Prediction of the Health Effects of Food Peptides and Elucidation of the Mode-of-action Using Multi-task Graph Convolutional Neural Network
Prediction of the Health Effects of Food Peptides and Elucidation of the Mode-of-action Using Multi-task Graph Convolutional Neural Network
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DOI：
10.1002/minf.201900134
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发表时间：
2019-11-28
影响因子：
3.6
通讯作者：
Yamanishi, Yoshihiro
中科院分区：
文献类型：
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作者：
Fukunaga, Itsuki;Sawada, Ryusuke;Yamanishi, Yoshihiro
食物蛋白质不仅是营养物质，而且是人体生理功能的调节剂。食物蛋白质的生理功能基本上是由食物蛋白质序列中编码的肽（食物肽）调节的。在这项研究中，我们提出了一种新的基于深度学习的方法来预测食物肽的健康影响并阐明其作用模式。在该算法中，我们使用多任务图卷积神经网络来估计食物肽的潜在靶蛋白，并使用有关疾病治疗靶点的信息来预测其健康影响。我们基于21，103种肽-蛋白质相互作用构建了预测模型，涉及10，950种肽和2，533种蛋白质，并将模型应用于食品肽（例如，例如，在一个实施例中，乳三肽、异亮氨酰酪氨酸和沙丁鱼肽）。这些模型表明了几种食物肽的潜在作用，如降低血压作用、降低血糖水平作用和抗癌作用。通过对接模拟证实了食物肽与靶蛋白的相互作用。
Food proteins work not only as nutrients but also modulators for the physiological functions of the human body. The physiological functions of food proteins are basically regulated by peptides encrypted in food protein sequences (food peptides). In this study, we propose a novel deep learning-based method to predict the health effects of food peptides and elucidate the mode-of-action. In the algorithm, we estimate potential target proteins of food peptides using a multi-task graph convolutional neural network, and predict its health effects using information about therapeutic targets for diseases. We constructed predictive models based on 21,103 peptide-protein interactions involving 10,950 peptides and 2,533 proteins, and applied the models to food peptides (e. g., lactotripeptide, isoleucyltyrosine and sardine peptide) defined in food for specified health use. The models suggested potential effects such as blood-pressure lowering effects, blood glucose level lowering effects, and anti-cancer effects for several food peptides. The interactions of food peptides with target proteins were confirmed by docking simulations.