-functionalities of bioactive peptides (MLBP) can predict multiple peptide functions with absolute true value and accuracy of 0.699 and 0.708, respectively. Utilizing peptide sequence input, torsion angles, and inter-residue distance to train neural networks, APPTEST predicted the average backbone root mean square deviation (RMSD) value of peptide (5-40 aa) structures as low as 1.96 Å. Overall, with the exploration of more neural network architectures, deep learning could be considered a critical research tool to reduce the cost and improve the efficiency of identifying novel endogenous ANGICon-EIPs.
乳源血管紧张素转换酶抑制肽（ANGICon-EIPs）已被视为高血压患者相对安全的补充饮食疗法策略，由于短肽可直接被肠道吸收，因此可能具有更相关的降压益处。我们之前的探索已经证实，内源性羊奶短肽也是 ANGICON-EIP 的重要来源。然而，由于内源性肽的提取和富集存在局限性，目前对内源性 ANGICON-EIP 的研究还很有限。本综述概述了改良的预处理策略、数据采集方法和用于预测肽结构和功能的工具，旨在为发现新型 ANGICON-EIP 提供创造性思路。目前，基于深度学习的肽结构和功能预测算法已经取得了显著进展。卷积神经网络（CNN）和基于肽序列的生物活性肽多功能性确定的多标签深度学习方法（MLBP）可以分别以绝对真实值和 0.699 和 0.708 的准确率预测多种肽功能。利用肽序列输入、扭转角和残基间距离来训练神经网络，APPTTEST 预测肽（5-40 个氨基酸）结构的平均主链均方根偏差（RMSD）值低至 1.96Å。总的来说，随着更多神经网络架构的探索，深度学习可以被认为是降低成本和提高识别新型内源性 ANGICON-EIP 效率的关键研究工具。
